[ { "uid": "RP-1AC934", "title": "Applying ML Methods to Determine Fatalities' Factors of Covid-19", "task_team": false, "dur_project_id": "DUR-00B4161", "workspace_status": "CLOSED", "lead_investigator": "Faisal Manjahy", "research_statement": "This project focuses on making use of machine learning capacities to help determine \"what factors contribute to the fatalities of Covid-19\". Data are meant to be utilized in training models capable of classifying a specific case, or fitting it into the appropriate cluster, so that such factors contributing to fatalities can be identified.", "accessing_institution": "North Dakota State University" }, { "uid": "RP-D05523", "title": "Case-Control Studies of Medications and Their Possible Associations with COVID-19 Severity", "task_team": false, "dur_project_id": "DUR-014937A", "workspace_status": "ACTIVE", "lead_investigator": "Alejandro Schaffer", "research_statement": "Project Summary: \n\nThe current COVID-19 pandemic, which began in China in last December, has already caused over 1 million deaths world-wide (1). This is an unprecedented threat, to which scientists must find a response, using all the resources which can be made available. The Cancer Data Science Lab, led by Prof. Eytan Ruppin, M.D., Ph.D. in the National Cancer Institute (henceforth the CDSL), specializes in the application of data science tools, and in particular computational biology and systems biology, in order to identify better diagnostics, better treatments, and prevention strategies to cancer and other preventable diseases. I am a Staff Scientist in the CDSL.\nWe have established a collaboration with Dr. Ariel Israel, M.D., Ph.D. in Clalit Health Services (CHS), the largest health insurer in Israel which has over 4.5 million members. Our goal is to harness this unique resource to identify preventable risk factors for COVID-19 and possible treatments. CHS' database has already served to show the strong link between vitamin D deficiency and the rate of SARS-CoV-2 infection and disease severity.\nIn our most recent collaborative work, we have systematically analyzed the CHS database to identify drugs which when acquired in the month before SARS-CoV-2, decrease the risk of severe disease and hospitalization. In this analysis, we have identified a few medications, like ezetimide, rosuvastatin, ubiquinone and fleicanide, which we found to be associated with a significantly decreased risk of hospitalization. Combining these epidemiological findings, with patterns of gene expression following coronavirus infection, we came to identify the very specific pathways and molecular mechanisms which are targeted by the virus, and the mechanisms by which these drugs prevent severe disease.\nReproducing and analyzing data from different populations is of crucial importance to establish the validity of epidemiologic results. In this application, we request access to N3C data in order to replicate and extend our understanding of preventable risk factors for severe COVID-19. \n", "accessing_institution": "National Cancer Institute" }, { "uid": "RP-4081D5", "title": "Perioperative outcomes in surgical patients with and without mental health diagnoses in the COVID-19 pandemic using the N3C database", "task_team": false, "dur_project_id": "DUR-015C7B8", "workspace_status": "ACTIVE", "lead_investigator": "Megan Rolfzen", "research_statement": "In the past few years, a burgeoning amount of evidence suggests that there is an association between the pandemic and a greater mental health burden. However, the association between mental health variables and COVID-19 in-hospital outcomes after surgery is not as clearly defined. We aim to describe the association between mental health diagnoses and perioperative morbidity and mortality in a COVID-19 cohort.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-2AE058", "title": "[N3C Operational] Machine-learning resources for N3C", "task_team": false, "dur_project_id": "DUR-02C5808", "workspace_status": "CLOSED", "lead_investigator": "Peter Robinson", "research_statement": "The N3C machine learning (ML) group has formed to develop best practices, examples, documentation, and modular, reusable code for N3C researchers. For this purpose we are submitting this DUR to have a common workspace where we can test code, integrate different modules for analysis and visualization, and have an environment where multiple group members can assess products before they are released for the entire N3C Data Enclave via the Knowledge Store.", "accessing_institution": "The Jackson Laboratory" }, { "uid": "RP-60E788", "title": "Renal Outcome of COVID-19 infection", "task_team": false, "dur_project_id": "DUR-0548EA2", "workspace_status": "CLOSED", "lead_investigator": "Omar Maarouf", "research_statement": "In COVID-19 acute kidney injury (AKI), various reports describe different causes of AKI including possible viral tropism of the kidney. The role of systemic inflammation & immune dysfunction complicated by endothelial dysfunction remains uncertain. Available evidence suggests that cardiovascular disease risk factors and COPD are associated with worse outcome and are risk factors for the development of AKI in COVID-19 patients. The pathophysiology of COVID-19 AKI is probably multifactorial ? consistent with the pathophysiology of other modalities of AKI. Early diagnosis of kidney involvement in COVID-19 patients and implementation of preventive and/or therapeutic measures are essential to impede AKI severity. This will also lead to delayed progression to chronic kidney disease (CKD)/end-stage kidney disease (ESKD) and a decrease in COVID-19 AKI related mortality. Understanding the pathophysiology, course and outcome of AKI in COVID-19 patients is an unmet need.\nWe want to use the N3C de-identified data base to retrospectively study the following research aims:\n1.\tProportion of different comorbidities in patients with and without AKI\n2.\tDifferent phenotypes of COVID-19 AKI based on clinical presentation at diagnosis, patterns of injury, duration and course of AKI, and progression to CKD, RRT or death.\n3.\tAssociation of systemic inflammatory or disease markers (like ESR, CRP, ferritin, D-dimers, IL-6, Il-10, procalcitonin, WBC count) and the need for mechanical ventilation in the development, course and outcomes of COVID-19 AKI\n4.\tThe association of traditional markers of AKI (proteinuria and hematuria) with COVID-19 AKI and its severity, including RRT initiation.\n5.\tThe effect of the need for anticoagulation on COVID-19 AKI outcome (CKD or its progression, RRT dependence, death). \n6.\tRole of antivirals, steroids & systemic anticoagulants in the development and progression of AKI.\n7.\tSeverity of COVID-19 AKI and risk to initiate RRT, including RRT modality\n8.\tKidney recovery post COVID-19 AKI and post hospital discharge course/COVID-19 AKI kidney outcome \n", "accessing_institution": "Thomas Jefferson University" }, { "uid": "RP-44BAAD", "title": "Obese Patient Outcomes with COVID-19 Associated ECMO Therapy", "task_team": false, "dur_project_id": "DUR-056BB4D", "workspace_status": "ACTIVE", "lead_investigator": "Jeremiah Hayanga", "research_statement": "The prevalence of obesity is rising in the United States. Excess adipose tissue causes difficulty in managing severe illness secondary to changes in anatomic and physiological properties. Changes in respiratory mechanics can make the mechanical ventilation of a person with obesity more challenging. The use of extracorporeal membrane oxygenation (ECMO) has been shown to be safe in people affected by obesity. Unfortunately, obesity is a risk factor for poor outcomes with COVID-19 complicated by ARDS. It is unknown whether outcomes in patients with obesity may have improved outcomes on ECMO therapy. We seek to evaluate the outcomes of patients that are obese versus non-obese with life-threatening COVID-19 illness treated by ECMO therapy.", "accessing_institution": "West Virginia University" }, { "uid": "RP-34729D", "title": "Understanding the Timeline of COVID-19 ", "task_team": false, "dur_project_id": "DUR-06AD2C1", "workspace_status": "CLOSED", "lead_investigator": "Anru Zhang", "research_statement": "This project aims to investigate the timeline of COVID-19.", "accessing_institution": "Duke University" }, { "uid": "RP-2EC36D", "title": "Deep learning models to predict symptomatic breakthrough SARS-CoV-2 infection.", "task_team": false, "dur_project_id": "DUR-088099D", "workspace_status": "CLOSED", "lead_investigator": "William Hillegass", "research_statement": "Our previous work has examined individual and area-level risk factors for breakthrough SARS-CoV-2 infection in the fully vaccinated using parametric statistical models. We plan to evaluate whether a ML/DL model can improve prediction of breakthrough infection risk. This may be useful in guiding the selection and timing of booster doses as well as other prevention efforts in individuals with feature groups associated with increased risk for symptomatic breakthrough infection.", "accessing_institution": "University of Mississippi Medical Center" }, { "uid": "RP-8F71E7", "title": "Evaluating the role of vitamin D deficiency as an independent risk factor for COVID-19 diagnosis and disease severity among racial and ethnic minority groups", "task_team": false, "dur_project_id": "DUR-1A7A093", "workspace_status": "CLOSED", "lead_investigator": "Fatima Jones", "research_statement": "The purpose of this project is to evaluate the role of vitamin D deficiency is an independent risk factor for COVID-19 infection and disease severity among racial and ethnic minority groups. This project will utilize De-identified Level 2 data.", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-B322BC", "title": "Gastrointestinal Symptoms and COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-2C0532C", "workspace_status": "CLOSED", "lead_investigator": "William Hillegass", "research_statement": "A single center analysis suggests a variety of GI symptoms at and during hospitalization for C19 are associated with worse C19 outcomes adjusted for other organ system symptoms and findings. These associations will be evaluated in the larger N3C experience.", "accessing_institution": "University of Mississippi Medical Center" }, { "uid": "RP-C94D0A", "title": "Prediction model of risk factors", "task_team": false, "dur_project_id": "DUR-B3047A2", "workspace_status": "CLOSED", "lead_investigator": "Asma Hussain M Al Bishi", "research_statement": "Predicting risk factors of the Patients Having Confirmed COVID-19 Infection Using the National COVID Cohort Collaborative (N3C) Data Using Data Mining Methods", "accessing_institution": "George Mason University" }, { "uid": "RP-A61709", "title": "Age- and Sex-Based Patterns in Severe COVID-19, Expressed Hospitalization and Length of Stay ", "task_team": false, "dur_project_id": "DUR-095244E", "workspace_status": "CLOSED", "lead_investigator": "Ila Nimgaonkar", "research_statement": "We aim to better understand the underlying factors contributing to increased risk of severe COVID-19, as expressed in hospitalization for the disease and the length of stay. Our previous work drawing from public databases showed that sex- and age-based patterns in COVID-19 mortality closely mirrored those seen in cardiovascular disease in the general population [1]. Here, we would like to examine age- and sex-based patterns in COVID-19 severity. We have requested the Level 2 dataset which will allow us to calculate COVID-19 hospitalization rates and length of stay by age group, as well as to stratify individuals based on sex, comorbidities, and race/ethnicity. We will limit our dataset to those individuals infected before January 2021, since vaccination and the delta variant of SARS-CoV-2 will complicate the analysis. This work will contribute to a better understanding of the factors that predispose individuals to severe COVID-19.\n\nReference: \n1.\tNimgaonkar I, Valeri L, Susser E, Hussain S, Sunderram J, Aviv A. The age pattern of the male-to-female ratio in mortality from COVID-19 mirrors that of cardiovascular disease in the general population. Aging (Albany NY). 2021 Feb 7;13(3):3190-3201. doi: 10.18632/aging.202639. Epub 2021 Feb 7. PMID: 33550276; PMCID: PMC7906174.\n", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-C9A0D3", "title": "Effectiveness of Masks Against COVID-19 ", "task_team": false, "dur_project_id": "DUR-0B64A26", "workspace_status": "CLOSED", "lead_investigator": "Ashlee Davis", "research_statement": "Masks are touted to be effective against COVID-19, but the degree to which they are effective has been nebulous at best. We are interested to explore how the cities/counties with mask mandates correlate with COVID cases and mortality. De-identified data (level 2) related to mask use/mandate and COVID-19 cases and deaths for the US counties and/or cities would be most helpful. We expect to use generalized linear model (GLIM) to explore the relationship between mask mandate/use and disease outcome.", "accessing_institution": "East Carolina University" }, { "uid": "RP-6A50EE", "title": "Disparities in Cardiovascular Disease in COVID-19 (DCDC-19 study)", "task_team": false, "dur_project_id": "DUR-0C67DE5", "workspace_status": "CLOSED", "lead_investigator": "Osama Dasa", "research_statement": "Epidemiologic data on Coronavirus Disease-2019 (COVID-19) suggest that African Americans (AA) and Hispanics contract the virus at higher rates, and are more likely suffer from its consequences. These racial disparities have been attributed to the higher rates of cardiometabolic conditions in AA and Hispanics: hypertension, diabetes, obesity and cardiovascular disease (CVD). There exists a limited understanding of why cardiovascular risk factors predispose individuals to more severe COVID-19 phenotypes, and whether the greater prevalence of these factors explain worse COVID-19 outcomes in AA and Hispanics. \nThe overall goal for this study is to better understand the epidemiologic basis for racial disparities in COVID-19. We therefore hypothesize that much of the disparities in COVID-19 outcomes can be explained by cardiovascular comorbidities and their risk factors. To develop this hypothesis, we propose a multilayered conceptual model based a scoping review of the literature. The model proposes that the disproportionate burden of underlying CVD and its risk factors act as a main driver for worse COVID-19 outcomes, but also contains other alternative explanations.\n", "accessing_institution": "University of Florida" }, { "uid": "RP-D2C001", "title": "The role of social vulnerability and COVID-19 pandemic on children's mental health emergency department visits", "task_team": false, "dur_project_id": "DUR-0CE5173", "workspace_status": "ACTIVE", "lead_investigator": "Nayra Rodriguez-Soto", "research_statement": "Mental, emotional, and behavioral disorders are presented among 1 in 5 children ages 3-17 years in the United States. The COVID-19 pandemic, as a public health emergency, has significantly increased children?s depressive, anxiety, and suicidality or self-injury symptoms and mental health-related hospitalizations. Indeed, some children are more vulnerable to the adversity brought on by the COVID-19 pandemic by increasing their risk for mental, emotional, and behavioral disorders (i.e., anxiety and mood disorders), or by exacerbating a previous mental health condition (i.e., mental health-related hospitalization) due to delays in mental health services and social distancing measures. Despite the fact that children have less severe SARS-CoV-2 than adults, community/social vulnerability has been identified as risk factor for increased mortality among children. Social vulnerability refers to neighborhood factors, such as socioeconomic status, household composition and disability status, housing type and transportation, and minority status/language, that may reduce a community?s ability to adapt, respond and recover after disasters. To our knowledge, researchers have suggested bidirectional associations between COVID-19 severity and mental health diagnosis among adults, while Hispanics have more fears and hospitalizations. Yet, there is still a need to understand the role of social vulnerability (taking into account SARS-CoV-2 diagnosis) on mental health-emergency department (ED) visit and hospitalization among children aged 3-17 years. Hence, our study aims to assess children mental health ED visit and hospitalization during the COVID-19 pandemic. Our central hypothesis is that community/social vulnerability and SARS-CoV-2 diagnosis/severity may play a role in the number of mental health ED visit and hospitalization among children aged 3-17 years old.", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-0526A3", "title": "Multistate models for acute kidney injury in patients with varying severity of COVID-19", "task_team": false, "dur_project_id": "DUR-0E79AA3", "workspace_status": "CLOSED", "lead_investigator": "Andrew Guide", "research_statement": "The goal of this project is to determine the risk of acute kidney injury (AKI) in patients hospitalized with COVID-19. Disease severity, along with other potential confounding variables, will be modelled to determine if the onset of AKI differs significantly for patients with varying levels of disease using de-identified data. To account for the competing risks of death and hospital discharge, multistate modelling will be used to track changes in the patients? state in order to remove bias from the analysis.", "accessing_institution": "Vanderbilt University Medical Center" }, { "uid": "RP-315F84", "title": "Role of School Programs on Adolescent Mental Health During COVID-19 Pandemic", "task_team": false, "dur_project_id": "DUR-10119D7", "workspace_status": "ACTIVE", "lead_investigator": "Jung Ae Lee", "research_statement": "The COVID-19 pandemic has led to widespread school closures as a measure to curb the virus's transmission. While these closures have been effective in mitigating the spread of the virus, they have also raised concerns about their potential impact on mental health, particularly among children and adolescents. \nThis research aims to comprehensively investigate the consequences of school closures on adolescent health outcomes across the US, including COVID-19 infection and mental health outcomes of adolescents during the pandemic. We hypothesize that timely and appropriate school programs can contribute to the prevention of adolescent mental conditions, such as depression, anxiety, and suicidal behavior. This study involves conducting a state-level temporal analysis to understand the relationship between school closure/reopening and mental health outcomes among youths aged 12-17 who have been diagnosed as COVID-positive. Additionally, this will be compared to the negative COVID cohort and the age group 18-25.\n", "accessing_institution": "University of Massachusetts Medical School" }, { "uid": "RP-3BC5A0", "title": "COVID with AKI", "task_team": false, "dur_project_id": "DUR-19B4720", "workspace_status": "CLOSED", "lead_investigator": "Jin Chen", "research_statement": "Temporal data signatures in electronic health records (EHR) have shown strong associations with specific patient populations and disease outcomes. We will develop and validate feasible deep learning approaches to predict short- and long-term kidney function trajectories using multimodal EHR data.", "accessing_institution": "University of Kentucky" }, { "uid": "RP-981323", "title": "Short-term cross-protective effects of respiratory virus infections and immunizations", "task_team": false, "dur_project_id": "DUR-1A549CC", "workspace_status": "ACTIVE", "lead_investigator": "Luca Giurgea", "research_statement": "During the COVID-19 pandemic, the incidence of other respiratory viruses has declined dramatically. This phenomenon has been largely attributed to aggressive public health interventions. However, the impact of non-specific immune activation, triggered by COVID-19 infection or vaccination, on other respiratory virus diseases is unclear. Similarly, the protective effects of immune activation from other respiratory viruses or vaccines on COVID-19 deserves further attention. ", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-856874", "title": "Clinical features and prognostic implications of cholangiopathy in COVID-19 patients: a National COVID Cohort Collaborative (N3C) Study ", "task_team": false, "dur_project_id": "DUR-1BBC240", "workspace_status": "CLOSED", "lead_investigator": "Bing Chen", "research_statement": "In this application, we outlined innovative proposal to investigate the clinical features and prognostic implication of cholangiopathy in patients with history of COVID infection. We will use the NCATS N3C Data Enclave to compare the incidence of cholangiopathy in COVID-19 infection and its clinical characteristics, risk factors, severity and mortality. We will need variables including but not limiting to patient basic characteristics, demographic, medical course, severity of COVID illness, oxygen need, treatment during hospitalization, lab values, imaging studies especially MRCP and outcomes. ", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-A20E7B", "title": "Knowledge-based COVID-19 Induced Pediatric Sepsis Severity Analytics", "task_team": false, "dur_project_id": "DUR-1C48523", "workspace_status": "ACTIVE", "lead_investigator": "Carmelo Velez", "research_statement": "Our goal is to develop, train, and validate knowledge-based screening tool to detect/predict pediatric patients at risk for hospitalization, need for ventilation, and cardiovascular interventions, utilizing de-identified electronic health record data available through NCATS' National COVID Cohort Collaborative (N3C) Data Enclave.", "accessing_institution": "Computer Technology Associates" }, { "uid": "RP-B3EACE", "title": "Subclassification of SARS-COV-2 Clinical Phenotypes by Confounding Factors From EHR Data", "task_team": false, "dur_project_id": "DUR-1C50A94", "workspace_status": "CLOSED", "lead_investigator": "Vincent Carrasco", "research_statement": "Physicians cannot provide patients with individualized diagnoses and personalized treatments that maximize their chances for the best possible outcome. Predictive point-of-care decisions for personalized care are not possible. Electronic health records (EHRs) document physicians? experiences managing diseases, and with proper analysis, EHRs can provide evidence of treatment outcomes. Clinical experience combines skill and knowledge, enabling clinicians to personalize care for individual patients. It is gained by treating real-world patients and is documented within EHRs. COVID-19 is a novel respiratory disease caused by the SARS-CoV-2 virus that overwhelmed health care systems worldwide. Experience treating COVID-19 expanded exponentially during the SARS-CoV-2 viral outbreak of 2019 and was captured by EHRs worldwide. This experience, with proper analysis, forms the basis for what is known as experience-based medicine (ExBM), real-world medicine (RWM), or practice-based medicine (PBM) [Hutchison and Rogers. 2012; Zozuz, Richesson, Hammond, Simon. 2015; Horwitz, Ralph I., and Burton H. Singer. 2017]. Sub-classifying COVID-19 clinical phenotypes from EHRs by co-morbidities and other confounding factors represents a method for gathering statistically significant numbers of real-world clinical phenotypes for measurement and forecasting outcomes. \nSub-classifying EHR data by personal health information (PHI), co-morbidities, outcomes, and other confounding factors using machine intelligence is a way to organize clinical experience. Continuous analysis, modeling, and forecasting update research as data accumulates fine-tuning evidence and conclusions [Ruan, Qiurong, Kun Yang, Wenxia Wang, et al. 2020]. They evolve - treatments initially suggesting good outcomes may over time may prove ineffectual or deleterious [Popp, Maria, Miriam Stegemann, Maria-Inti Metzendorf, et al. 2021; Gautret, Philippe, Jean-Christophe Lagier, et al. 2020; Mazzitelli, Maria, Chiara Davoli, Vincenzo Scaglione, et al. 2020]. \nResearch Questions\n1.\tWhat is the best strategy to sub-classify covid-19 clinical phenotypes by confounding variables?\n2.\tDoes N3C provide sufficient confounding variable data to measure patients? outcomes in clinically relevant configurations using single and multiple variables?\n3.\tDoes the inclusion of unstructured EHR data need to be explored to sub-classify patients with COVID-19 into groups using confounding factors\nThere are no established methods to exploit clinical experience for clinical decision support. This work is the first phase of a multi-phase effort to mine EHRs, for physicians? experience treating real-world patients as clinical evidence to individualize patient care. \n", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-10C598", "title": "Predicting Long COVID", "task_team": false, "dur_project_id": "DUR-1C97023", "workspace_status": "ACTIVE", "lead_investigator": "Theophilus Eboigbe", "research_statement": "This study presents an innovative approach to predicting the onset and severity of long COVID, a condition characterized by persistent symptoms following an initial recovery from acute COVID-19, by utilizing laboratory biomarkers. Recognizing the urgent need to understand and mitigate the impact of long COVID on individuals and healthcare systems, our research focuses on identifying specific biomarkers that can serve as early indicators of an increased risk for developing long-term complications.\n\nUtilizing a retrospective cohort of patients who have recovered from acute COVID-19, we analyze a wide range of laboratory data collected during their initial infection phase, including inflammatory markers, blood cell counts, metabolic profiles, and markers of organ function. Through a combination of statistical analysis and machine learning techniques, we identify a subset of biomarkers strongly correlated with the subsequent development of long COVID symptoms, such as fatigue, cognitive dysfunction, and dyspnea.\n\nOur predictive model integrates these biomarkers into a risk assessment tool, offering clinicians the ability to identify patients at high risk for long COVID early in their recovery process. The model's predictive performance is rigorously evaluated using cross-validation methods and an independent validation cohort, ensuring its reliability and applicability in diverse clinical settings.\n\nThe findings of this study not only contribute to the understanding of the pathophysiological mechanisms underlying long COVID but also pave the way for targeted interventions aimed at preventing or mitigating the condition. By providing healthcare professionals with a powerful tool for early risk stratification, our research has the potential to significantly improve the long-term health outcomes of COVID-19 survivors and optimize the allocation of medical resources. Future directions include exploring the dynamic changes in these biomarkers over time and their relationship with the severity and progression of long COVID symptoms.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-6E1947", "title": "Maternal and Neonatal Outcomes with COVID-19 - related ECMO Therapy", "task_team": false, "dur_project_id": "DUR-1D894D0", "workspace_status": "ACTIVE", "lead_investigator": "Jeremiah Hayanga", "research_statement": "The effect of severe COVID-19 infection in pregnancy is limited to a small number of case reports. The use of extracorporeal membrane oxygenation (ECMO) in pregnancy is rare, typically reserved for salvage therapy in acute respiratory distress syndrome (ARDS). Specifically, veno-venous ECMO (V-V ECMO) may be used for refractory hypoxemia in patients who cannot be safely supported with conventional mechanical ventilation. The use of ECMO for COVID-19 infection in pregnancy has not yet been reported in a large series as individual institutions only have few cases. This limits the information available for analysis. We seek to investigate the maternal and neonatal outcomes of ECMO use in pregnancy-associated COVID-19 infection in a large national cohort.", "accessing_institution": "West Virginia University" }, { "uid": "RP-5992C1", "title": "Evolution of hospital stay parameters for COVID patients during COVID pandemic and a characterization of post COVID care needs and healthcare utilization ", "task_team": false, "dur_project_id": "DUR-1E2C34A", "workspace_status": "CLOSED", "lead_investigator": "Corneliu Antonescu", "research_statement": "This project will focus on characteristics of hospital stays and some outcomes for patients hospitalized with COVID 19 (like mortality, transfer to ICU for mechanical ventilation, length of stay, readmissions, discharge dispositions), resource utilization and how these changed at different stages of the pandemic. ", "accessing_institution": "University of Arizona" }, { "uid": "RP-31AA51", "title": "Assessing Temporal Lab Value Changes and Medications as Predictors of Health Outcomes for COVID19+ Patients (Level 3)", "task_team": false, "dur_project_id": "DUR-2BC71EC", "workspace_status": "CLOSED", "lead_investigator": "Michael Patton", "research_statement": "Despite significant recent advances in defining the molecular pathophysiology of SARS-CoV2, healthcare professionals still lack a set of easily orderable clinical tests that can predict severity of disease and/or high mortality outcomes in COVID19+ patients. In order to identify predictive trends, we will conduct a temporal analysis of key biometric and laboratory value changes with respect to critical outcome timepoints:\n\nNew Outcomes of Interest\n1) Long COVID-19 Symptom Onset (new)\n2) Break-through Infections (new)\n\nPrevious/Ongoing Outcomes of Interest\n1) First COVID19 positive test\n2) First admission to a critical care unit\n3) Respiratory failure & first required use of mechanical ventilation\n4) Thrombotic events\n5) Sepsis onset\n6) Hospital discharge or patient expiration\n\nParallel to the analysis of predictive biomarkers, we will further expand our investigation by stratifying patient cohorts by select pre-existing conditions and use of select medications to determine their effect on high mortality outcomes.", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-1DCE16", "title": "Retrospective analysis of the efficacy of Nirmatrelvir ritonavir for preventing severe COVID-19 in children", "task_team": false, "dur_project_id": "DUR-1E2D53C", "workspace_status": "CLOSED", "lead_investigator": "Lorne Walker", "research_statement": "Nirmatrelvir/ritonavir (NTV/r) use has not been widespread among children, likely due to a lack of awareness among providers and patients, potential drug-drug interactions, low perceived levels of individual risk, limited availability of the drug, and lack of pediatric data. Due to the limited usage of NTV/r, many pediatric patients did not receive the medication, resulting in two distinct groups: one group that received NTV/r and another that did not. We propose to perform a retrospective, data repository-based, propensity-matched analysis of the effect of NTV/r on hospital admission and severe disease.\n\nEligible patients for NTV/r treatment would be between 12 to 18 years of age, weighing >=40kg, having first positive SARS-CoV-2 testing in an outpatient setting and having at least one risk factor for severe COVID-19, as described by the American Academy of Pediatrics (AAP). The risk factors will be identified using ICD-10 codes. Exclusion criteria will be patient with previous history of SARS-CoV-2 infection, patient taking any of the medication described in Pfizer prescribing information as contraindications to NTV/r and patients with ICD-10 codes indicating Child-Pugh Class C hepatic impairment. Among all eligible patients, we will assess whether they received the medication within 5 days of positive SARS-CoV-2 test or not. Employing propensity matching using patient characteristics including age, body weight, BMI, study site, and presence of risk factors as described above by AAP, we will categorize patients into two groups and analyze whether NTV/r reduced the incidence of hospitalization or severe disease.\n\n Furthermore, we will conduct additional exploratory analyses to investigate if NTV/r was preferentially given to children with specific eligibility criteria such as obesity, asthma, and immunocompromising conditions, compared to other conditions and whether NTV/r had differential effects on hospitalization risk in these subgroups. We will also perform exploratory analysis of the prevalence of side effects in NTV/r including dysgeusia, diarrhea, elevated fibrin, d-dimer increase, alanine aminotransferase increase, headache, nausea, creatinine renal clearance decrease, and vomiting compared to the group that did not receive NTV/r within 14 days of taking the medication.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-3A2FE0", "title": "Development of a risk score prediction algorithm for nosocomial and COVID coinfection", "task_team": false, "dur_project_id": "DUR-20EDA38", "workspace_status": "CLOSED", "lead_investigator": "Katelyn Trigg", "research_statement": "Using N3C to determine if coinfection of COVID with nosocomial infection leads to increased hospitalization and worsened clinical severity. Interested in using indicators like: hospitalization, uptake of ambulatory services, LOS, discharge status, & discharge with antibiotics to develop a simple risk score to predict mortality in hospitalized patients.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-6223D2", "title": "Statistical Learning approach to access the causal effect of mechanical ventilator settings on COVID-19 patients' survival ", "task_team": false, "dur_project_id": "DUR-24D7B90", "workspace_status": "ACTIVE", "lead_investigator": "GUANHUA CHEN", "research_statement": "Understanding the causal effect of treatments or exposures is a major goal of clinical research. Many continuous, non-discrete, or otherwise many-valued treatments are difficult or impossible to study with randomized controlled trials (RCTs), leaving observational data as the primary source for understanding their effects on outcomes. Continuous-valued/vector-valued treatments and exposures are ubiquitous in medical research including the usage of mechanical ventilator for COVID-19 patients. For example, the set of settings of a mechanical ventilator under control of the operator: respiratory rate, tidal volume, flow rate, waveform, inspiratory/expiratory ratio can all impact the patients? survival. These controls are often set together according to guidelines, but a refined understanding of the causal effect of all settings when taken together can improve knowledge and use of ventilators. A key barrier to progress is the presence of confounding: factors that impact both the value of the treatment received and the outcome must be controlled for to understand the causal effects of continuous treatments. In the mechanical ventilation example, guidelines for their use are based on the condition of the patient, which includes interpretation of chest x-rays; thus sicker patients receive different vent settings. While discrete treatments have been well-studied, with many approaches for analysis such as propensity score matching, weighting and sensitivity analyses for unmeasured confounding, continuous exposures render causal inference considerably more challenging and the literature has only recently begun to develop. In this project, we will develop a novel statistical/machine learning approach for estimating weights to introduce independence between non-binary exposures (continuous-valued/vector-valued treatments and exposures) and complex high dimensional confounders to eliminate confounding bias.", "accessing_institution": "University of Wisconsin?Madison" }, { "uid": "RP-289D15", "title": "THE LONG-TERM EFFECT OF COVID-19 EXPOSURE AND ACUTE HYPERGLYCEMIA ON CARDIOMETABOLIC OUTCOMES AND MORTALITY", "task_team": false, "dur_project_id": "DUR-2737EAC", "workspace_status": "CLOSED", "lead_investigator": "Maria Santos", "research_statement": "COVID-19 continues to be a leading cause of death in the United States and worldwide, ranking as the fourth leading cause of death in the U.S. The prevalence of COVID-19 one-year post-discharge mortality rates to be as high as 7.9%, revealing the need to investigate the long-term impacts of COVID-19 and its determinants. To date, studies exploring the long-term effects of COVID-19 are limited to short follow-up times (? 1 year), and U.S studies have been mostly confined to nonrepresentative samples including overrepresented by men and veterans. Furthermore, studies exploring the risk factors associated with post-recovery COVID-19 mortality and morbidities such as incident cardiovascular disease outcomes and diabetes mellitus are limited to exploring the impact of chronic conditions. A gap remains in terms of the effect of acute hyperglycemia as a risk factor for post-recovery COVID-19 morbidities and mortality. In addressing the critical need to bridge the existing knowledge gap surrounding the impact of COVID-19 exposure and acute hyperglycemia during hospitalization, our research team presents a comprehensive and high-impact proposal.", "accessing_institution": "Tulane University" }, { "uid": "RP-8A1BD0", "title": "Investigation of the benefit of Ciclosporin A therapy.", "task_team": false, "dur_project_id": "DUR-2753C49", "workspace_status": "CLOSED", "lead_investigator": "Markus Seeliger", "research_statement": "The purpose of this project is to evaluate the benefit of Ciclosporin A therapy (CsA) for pre-existing conditions such as kidney transplant or autoimmunediseases in patients with COVID-19. We will study the association of patient data (including socio-demographics, comorbidities, medications, laboratory values and other investigations) with outcomes (mechanical ventilation, and death) in COVID-19 patients. For all analyses, we will conduct studies to compare outcomes in patients with COVID-19 with those who tested negative for COVID-19 (control group).", "accessing_institution": "Stony Brook University" }, { "uid": "RP-850D26", "title": "Topological modeling of the trajectory of post-acute sequelae of COVID-19", "task_team": false, "dur_project_id": "DUR-28D5E64", "workspace_status": "CLOSED", "lead_investigator": "John Holmes", "research_statement": "This project seeks to use topological modeling enhanced with pseudotime methods to characterize and model the trajectory of syndromes (sequelae) experienced by those who were hospitalized for acute infection with COVID-19. We will examine the hospital course for these individuals and identify, using machine learning methods to reduce dimensionality and select features that are candidate confounders, mediators, and effect modifiers. Using these data we will build exploratory predictive models that could be used to characterize \"pre-morbidities\", or signs and symptoms that may be associated with post-acute syndromes, which may, in turn, have not appeared during the acute phase of the disease. Using topological and other network methods we have developed for modeling chronic disease such as Type 2 Diabetes, we will create analyzable and explorable graphical representations of the trajectories for use in deep digital phenotyping of those who experience Post-Acute Sequelae of COVID-19 (PASC). We will make these models reproducible and replicable and offer them in the public domain for further investigation. ", "accessing_institution": "University of Pennsylvania" }, { "uid": "RP-403923", "title": "Characteristics and Outcomes of Hospitalized Children with COVID-19", "task_team": false, "dur_project_id": "DUR-2BDD3B5", "workspace_status": "CLOSED", "lead_investigator": "Kamakshya Patra", "research_statement": "Background: Children are at risk for severe SARS-Cov-2 infection. Epidemiology of SARS-COVID-19 in children is evolving and poorly characterized. There is paucity of literature regarding outcomes and predictive factors of hospitalized children. Projections indicate that incidence of COVID-19 may persist for several months. Clinical characteristics and outcomes of hospitalized children will be helpful for future resource utilization. \nObjectives:\nTo study the characteristics and outcomes of children presenting with COVID-19 using a large multi-institutional dataset and a validated identification method. To study the factors predictive of adverse outcomes in hospitalized children.\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-7A76FC", "title": "Incidence and Risk Factors for Invasive Fungal Infections in Hospitalized Transplant Recipients with COVID-19", "task_team": false, "dur_project_id": "DUR-2CBA654", "workspace_status": "ACTIVE", "lead_investigator": "Zachary Yetmar", "research_statement": "Since the onset of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, certain high-risk populations have been disproportionately affected by COVID-19. Additionally, patients with severe COVID-19 have been found to have appreciable rates of invasive pulmonary aspergillosis, termed coronavirus disease-associated pulmonary aspergillosis (CAPA). The use of high-dose corticosteroids for COVID-19 therapy have also led to outbreaks of mucormycosis. Prior studies have estimated about 1-2% of at-risk patients will develop an invasive fungal infection after COVID-19. However, past studies have largely been limited by small samples sizes, heterogeneous patient populations, and lack of control populations without fungal infections. Thus, we aim to describe the 90-day incidence of invasive fungal infection among solid organ and hematopoietic stem cell transplant recipients hospitalized with COVID-19, analyze possible associations with invasive fungal infection development, and compare mortality based on development of invasive fungal infection.", "accessing_institution": "Cleveland Clinic" }, { "uid": "RP-1C6E5B", "title": "Impact of COVID-19 infection in patients with pulmonary non-tuberculous Mycobacterium (NTM) infection: A cohort study.", "task_team": false, "dur_project_id": "DUR-2DD4A0C", "workspace_status": "ACTIVE", "lead_investigator": "Carlos Figueroa Castro", "research_statement": "COVID-19 is an infection caused by the SARS-CoV-2 virus, a novel coronavirus causing acute respiratory infection. It is considered a pandemic, affecting at least 31 million people and causing almost a million deaths worldwide. As of September 28, 2020, the virus has infected more than six million in the United States, and caused more than 200,000 deaths. Based on observational studies, multiple risk factors have been associated to poor outcomes, including advanced age, and cardiac and pulmonary conditions. However, the data used for establishing the level of risk in special populations is difficult to extract and analyze when compared to studies analyzing the general population.\n\nOur research team is interested in studying the impact of COVID-19 infection in patients with pulmonary non-tuberculous Mycobacterium (NTM) infections. These infections tend to affect people with pulmonary conditions like chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), bronchiectasis, and certain humoral and cellular immunodeficiency conditions. These infections have a worldwide distribution. In the United States, the most common cause of NTM infections is M.avium intracellulare complex (MAC). The mainstay of therapy is antibiotic therapy, usually a combination of a macrolide (azithromycin, clarithromycin), ethambutol, and rifampin for 18-24 months. Reinfection is common. It is not known whether these patients are at a higher risk for COVID-19 infection, or its severe manifestation. ", "accessing_institution": "Medical College of Wisconsin" }, { "uid": "RP-C79023", "title": "Predictors and Outcomes of COVID-19 in Vaccinated patients", "task_team": false, "dur_project_id": "DUR-2E63342", "workspace_status": "CLOSED", "lead_investigator": "Muhammad Gul", "research_statement": "\nAs more people get vaccinated, spurious cases of COVID-19 emerge in vaccinated patients. We want to evaluate the risk profile of the vaccinated patients who got COVID-19 and admitted. We plan to predict the vaccinated patient population that contract COVID-19 so that they can be prioritized for booster shots. We wish to evaluate the outcomes of such patients, as in general its thought COVID-19 presents in a milder form in vaccinated patients. We also wanted to evaluate the time duration after vaccination when the vaccinated patients are contracting COVID-19. \n", "accessing_institution": "University of Kentucky" }, { "uid": "RP-A8E2CC", "title": "Angiotensin II for the Treatment of Shock", "task_team": false, "dur_project_id": "DUR-303B331", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Chow", "research_statement": "Angiotensin II has been utilized for the treatment of distributive shock, but its effects in COVID-19+ patients are unknown. Preliminary studies from COVID+ patients in Italy and New York have found that Angiotensin II is an effective vasopressor that also leads to improved respiratory parameters such as the ratio of SpO2/FiO2, but these are small case series from <30 patients. Other studies in non COVID+ patients have found that Angiotensin II is associated with improved mortality and improved liberation from renal replacement therapy in those with acute kidney injury, but this data is based off of a post-hoc analysis of non-COVID+ patients. The aim of this project is to examine the hemodynamic, cardiovascular, renal, and safety outcomes in COVID+ patients receiving Angiotensin II, and compare those outcomes against a cohort of COVID negative patients.", "accessing_institution": "George Washington University" }, { "uid": "RP-7E622D", "title": "Health Equity and Access to Covid-19 Treatments Available through Compassionate Drug Use and Emergency Use Authorizations and Expanded Access Programs", "task_team": false, "dur_project_id": "DUR-310B18A", "workspace_status": "CLOSED", "lead_investigator": "Candon Johnson", "research_statement": "Understanding how access to care may vary across the U.S. is a critical component to ensuring health equity for all individuals. During the COVID-19 pandemic, there has been a surge in drug and biological products made available by the US Food and Drug Administration (FDA) through Emergency Use Authorizations (EUAs) and the Expanded Access (EA) program. In this study, we seek to understand how access to these products for COVID-19 has varied across patients based on their race, age, gender, socioeconomic status, health risk factors, and geographic characteristics.", "accessing_institution": "Food and Drug Administration" }, { "uid": "RP-ED4284", "title": "Multisystem Inflammatory Syndrome in Children (MIS-C) and its Long-Term Outcomes in COVID-19 Patients", "task_team": false, "dur_project_id": "DUR-3181D83", "workspace_status": "CLOSED", "lead_investigator": "Akram Mohammed", "research_statement": "Children and adolescents with MIS-C are affected by multiple organs dysfunction (cardiovascular, gastrointestinal, renal, dermatologic, hematologic, neurologic, etc.,), fever, and elevated inflammatory markers. In this project, we aim to develop machine learning algorithms to predict multisystem inflammatory syndrome in children (MIS-C) in COVID-19 patients. We plan to use common clinical labs, vital signs, medication, ventilation, comorbidities, and demographics data to predict the early onset of MIS-C in COVID-19 and long-term outcomes after MIS-C.", "accessing_institution": "University of Tennessee Health Science Center" }, { "uid": "RP-7DE911", "title": "Post-recovery SARS-CoV-2 (COVID-19) infection and incident of de-novo heart failure: in-patient hospital data analysis", "task_team": false, "dur_project_id": "DUR-322CA09", "workspace_status": "ACTIVE", "lead_investigator": "Gal Levy", "research_statement": "The onset of the SARS-CoV-2 (COVID-19) pandemic brought about significant health challenges; among them is the notable relationship between COVID-19 and heart failure. A variety of studies have investigated this development of heart failure among patients recovering from SARS-CoV-2 (COVID-19). However, research explicitly linking post-recovery complications of COVID-19 to the onset of de-novo heart failure in individuals without pre-existing comorbidities is very limited. This study aims to investigate the long-term risk of new-onset heart failure among hospitalized COVID-19 survivors from March 2020 to July 2024. ", "accessing_institution": "Howard University" }, { "uid": "RP-566933", "title": "Coronary Artery Disease High risk PCI impact for COVID-19 positive populations", "task_team": false, "dur_project_id": "DUR-4630863", "workspace_status": "ACTIVE", "lead_investigator": "Dan Housman", "research_statement": "This project will implement a scoring algorithm to classify patients based on qualification for High Risk PCI HR-PCI within the full N3C patient population and then analyze the impact of COVID-19 as a general trend towards increased CAD severity and in COVID-19 characterized sub-populations. The scoring algorithm will be reviewed by interventional cardiologists as experts to refine logic and scoring to establish a consistent method between patient records for determining relative qualifications as a as a candidate for an HR-PCI as an intervention. Research will test whether patients with COVID-19 positive exposure impacted the distribution of risk scores in various ways based on variability identified over time eras and in subpopulations. ", "accessing_institution": "Graticule" }, { "uid": "RP-02B107", "title": "Application of targeted learning models to determine if SSRI exposure protects against Covid-19 severity", "task_team": false, "dur_project_id": "DUR-32EB4A3", "workspace_status": "ACTIVE", "lead_investigator": "Dan Housman", "research_statement": "The intersection between neuroscience and COVID-19 has led to a number of open questions relating to the causal relationship between medication exposure and outcomes (average treatment effect) among patients with COVID-19 exposure. The SSRI medication fluvoxamine has been the subject of research into its protective effects for preventing higher acuity cases based on a randomized controlled clinical trial in Brazil. Fluvoxamine treatment is uncommon in the US making the study difficult to apply directly to US healthcare. SSRI therapy with related medications such as Fluoxetine and Sertraline are commonly prescribed in the US and can be studied within the same class of medications as fluvoxamine.\n\nThis project will use targeting learning methods with the potential to publish regarding both the use of the methods and results. The project will generate analyses to understand the correlation between SSRI usage and prevention of Covid-19 including causal relationships where possible. We will explore retrospective longitudinal data from N3C datasets to test the hypothesis that SSRIs reduce severity of illness and additionally explore the utility of targeted learning to establish the causal relationships. Additional drug exposures may be ", "accessing_institution": "Graticule" }, { "uid": "RP-B74C8C", "title": "Machine Learning to Predict COVID-19 Risk", "task_team": false, "dur_project_id": "DUR-3493396", "workspace_status": "CLOSED", "lead_investigator": "Ryan Melvin", "research_statement": "The COVID-19 pandemic continues to be a major healthcare crisis at the time of this writing. In this work, we apply a deep learning technique for combining multiple machine learning models ? each considering a different set of facts for the prediction of a positive test for COVID-19. The output of the model is the prediction of a positive test for COVID-19. Additionally, using variable importance methods, we report the most predictive risk factors in the final trained model. The final model is a set of multi-layer perceptrons trained in concert with one another whose outputs are combined by a final layer which outputs a class (positive test or negative test). This paper shows that deep learning provides novel insights into the risk factors predictive of a positive COVID-19 test. \n", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-4C5689", "title": "Determining rates of COVID-19 outcomes during the Omicron and BA.2 eras ", "task_team": false, "dur_project_id": "DUR-34A1D91", "workspace_status": "CLOSED", "lead_investigator": "Diego Seira", "research_statement": "While new therapies for early, outpatient COVID-19 are urgently needed, the rapidly changing nature of the virus has made it difficult to design robust clinical trials. Data on the rates of outcomes of COVID-19, including hospitalization, death, and symptom resolution (both with treatment and without) categorized by pertinent risk factors are urgently needed to facilitate design of phase 3 registrational studies of new COVID-19 therapies. This project will evaluate such risk factors in order to optimize clinical trials design for a new COVID-19 outpatient treatment.", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-2E4C1D", "title": "Air Quality and COVID19 via Synthetic Data set", "task_team": false, "dur_project_id": "DUR-35ADC07", "workspace_status": "CLOSED", "lead_investigator": "Cavin Ward-Caviness", "research_statement": "This project examines links between air quality and COVID19 (infection, morbidity, mortality, and long-term complications) using data from the synthetic dataset. It will be used by the Environmental Health data team to develop workflows and pipelines which will then be translated to the Limited Data Set as access to that is granted. ", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-A5E361", "title": "Identifying risk factors for post-acute sequelae of COVID-19 (PASC) symptoms, as well as the effect of various clinical interventions", "task_team": false, "dur_project_id": "DUR-35DC0A8", "workspace_status": "CLOSED", "lead_investigator": "Tal Kozlovski", "research_statement": "The overarching goals of this research are: (1) to identify risk factors for post-acute sequelae of COVID-19 (PASC) symptoms, as well as (2) to quantify the effect of various clinical interventions on the probability to suffer from PASC; (3) to investigate COVID-19 disease progression clusters within hospitalized patients, and (4) to assess variants effect on observed progression paths.\nWe aim to use causal inference tools and advanced AI technologies to learn the relation between Post-Acute Sequelae of COVID-19 (PASC) with the clinical attributes of hospitalized patients with COVID-19, prior clinical history, and SARS Cov-2 circulating variants. Furthermore, the aim is to assess and quantify the effect of PASC caused by different interventions during the hospitalizations of a COVID-19 patient. Such a study can help governments and authorities better allocate resources to protect sub-populations with increased risk for PASC. We expect to find a distinguishable cluster between COVID-19 patients and the sub-population that further suffers from PASC.\n", "accessing_institution": "IBM" }, { "uid": "RP-D54461", "title": "Racial/geographical disparities in HIV care continuum due to COVID-19 pandemic through multilevel risk prediction", "task_team": false, "dur_project_id": "DUR-36B8E6D", "workspace_status": "ACTIVE", "lead_investigator": "Xueying Yang", "research_statement": "It is imperative HIV patients remain engaged with their primary and HIV healthcare providers amidst the COVID-19 pandemic to ensure consistent access to HIV-related care and treatment. However, interactions with health providers, access to HIV treatment, and adherence are undermined by the COVID-19 preventive measures (e.g., shelter-in-place orders) imposed by many states and municipalities. In light of the COVID-19 pandemic, it is likely that economic, geographic inequities will prevent some PLWH from accessing care due to the potential social and structural determinants of health, including the physical, social, community or polity aspects of the environments that impede or facilitate efforts to retain in care. The syndemic health problems PLWH face during COVID-19 pandemic may interact synergistically to produce an increased burden of disease. Whether the COVID-19 pandemic will reinforce the social and structural determinants which contribute to the geographic inequities in terms of the HIV care continuum is unclear. To protect PLWH from COVID-19 and future pandemics, programming to address COVID-19 disease in light of the multiple mutually reinforcing health burdens faced by PLWH is imperative. This study aims to use N3C enclave data to perform a large-scale prospective cohort study to explore the impact of COVID-19 pandemic on the racial/geographical disparities in HIV care continuum among HIV patients and examine the multilevel predictors (e.g., individual- and zip code-level) accounting for such disparities. Our team is requesting access to level 2 de-identified data and level 3 Limited Data set (LDS) for data analysis.", "accessing_institution": "University of South Carolina" }, { "uid": "RP-1BB472", "title": "The study of lipoproteins and their components in SARS-CoV-2 infections", "task_team": false, "dur_project_id": "DUR-44A1395", "workspace_status": "CLOSED", "lead_investigator": "Rajendra Kulkarni", "research_statement": "Triglycerides and cholesterol are the most clinically important lipids in the plasma. Cholesterol is an essential part of cell membranes and is the precursor to many physiologically important molecules, while triglycerides are important as a source for energy production. Both are, however, insoluble in plasma and are transported by large particles called lipoproteins. Chylomicrons and very low-density lipoprotein are the main carriers of triglycerides, while low-density lipoprotein and high-density lipoprotein are the primary carriers of cholesterol. The metabolism of lipoproteins is quite complex, and they play a role in a myriad of human diseases, including cardiovascular disease, and other diseases where inflammation plays a role. Recent studies have demonstrated the involvement of lipoproteins in the pathogenesis of Covid. We would like to examine the association between Covid and lipoproteins and their components.", "accessing_institution": "George Mason University" }, { "uid": "RP-1C471A", "title": "Post-COVID healthcare utilization and health outcomes among vulnerable populations with chronic conditions", "task_team": false, "dur_project_id": "DUR-37467C5", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "The COVID-19 pandemic has illuminated health disparities and vulnerabilities within populations, emphasizing the need for targeted research to understand its impact fully. This study will explore the intersectionality of vulnerabilities, including age, race/ethnicity, and insurance status, along with chronic conditions such as diabetes and HIV, on post-COVID healthcare utilization and outcomes.\nPrevious studies have shown that vulnerabilities such as older age, racial/ethnic minorities, and uninsured or underinsured status are associated with increased healthcare utilization and poorer glycemic control post-COVID. Moreover, individuals with pre-existing chronic conditions, notably diabetes and HIV, exhibit exacerbated outcomes. Using data from the N3C enclave records of patients, variables including age, race/ethnicity, insurance status, presence of chronic conditions (diabetes, HIV) and healthcare utilization metrics. We will investigate the impact of Vulnerabilities (Age, Race/Ethnicity, Insurance Status) and Chronic Conditions (Diabetes, HIV) on Post-COVID Utilization and Outcomes (HA1C). In order to explore the association between vulnerabilities, chronic conditions, and post-COVID outcomes we intend to use a multivariable regression model\nThe findings will help to understand how the impact of vulnerabilities and chronic conditions on post-COVID healthcare utilization and outcomes is critical for guiding targeted interventions and resource allocation to mitigate disparities and improve overall health equity.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-2466D9", "title": "Multiorgan Dysfunction Syndrome and Complex Clinical Trajectory", "task_team": false, "dur_project_id": "DUR-3782229", "workspace_status": "CLOSED", "lead_investigator": "Rishikesan Kamaleswaran", "research_statement": "This project seeks to develop clinical phenotypes of critical illness among patients with COVID-19 that progress to have multiple organ dysfunction syndrome (MODS). We propose to develop machine learning models that predict worsening clinical course and severity of MODS.", "accessing_institution": "Emory University" }, { "uid": "RP-CE303E", "title": "Physician-Resident", "task_team": false, "dur_project_id": "DUR-37BD710", "workspace_status": "CLOSED", "lead_investigator": "Fahad Ahmed", "research_statement": "The use of ML to look at clinical outcomes in the inpatient setting", "accessing_institution": "Wayne State University" }, { "uid": "RP-973D59", "title": "Covid-19 associated neurodegeneration correlation analysis and biomarker discovery ", "task_team": false, "dur_project_id": "DUR-3A9043A", "workspace_status": "CLOSED", "lead_investigator": "Dan Housman", "research_statement": "Long Covid comprises a wide range of new, returning, or ongoing health problems which people experience four or more weeks after first being infected with the virus that causes COVID-19. Neurological issues associated with Long Covid include headaches, change in the senses of smell and taste, fatigue, insomnia, brain fog, memory issues, confusion and lack of concentration. COVID-19 in long COVID has been suggested as a cause of accelerated neurodegeneration resulting in diagnoses or progression in Parkinson?s disease, Alzheimer's disease, Multiple Sclerosis, and other forms of dementia. It is important to identify clinical biomarkers which could facilitate early detection and prompt treatment. This would help in preventing permanent damage to the brain caused by the viral load and enable development of new therapeutics or repurposing of existing ones to limit impact of the infection. \n\nThis project is a study to identify data elements within the N3C datasets that can be used to discover clinical biomarkers indicative of neurodegeneration in Covid-19 patients. This project will explore the retrospective longitudinal data from N3C datasets to identify whether there is a correlation between COVID-19 and neurodegeneration. Furthermore the project will seek to identify potential biomarkers for early neurodegeneration in COVID-19 patients . Furthermore, the study will segment patients to identify known and previously unknown patient characteristics that are risk factors for COVID-19 exposure that result in neurodegenerative decline.\n", "accessing_institution": "Graticule" }, { "uid": "RP-148569", "title": "Pediatric Severity Prediction Challenge", "task_team": false, "dur_project_id": "DUR-3B16608", "workspace_status": "CLOSED", "lead_investigator": "Timothy Bergquist", "research_statement": "Children with COVID-19 are at risk for severe clinical outcomes including hospitalization, acute COVID, and Multisystem Inflammatory Syndrome in Children (MIS-C). Predictive methods are needed to identify children who are at risk for severe COVID symptoms. To that end, we are proposing to conduct a community challenge within the National COVID Cohort Collaborative (N3C) enclave to engage with the machine learning community to develop risk prediction models for identifying children who are at risk for severe COVID symptoms. We will establish a gold standard true positive dataset against which risk prediction models will be benchmarked.\n\nUsing N3C data, challenge organizers will identify viable challenge questions focused on prediction of complications for pediatric COVID patients. Participants in this challenge will build models on a training dataset established by the challenge organizers. Those trained models will then be tested on a holdout set to establish initial model accuracy. Models will then be put through a ?final training?. These trained models will be evaluated against a battery of accuracy and generalizability tests including longitudinal generalizability, cross-site generalizability, hold-out dataset accuracy, and prospective evaluations.", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-E77CC5", "title": "Cardiovascular Complications of COVID-19", "task_team": false, "dur_project_id": "DUR-3B4068E", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "This study is aimed to assess the cardiovascular and pulmonary complications in patients suffering from COVID-19. The results of this study will help health-care providers assess the severity and prevalence of COVID-related complications and risk-stratify the patients accordingly. \n \nThe clinical impact of this work is expected to be a better understanding of mechanisms associated with adverse events from COVID-19. The long-term impact could be determination of approaches to improve outcomes for these patients in the future.", "accessing_institution": "University of Virginia" }, { "uid": "RP-C609E0", "title": "Robust longitudinal causal inference methods with machine learning", "task_team": false, "dur_project_id": "DUR-4035C03", "workspace_status": "CLOSED", "lead_investigator": "Liangyuan Hu", "research_statement": "Real world evidence is essential to answering clinical questions in comparative effectiveness research (CER). In many circumstances, randomized controlled trials (RCTs) are not practical or ethical, and their stringent inclusion/exclusion criteria limit generalizability to vulnerable populations. Drawing causal inference from large-scale data collected from real-world clinical settings is therefore critical to forming important policy related to interventions. There is a substantial body of causal inference methods with a time-fixed treatment. In comparison, causal inference methods, particularly flexible ones using machine learning, for time-varying treatment are relatively sparse. Furthermore, existing approaches in this area no longer meet the growing challenges posed by complex health data structures and treatment patterns. An emerging clinical research question motivates our project. The causal effects of multiple COVID treatment strategies on the long-term health consequences of COVID-19 infection, especially in marginalized populations who are under-represented in clinical trials, are important but yet unknown. Such investigations are urgently needed but present five main methodological issues that prohibit direct applications of existing parametric longitudinal causal inference approaches: 1) treatment is time-varying in relation to study entry, 2) outcome of interest is beyond population means, 3) there can exist more than one time-varying treatments with multiple switches, 4) a potential source of bias from model misspecification, and 5) a potential source of bias from longitudinal unmeasured confounding. We will develop a new robust marginal structural quantile model to draw simultaneous causal inference about longitudinal treatments and further improve the flexibility of the model by using machine learning (Aim 1). For censored survival outcomes, we will first develop a new joint marginal structural model in continuous-time for the restricted mean survival times. We will then develop a Bayesian likelihood-based machine learning method that can accommodate time-varying covariates to estimate a set of weights for correcting time-varying confounding or selection bias due to informative censoring (Aim 2). To tackle the ?no unmeasured longitudinal confounding? assumption, we will further develop a flexible and interpretable sensitivity analysis framework. Machine learning will be used to estimate the causal effects adjusted for the posited amount of unmeasured confounding over time. Finally we will apply the methods developed in Aims 1-3 to address the motivating CER question using the N3C data. We are requesting the limited data set, as both dates of services and zip codes are important for building models while accounting for treatment effect heterogeneity and treatment protocols at different times. The proposed project is associated with the Machine Learning Domain Team.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-B0384E", "title": "Location- and time-robust long COVID risk prediction", "task_team": false, "dur_project_id": "DUR-412EB16", "workspace_status": "CLOSED", "lead_investigator": "ELIOR RAHMANI", "research_statement": "Risk prediction from electronic health records (EHR) is a notoriously challenging task, and it has been repeatedly reported that EHR-based models are susceptible to poor generalization on out-of-distribution data that represent locations, populations, medical practices, or other factors that were not represented in the training data. This project will aim to learn highly interpretable models for long COVID risk prediction, while enforcing calibration, temporal consistency, and robustness across locations and over time.", "accessing_institution": "University of California, Los Angeles" }, { "uid": "RP-B608C9", "title": "Use of Causal Effects Analysis to Explore the Use of Therapeutics and Interventions for the Treatment of COVID-19", "task_team": false, "dur_project_id": "DUR-41D4A22", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Chow", "research_statement": "In the midst of a worldwide surge of COVID-19, it may be neither practical nor feasible to perform placebo-controlled RCTs. Causal effects analysis is able to reduce confounding of observational data, and similar to an RCT, may able to estimate the average causal effect of an intervention.", "accessing_institution": "George Washington University" }, { "uid": "RP-E8DC53", "title": "The outcome of gastrointestinal bleeding in pateints of COVID-19", "task_team": false, "dur_project_id": "DUR-51D013B", "workspace_status": "CLOSED", "lead_investigator": "Bing Chen", "research_statement": "Due to the critical illness, SARS-CoV2 direct invasions, and the use of steroids and anticoagulation, patients with COVID-19 are predisposed to gastrointestinal bleeding (GI) bleeding. In this study, we aimed to investigate the mortality in COVID-19 patients with GI bleeding and the impact of Endoscopy (EGD) on mortality.\n", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-E52664", "title": "Understanding the risk, disparity and outcomes of COVID-19 and dementia", "task_team": false, "dur_project_id": "DUR-4925217", "workspace_status": "CLOSED", "lead_investigator": "Haitao Chu", "research_statement": "Severe illness of COVID-19 predominantly occurs in older people and in individuals with underlying medical comorbidities. Dementia including Alzheimer disease (AD) is a common cause of morbidity and mortality in the aging population. In addition, the majority of people with dementia were living with one or two additional chronic health conditions. Common comorbidities including cardiovascular diseases, diabetes, obesity, and hypertension, and epidemiologic factors such as age, sex, and race are documented as strong risk factors for cognitive decline and dementia. Many of these factors in patients with dementia are also demonstrated strong risk factors for COVID-19. However, there is little if any quantitative analysis of the risks and outcomes for COVID-19 in individuals with AD or dementia in the United States. For example, it is largely unknown on how race and other demographic factors such as age and sex affect the risk of COVID-19 in patients with dementia. Using advanced statistical and causal inference methods, our specific goals are to i) evaluate the impact of sex, race, and age on the association of dementia with COVID-19 in the United States, and ii) identify and predict which subgroups of dementia are most likely to develop severe COVID-19 illness.", "accessing_institution": "University of Minnesota" }, { "uid": "RP-BBC071", "title": "COVID-19 and Air pollution", "task_team": false, "dur_project_id": "DUR-498A685", "workspace_status": "CLOSED", "lead_investigator": "Bora Jin", "research_statement": "Aim to understand the effects of exposure to air pollution on vulnerability to COVID-19. \n", "accessing_institution": "Duke University" }, { "uid": "RP-97279A", "title": "Understanding COVID-19 Burden and Severe Outcomes with National NCATS Data", "task_team": false, "dur_project_id": "DUR-4C0BA2D", "workspace_status": "ACTIVE", "lead_investigator": "Julie Swann", "research_statement": "Our team will research predictions around true disease burden, and likelihood of severe outcomes and measures of equity across the system. First task ? predict who will be hospitalized, second task using statistical and machine learning methods to predict who needs an ICU. The disease burden will be explored as a function of preexisting or coexisting conditions (including pregnancy) and patient demographics including: create variables from raw data into compiled data set to capture elements such as recent visit for respiratory illness, existence of primary care provider, age in categories, sociodemographics, location eg. Urban/rural, and others.", "accessing_institution": "North Carolina State University" }, { "uid": "RP-92E57A", "title": "N3C Clinical Tabular Data in the Wild: Data Science Modeling Improvements", "task_team": false, "dur_project_id": "DUR-5220D28", "workspace_status": "ACTIVE", "lead_investigator": "Jelena Tesic", "research_statement": "Tabular data in the wild are difficult to model because of the varying distribution of attributes, missing, overlapping and noisy values, and a mix of categorical and numerical datasets. We have already shown that our intentional data science pipeline can automatically uncover important attributes, reduce feature space, and model prediction in the robust manner from multi-source tabular data in https://github.com/DataLab12/educationDataScience. In this project, we compare and contrast the multi-step state-of-art statistical learning pipeline with multiple state of art decision tree modeling algorithms, including boosting and bagging. We compare the methods using clinical features selected from demographics, location, conditions and observations information using cross entropy. Statistical learning pipeline uncovers interesting feature correlations between long COVID and demographics, where decision tree and gradient boosting modeling show that the use of clinical data needs to be expanded on all attributes available as our current modeling strategies result in a high number of predicted false positives. ", "accessing_institution": "Texas State University" }, { "uid": "RP-9674DE", "title": "Associations between asthma and COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-524D3CE", "workspace_status": "CLOSED", "lead_investigator": "Wangfei Wang", "research_statement": "Although there is evidence suggesting that chronic comorbidities drive COVID-19 mortality, little is known on whether people with asthma have adverse COVID-19 outcomes. Current literature that investigated COVID-19 outcomes in those who have asthma usually included a small cohort, and therefore N3C access will enable a large population-based analysis. In order to investigate the association between asthma and COVID-19 outcomes, we will employ a number of exploratory and statistical analysis among subjects with and without asthma. Demographic factors, pre-existing comorbidities and common asthma risk factors will be taken consideration in the statistical models. Our analysis will provide important insights into clinical management in patients who have chronic conditions such as asthma amid coronavirus pandemic. ", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-266B35", "title": "Impact of Diabetes and Diabetic Complications on COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-52B7C4A", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "The COVID-19 pandemic has underscored the significant role of underlying health conditions in determining the severity and progression of infections. Among these, diabetes has emerged as a key risk factor for poor COVID-19 outcomes, including higher rates of hospitalization, severe disease, and mortality. Diabetes impairs immune function, disrupts metabolic processes, and increases inflammation, all of which may contribute to worsened outcomes in viral infections. This project aims to investigate the specific impact of diabetes and its associated complications?such as cardiovascular disease, kidney dysfunction, and neuropathy?on the clinical outcomes of COVID-19.\nUsing data from the N3C enclave, this study will conduct an analysis of how diabetes, particularly when complicated by comorbidities, influences the severity of COVID-19. of COVID-19. Demographic information, including race, ethnicity, age, and both Type 1 and Type 2 diabetes patients, considering variables such as glycemic control, the presence of complications. The analysis will employ statistical techniques, including multivariable regression models, to identify the associations between Diabetic Complications on COVID-19 Outcomes.\nThe proposed research is critical for understanding the interaction between diabetes and COVID-19, particularly in the context of the growing number of individuals with chronic health conditions. Findings from this project will help inform targeted clinical management strategies for diabetic patients during pandemics and may contribute to the development of public health policies aimed at reducing the burden of COVID-19 among high-risk populations. Ultimately, the study will provide valuable insights into improving outcomes for diabetic patients, offering a more tailored approach to managing their care in future health crises.\n\n", "accessing_institution": "login.gov" }, { "uid": "RP-12FCB1", "title": "Characterization of Racial Health Disparities in COVID-19 Outcomes and Treatment Utilization and Effectiveness", "task_team": false, "dur_project_id": "DUR-5341126", "workspace_status": "CLOSED", "lead_investigator": "Saria Awadalla", "research_statement": "The COVID-19 pandemic has underscored existing disparities in US healthcare access and utilization. Racial and ethnic minorities have disproportionately higher incidence of COVID-19 cases, severe symptoms, and mortality; yet, they have significantly lower vaccination rates and lower utilization of recently available treatments. The aims of this study are to identify underlying drivers of excess risk of severe COVID-19 outcomes in racial/ethnic minorities; investigate disparities in the utilization and effectiveness of COVID-19 treatments; and quantify the impact of time-to-treatment on effectiveness. A broad objective of this study is to identify social and structural determinants of disparate clinical outcomes. In this study, we will leverage nationally representative, harmonized electronic health data of COVID+ patients and matched controls from the N3C Enclave database to investigate our aims. The impact of our analyses will be supportive data to inform targeted interventions in urban settings to mitigate the effects of social determinants of health disparities.", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-3D6C57", "title": "Assessing racial inequality in COVID-19 testing with Bayesian threshold tests ", "task_team": false, "dur_project_id": "DUR-5436544", "workspace_status": "CLOSED", "lead_investigator": "Gal Wachtel", "research_statement": "Extensive literature shows that COVID-19 disproportionately impacts marginalized communities. In response, efforts have been made to improve reporting of race disaggregated COVID-19 data, and significant resources have been invested in studying the impact of sociodeterminants of health on COVID-19 prevalence and patient outcomes. However, such efforts are hindered by disparities in COVID-19 testing, specifically differences in testing amongst racial groups. Preliminary work has used an outcome test known as a threshold test (previously developed for assessing racial disparities in police strops) to evaluate disparities in COVID-19 testing in the state of Indiana (Pierson, 2020). This project aims to extend that work by to characterize racial differences in COVID-19 testing in the U.S. at a national-level. Understanding the factors that correlate with differences in thresholds for testing of minority populations is crucial in order to inform pandemic responses both for the current and future health crises. Additionally, it is to be hoped that the characterization of testing bias will improve current estimates of disease parameters, such as the infection fatality rate, both broadly and amongst marginalized groups. ", "accessing_institution": "University of Oxford" }, { "uid": "RP-93C78A", "title": "Covid and Cancer", "task_team": false, "dur_project_id": "DUR-54B8027", "workspace_status": "CLOSED", "lead_investigator": "Aniket Alurwar", "research_statement": "Our aim at center for precision medicine and data sciences(CPMDS) is to study all the possible outcomes when a patient is diagnosed with Cancer and also gets covid. We have done preliminary work in our UC Davis health OMOP database and discovered some outcomes based on social deteminants of Health. Our center has specialization in AI/ML and we need a dataset from across the country so we can get a diverse enough dataset to study how vaccinations have helped patients with covid and cancer. We also wanted to study with the help of ML how different comorbities affect our cohort", "accessing_institution": "University of California, Davis" }, { "uid": "RP-EB4521", "title": "Adaptive and interpretable machine learning to predict COVID-19 trajectory and severity ", "task_team": false, "dur_project_id": "DUR-56C9EA0", "workspace_status": "CLOSED", "lead_investigator": "Matthew Robinson", "research_statement": "The clinical trajectory of patients diagnosed with COVID-19 is highly variable. Traditional measures of association reported by observational studies are difficult for clinicians to apply to understand an individual patient?s risk of developing severe disease. Conversely, the underlying logic driving machine learning-based predictions of COVID-19 severity are difficult to interpret which limits the ability of treating clinicians to trust their insight. The goal of this project is to design and implement a series of prediction tools driven by machine learning that use adaptive inputs and highly intuitive summaries to deliver accurate predictions that front-line clinicians can use to understand an individual patient?s risk of progression from COVID-19 diagnosis to hospitalization, severe disease, and death. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-7E1733", "title": "COVID-19 pandemic associated adverse drug event disproportionality differences", "task_team": false, "dur_project_id": "DUR-56E9619", "workspace_status": "CLOSED", "lead_investigator": "Richard Boyce", "research_statement": "The purpose of this PV analysis is to compare the ten most frequently reported ADEs for each of the twenty most commonly used medications in COVID-19 patients during pre-pandemic, post-pandemic, and post-pandemic when confirmed to be used for direct treatment of COVID-19. The secondary aim of this study is to investigate if there is a significant difference in the top ten reported ADEs and frequency of ADEs between genders and between age groups (18-64 and >65) during post-pandemic for each drug with confirmed use for direct COVID-19 treatment. \n\nA robust list of medications used in COVID-19 patients that are currently being studied was compiled through use of NCBI-PubMed Central, Clinicaltrials.gov., and the FDA Adverse Event Reporting System (FAERS) database. Concept sets were created on ATLAS for each medication. Using these concept sets, the twenty most commonly used medications will be identified through the National COVID Cohort Collaboration (N3C). Once the top twenty medications are identified, comparison data of reported ADEs pre-pandemic, post-pandemic, and post-pandemic for direct COVID-19 treatment will be collected through a query of the FAERS database from September 11th, 2019 - March 1st, 2021. ADEs for comparison will also be obtained from Micromedex. Disproportionality analysis using reporting odds ratios will be used to compare frequencies of ADEs between time periods, genders, and age groups. Overall, our findings may aid as a future reference for selecting clinically safe medications for use in COVID-19 patients by revealing patterns of ADEs between different groups, as well as providing updated data on common ADEs seen in medications used for COVID-19 treatment or symptom alleviation.", "accessing_institution": "University of Pittsburgh" }, { "uid": "RP-147652", "title": "COVID-19 Prognostic Factors and Outcomes on Adult Inpatients ", "task_team": false, "dur_project_id": "DUR-573C4C6", "workspace_status": "CLOSED", "lead_investigator": "David Natanov", "research_statement": "Although the COVID-19 pandemic is the largest emergent health crisis of the 21st century, the reasons for variation in clinical severity remain poorly understood. Accurately predicting COVID-19 prognosis early can lead to better patient outcomes and more appropriate resource utilization. We have developed several models for predicting COVID-19 prognosis from a retrospective cohort of 969 hospitalized COVID-19 patients at Robert Wood Johnson University Hospital (RWJUH) during the first pandemic wave in the United States. One of two main models uses age and five widely-available laboratory values to predict COVID-19 mortality. This model was better able to predict mortality (AUC ROC=0.793, F1=0.564) than a commonly used clinical prediction rule for pneumonia severity (CURB-65; AUC ROC=0.722, F1= 0.547). The other major initiative is investigating how a patient?s C-reactive protein levels can be used to predict COVID-19 prognosis in conjunction with demographic data. With increasing age, or with a CRP value > 10 within the first five hospital days, patients had increased odds of severe COVID (OR: 1.02 per 1-year increase in age, p <0.001, OR: 3.78, p < 0.001, respectively). Using data derived from NC3, we now aim to validate our models on patients outside of our home institution and publish an open source version of our finalized models, so that front-line clinicians can utilize these algorithms in their own patient population.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-61E96C", "title": "Workflow Construction with Synthetic Data -Version 1", "task_team": false, "dur_project_id": "DUR-5A35831", "workspace_status": "CLOSED", "lead_investigator": "Will Beasley", "research_statement": "This will help us establish the OU workflow for obtaining enclave data, restructuring the format, and modeling outcomes. Only synthetic data are involved.", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-18D788", "title": "Impact of COVID-19 on patients with Cardiovascular Disease", "task_team": false, "dur_project_id": "DUR-7344076", "workspace_status": "ACTIVE", "lead_investigator": "Jacob Neumann", "research_statement": "Patients who have acquired coronavirus disease 2019 (COVID-19) have displayed numerous cardiovascular complications during the course of their infection, where these complications have included acute myocardial injury, ischemic heart disease, heart failure, stroke, and arrhythmias. While these manifestations are responsible for several complications during the course of the disease, numerous patients have reported continued cardiovascular abnormalities following their recovery. Our study will use the data present in the N3C Enclave to investigate the pharmacological management of patients prior to and post COVID19 exposure to determine whether there is an association between COVID-19 and an increased incidence or progression of different cardiovascular diseases that required increased or additional pharmacological management. ", "accessing_institution": "West Virginia School of Osteopathic Medicine" }, { "uid": "RP-B6C60A", "title": "The Cost of Being Black: The influence of race on medical resource allocation in COVID-19", "task_team": false, "dur_project_id": "DUR-5B0B8DC", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "To date, COVID-19 has claimed the lives of 449,020 Americans and has infected tens of millions more. In doing so, this massively sweeping pandemic has uncovered systemic flaws leading to the unequal share of burden to be held by racial minorities. In fact, Black, Hispanic, and Indigenous Americans have 1.5x higher infection rates, 4x higher hospitalization, and 2.7x higher death rates than White Americans. Data shows that this disparity exists across all age groups with the potential for furthering devastation in minority communities. Currently, 41% of all new COVID-19 infections are assigned to persons 35-49yrs old. Unfortunately, for 35-44yo Black and Hispanic Americans, they are 8-11x more likely to die following COVID infections compared to their White counterparts. Despite these startling statistics, racial minorities are receiving COVID vaccines at dramatically lower rates, with majority of states reporting vaccination patterns along lines of race showing a 2-4x higher vaccination rate for White vs Black Americans. The cause of these disparities in outcomes vs intervention is multifactorial due to the compounding issues of lack of access, health care bias, and presence of negative factors impacting social determinants of health largely assigned to Black and Hispanic communities. Knowing that, we aim to investigate whether or not there are racial disparities in medical resource allocation.", "accessing_institution": "University of Virginia" }, { "uid": "RP-6D19DE", "title": "Contribution of Race and other Social Determinants of Health to Disparities in Patient Outcomes in COVID-19", "task_team": false, "dur_project_id": "DUR-61D8CD3", "workspace_status": "CLOSED", "lead_investigator": "Xuan Han", "research_statement": "As the COVID-19 pandemic has progressed and increasing data has become available on patient outcomes, a striking pattern has emerged: Black and Hispanic Americans exhibit disproportionately high rates of positive diagnoses, hospitalizations, and deaths. These disparities, which have been demonstrated across the country, have generated significant concern. Numerous hypotheses have been proposed to explain them, addressing a wide range of topics from individual physiology to unequal access to care, but they remain incompletely understood. We aim to use the N3C Limited Data Set to better characterize these disparities and to investigate how various patient- and community-level factors may be contributing to them. \n", "accessing_institution": "University of Chicago" }, { "uid": "RP-77DC62", "title": "Initial Data Exploration COVID genomics project", "task_team": false, "dur_project_id": "DUR-621C730", "workspace_status": "CLOSED", "lead_investigator": "Noelle Foster", "research_statement": "ThIs is an initial exploration of available data on patients with a positive COVID test to establish population size and demographics distribution over time preparatory to a larger project.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-7CD5D1", "title": "Examining non-spatial examples of Simpson's Paradox", "task_team": false, "dur_project_id": "DUR-6452C66", "workspace_status": "CLOSED", "lead_investigator": "Will Beasley", "research_statement": "The OU Data lab is interested in developing methods to discover statistical anomalies across COVID-19 data. Statistical anomalies can lead to premature or incorrect conclusions. Analysts who are not experts need tools and methodologies to help them avoid harmful or misleading results. We will develop the methods and evaluate the results across various sections of data.\n \nGoals of the project:\na) Implement Simpson's Paradox discovery pipeline for data sets.\nb) Identify Simpson's paradox across attributes of the data.\nc) Define data quality measures for available data sets.\nd) Implement visualizations to clearly show the strength of Simpson's paradox across data sets.", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-21ED56", "title": "COVID-19 Survivors and Long Term Cognitive Issues", "task_team": false, "dur_project_id": "DUR-6472C6B", "workspace_status": "CLOSED", "lead_investigator": "Ryan Hajj", "research_statement": "In recent months, a global re-emergence of Severe Acute Respiratory Syndrome Coronavirus 2 , SARS-CoV-2, and its new highly infectious variants are being observed in countries abroad. This variant could make its way back into the U.S. and pose a health risk to all, especially for those who are not vaccinated. Even asymptomatic COVID-19 patients are experiencing various cognitive issues at an alarming rate. Some common long term cognitive symptoms include brain fog, dizziness, loss of smell and taste, and memory loss. The combination of high transmissibility of the alpha variant of SARS-COV-2 virus and the uncertainty of long term cognitive symptoms of COVID-19 could result in a major public health crisis in near future. An analysis of community acquired respiratory viruses, such as MERS and SARS is done to determine the cognitive effects on patients with past outbreaks, allowing for a better understanding of cognitive symptoms of current SARS-COV-2. In addition, close examination of the CNS involvement with cognitive symptoms is conducted to understand how and why the virus affects a portion of the brain. This gives an advantage in allowing an early detection of patients symptoms and early treatment options to speed the cognitive recovery process. An examination of Electronic Medical Records from a public database to compare the relative likelihood of developing cognitive consequences between people who have tested positive for COVID-19 and those who tested negative. Also, gender and race of patients with cognitive symptoms are examined to determine if racial and gender disparity exist. \n", "accessing_institution": "University of California, San Diego" }, { "uid": "RP-9A927B", "title": "Changes in psychiatric diagnosis associated with SARS-CoV-2 infection and predicting the development of new psychiatric illness in COVID patients", "task_team": false, "dur_project_id": "DUR-6476972", "workspace_status": "ACTIVE", "lead_investigator": "Michael Russell", "research_statement": "Abstract - SARS-CoV-2 infection, primarily a disorder of the vascular endothelium, results in intravascular thrombosis and a generalized inflammatory response. While the most common and lethal manifestation of SARS-CoV-2 is acute hypoxemic respiratory failure, the occurrence of intracranial thrombosis resulting in stroke in young patients not otherwise at high risk has been reported. Additionally, the generalized inflammatory response seen is likely present in the brain as well. The consequences of neuroinflammation on neurotransmitter pathways and cognitive function are complex and may have implications for psychiatric disease onset or progression. Finally, the Long-Covid syndrome is associated with various cognitive disturbances such as impaired memory and concentration. These observations suggest a direct or indirect impact on neurophysiology that could manifest in differences in prevalence or progression of various psychiatric conditions following SARS-CoV-2 infection. This study will seek to explore prevalence of psychiatric diagnoses in patients testing positive for SARS-CoV-2 compared to a matched cohort of Covid negative patients using proportional hazards modeling. From the resulting relative risk and odds ratios, machine learning techniques will be used to develop a predictive model of future psychiatric sequalae of SARS-CoV-2 infection.", "accessing_institution": "West Virginia University" }, { "uid": "RP-74BCD7", "title": "Impact of Dexamethasone treatment on long COVID", "task_team": false, "dur_project_id": "DUR-68E9787", "workspace_status": "CLOSED", "lead_investigator": "Rohit Arora", "research_statement": "Given the pressing need for effective therapeutics for Long COVID, this study proposes a retrospective cohort analysis to investigate the potential of glucocorticoid therapy, specifically dexamethasone, in mitigating these symptoms. Our preliminary findings indicate a possible reduction in long COVID risk following acute dexamethasone treatment, suggesting a promising avenue for therapeutic intervention. By using N3C deidentified data, this study aims to provide much-needed evidence on the utility of glucocorticoids in treating long COVID.", "accessing_institution": "Harvard University" }, { "uid": "RP-BE2EBE", "title": "Validating a risk prediction model for 30-day adverse COVID-19 outcomes in N3C cohort.", "task_team": false, "dur_project_id": "DUR-6AD0839", "workspace_status": "CLOSED", "lead_investigator": "David Bui", "research_statement": "COVID-19 risk prediction models can continue to inform patient care and policy decisions as effective vaccines and antiviral treatments have become available. Such models may also help identify high-risk patients for facilitating future clinical trials of new pharmacotherapies. However, currently available COVID-19 risk prediction models have important limitations. First, most models were developed early in the pandemic and do not account for current viral strains, population immunity levels, and treatments, resulting in contemporary risk estimates that are less accurate and uncalibrated. Using data from the Veterans Health Administration and machine learning methods, we developed prediction models that accurately estimate the risk of COVID-19 hospitalization and mortality following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments. We plan to externally validate these models using the N3C de-identified cohort. Furthermore, we plan to calibrate our risk prediction models to the broader population represented by the N3C cohort.", "accessing_institution": "United States Department of Veterans Affairs" }, { "uid": "RP-5B127B", "title": "Tripledemic Management: Risk prediction for patients with respiratory diseases and COVID-19", "task_team": false, "dur_project_id": "DUR-6B0ED75", "workspace_status": "CLOSED", "lead_investigator": "JOERG Heintz", "research_statement": "Abstract: The COVID-19 pandemic and rising cases in respiratory diseases may cause a ?tripledemic? in this winter. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 and respiratory diseases (such as RSV) to inform distribution of limited healthcare resources. To address this challenge, we aim to propose a machine learning model to conduct the risk prediction for these patients. We aim to extract the most predictive features based on medical knowledge and incorporates the inter-feature relationships in the form of medical knowledge graphs via graph neural networks.", "accessing_institution": "University of Illinois at Urbana-Champaign" }, { "uid": "RP-6ABC20", "title": "Developing Dynamic Graphs for Prediction and Classification of COVID-19 Patient Trajectories", "task_team": false, "dur_project_id": "DUR-6B36C21", "workspace_status": "CLOSED", "lead_investigator": "James Howard", "research_statement": "We intend to use graphical models to summarize COVID-19 patient data, provide projections based on that data, and to enable classification and prediction of COVID-19 patient outcomes. Our project will use PyTorch Geometric and dynamic graphical models to construct a set of classifiers for COVID-19 disease trajectories. Successful development and deployment of this technology would enable better treatment and risk evaluation for COVID-19 patients.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-917CE0", "title": "The Interaction of Public Health Emergencies: Understanding Nation-wide and City-wide Spatiotemporal Dynamics of COVID-19 Transmission in a Warming World", "task_team": false, "dur_project_id": "DUR-6B40364", "workspace_status": "ACTIVE", "lead_investigator": "Arnab Ghosh", "research_statement": "The focus of this R03 is to build on this work to examine the interaction between two devastating public health emergencies: the COVID-19 (C-19) pandemic and climate change-amplified extreme heat events (EHEs). Although coronaviruses in general survive longer in environments of lower humidity, temperature, and sunlight, C-19 propagation has surged in summer months. A proposed explanation is that SARS-COV2 remains stable in hotter, humid environments, and that C-19 transmission is promoted by heat-avoidant behavior that increases indoor physical proximity and air conditioner use. EHEs adversely affected one in three Americans. Thus, EHEs may intensify C-19 propagation, particularly among more individuals and subpopulations vulnerable to both C-19-related and EHE-related morbidity and mortality ? older aged individuals with medical comorbidities, socioeconomically disadvantaged individuals, and minorities. An examination of this interaction will provide the first evidence of the association between climate-amplified EHEs and C-19, providing important data for future pandemic preparedness and climate-amplified infectious disease propagation ? a critical area of inquiry as de-scribed by several institutions, including the Federal Government. This proposal?s central objective is to examine the relationship between EHEs and C-19 propagation, providing data that can subsequently be translated into future tools for pandemic preparedness in the age of climate change. My central hypothesis is that EHEs in-crease C-19 risk by increasing housebound populations and promoting SARS-COV2 transmission dynamics, particularly in areas with higher proportions of older aged individuals, racial/ethnic minorities, and other socioeconomically disadvantaged individuals. I will test this hypothesis by employing patient-level data from the NCATS National COVID Cohort Collaborative (N3C),25 combined with area-level socioeconomic data from the US census, and environmental data from the National Weather Service (NWS) from 2020-2022. The aim is to identify adult individual-level demographic, clinical, and area-level socioeconomic characteristics associated with in-creased risk of C-19 related hospitalization after EHEs.", "accessing_institution": "Weill Cornell Medicine" }, { "uid": "RP-A0EDDC", "title": "Lidocaine Use in Patients With and Without COVID 19 with Orthopedic Conditions Involving the Spine ", "task_team": false, "dur_project_id": "DUR-6B53274", "workspace_status": "CLOSED", "lead_investigator": "Comron Saifi", "research_statement": "The present study aims to evaluate the prevalence of IV lidocaine use in a cohort of in patients with orthopedic spine conditions with and without COVID. ", "accessing_institution": "Houston Methodist Research Institute" }, { "uid": "RP-A5AC65", "title": "Mortality and Complications Among Patients Hospitalized during the Covid Pandemic.", "task_team": false, "dur_project_id": "DUR-6B92667", "workspace_status": "ACTIVE", "lead_investigator": "Karthik Raghunathan", "research_statement": "COVID-19 pandemic has exposed the existing healthcare disparities in the U.S and added on to the current burden in efforts to mitigate the health disparities. Several previous studies have shown how racial and ethnic minorities, economically and geographically deprived patients took the increased brunt of the pandemic that led to increased infection and mortality rate among them. However, there still remains a need to understand the impact of COVID-19 pandemic on 30-day/90-day mortality, complications, readmissions and length of hospital stay among socioeconomically disadvantaged patients who underwent procedures during the pandemic. Furthermore, we propose to investigate the association of malnutrition, social determinants of health and poor surgical and medical outcomes during COVID-19 pandemic. Our goal is to identify the factors associated with poor surgical and medical outcomes among the patients during COVID 19 so as to inform the prioritization of future interventions to improve surgical outcomes.", "accessing_institution": "Duke University" }, { "uid": "RP-E7676B", "title": "[N3C Operational] Phenotype and Data Acquisition Team Operations", "task_team": false, "dur_project_id": "DUR-6BD82DA", "workspace_status": "CLOSED", "lead_investigator": "Emily Pfaff", "research_statement": "N3C operations to work with contributing sites to submit their data in ACT, PCORnet, OMOP, or TriNetX format. This team is also heavily involved in running various data quality checks on submitted data, communicating findings to sites, and helping sites troubleshoot any issues.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-5984BE", "title": "Incidence of Mannose Binding Lectin Deficiency in COVID-19 ", "task_team": false, "dur_project_id": "DUR-8785B9D", "workspace_status": "CLOSED", "lead_investigator": "Breanne Hayes", "research_statement": "Abstract:\n\nBackground: The SARS-CoV-2 is the virus that is responsible for causing COVID-19 was declared a Public Health Emergency in January 2020, and a pandemic in March of 2020. 1 While rare, reinfection with the virus has been reported on multiple occasions .2 Mannose binding lectin (MBL) is a component of innate immunity. Individuals with mannose binding lectin deficiency have been shown to be susceptible to severe respiratory infections especially in those with additional risk factors.3 Effects of SARS-COV-1 in patients with MBL deficiency has been studied and known to be a contributor to severe cases. The effect of SARS-COV-2 in mannose binding lectin deficiency is unknown.\n\nAim: to discuss and analyze the incidence of COVID-19 recurrence in patients with MBL deficiency \n\nHypothesis: mannose binding lectin deficient patients will be more susceptible to more severe cases of covid-19 \n\nSetting: outpatient allergy immunology \n\nDesign: Retrospective Case Series \n\nConclusion: MBL deficient individuals are more prone to get upper respiratory and lung infections. The effects of COVID-19 on this cohort has yet to be studied, we hypothesize that they are at greater risk than the general public of a more severe course of COVID-19.\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-1958E4", "title": "Role of COVID-19 and Long Covid in orthodontic clinical outcomes", "task_team": false, "dur_project_id": "DUR-6C9E20C", "workspace_status": "ACTIVE", "lead_investigator": "Chen Liang", "research_statement": "Clinical cases series suggested that SARS-CoV-2 may cause long term conditions including systemic inflammation and significant bone loss. In cases of complicated covid-19, higher level of inflammatory cytokines IL1 and IL6 in patients with severe root resorption were observed. Severe root resorption, which is defined by losing more than one third of the root length is one of the most iatrogenic consequences of the orthodontic treatment. In orthodontics, root resorption is induced by inflammatory response. Studies have shown that the root resorption usually stops once the applied force is removed. However, in patients who suffer from long covid symptoms, a continuous systemic inflammation may still affect patient?s oral health and potential of continuous root resorption may be found. Despite a generally high risk of developing multisystem inflammatory syndrome in individuals during or after SARS-CoV-2, the interactions between SARS-CoV-2 and severe orthodontic root resorption remain unclear. Risk factors for severe orthodontic root resorption in long covid patients are undetermined because individuals with the same or similar severity level of COVID-19 show different clinical characteristics and the multifactorial etiology of root resorption. The interconnected risk factors create unique challenges to delineate the risk factors for long covid patient with severe orthodontic resorption. To address these challenges, this study will leverage Electronic Health Records (EHR) data mining methods to examine cohorts of individuals who have COVID-19 and long covid symptoms, respectively, and develop severe orthodontic root resorption.", "accessing_institution": "University of South Carolina" }, { "uid": "RP-AEA93C", "title": "Association between background oral anticoagulation therapy and in-hospital mortality in patients with COVID-19 disease.", "task_team": false, "dur_project_id": "DUR-6CDBCC3", "workspace_status": "CLOSED", "lead_investigator": "William Hillegass", "research_statement": "The project will investigate if background therapy with oral anticoagulants for history of AF, VTE/PE, mechanical heart valve, or CVA is associated with outcome among patients hospitalized for COVID-19 disease.", "accessing_institution": "University of Mississippi Medical Center" }, { "uid": "RP-A95DFB", "title": "The impact of COVID-19 positivity during pregnancy on Latinx/Hispanic mother?s birth outcomes", "task_team": false, "dur_project_id": "DUR-6ED2D3E", "workspace_status": "ACTIVE", "lead_investigator": "Polaris Gonzalez-Barrios", "research_statement": "SARS-COVID-19 infection is known to disproportionately affect underrepresented communities, like Latinx/Hispanics, resulting in worse health outcomes. For Latinx/Hispanic women, it is not known if pregnancy status can represent a greater risk of developing severe symptoms when infected by respiratory illnesses due to immunological changes of pregnancy or due to other preexisting medical conditions and/or social determinants of health (SDOH) such as socio-economic status, age, etc. This may put minority patients in the perinatal period at greater risk of worse pregnancy outcomes. Given the unknown association, we hope to compare pregnancy outcomes for Latinx/Hispanic women infected with COVID-19 during pregnancy and evaluate if poorer birth outcomes where due to status infection or SDOH, or both. We hypothesize that Latinx/Hispanic mothers who had COVID-19 infection during pregnancy and share specific SDOHs, will present poorer birth outcomes. This epidemiologic study will allow us to identify how SDOH relate to the effects of COVID-19 infection on birth outcomes in this population. Identifying these vulnerabilities in these mothers may help guide efforts to reduce health disparities and better prepare healthcare professionals in assisting pregnant mothers of minority populations in future pandemics.\n\nGiven out hypothesis and project aims, we are requesting to advance to Level 3 data access. The main goal is to be able to create linkage with residency-based SDOH measures and that we need with non-shifted dates to look at associations across the variant time period, particularly related to the release of vaccinations in the US. Furthermore, the treatment throughout the pandemic evolved dramatically. As a result, we require access to non-shifted dates using the Limited Data Set. These interventions include fewer intubations, the use of remdesivir as well as dexamethasone. All of these interventions will be analyzed in response to how they presented in pregnant persons and how they all relate to maternal birth outcomes. Latinx/Hispanics have higher incidence of morbidity in pregnancy, and understanding if this is related to health disparities, social determinants of health, COVID-19 infection status/treatment; or a combination; is of priority to advance future medical care in pandemics. This can be one of the few studies to the date indexing all these associations in Latinx/Hispanic birthing persons. \n ", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-5E4410", "title": "Drug Repurposing for Improving COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-6EE48D1", "workspace_status": "CLOSED", "lead_investigator": "Ivana Maric", "research_statement": "Currently, no therapeutics or pharmacological treatments exist for coronavirus disease 2019 (COVID-19). Repurposing existing drugs that have already been established as safe, if shown effective, would lead to more options for treatment of COVID-19. In this project, we plan to investigate several drugs and classes of drugs, that we hypothesize could potentially be effective in reducing the severity of COVID-19 outcomes, based on their characteristics and biological pathways they affect. In our previous retrospective study that focused on statins, we observed a small, but statistically significant, decrease in mortality among patients prescribed statins when compared with matched COVID-19-positive controls. In this project, we plan to use the N3C cohort in a retrospective matched case-control study to study other drugs/classes of drugs of interest. ", "accessing_institution": "Stanford University" }, { "uid": "RP-26E1ED", "title": "Critical Care Level 3 COVID-19 Data Consortium", "task_team": false, "dur_project_id": "DUR-707B999", "workspace_status": "CLOSED", "lead_investigator": "Rishikesan Kamaleswaran", "research_statement": "There is a growing and urgent demand for COVID-19 research due to the rapid development of the pandemic. We are more limited by our ability to analyze data than the data itself. The analysis of acute ICU outcomes will help inform management, appropriate physiological trajectories of illness, discovery of endotypes and phenotypes of disease severity and/or resolution. The National COVID Cohort Collaborative (N3C) has collected electronic health record (EHR) data from over 60 academic health centers across the country (now over 6 million patients and data on over 2.2M patients with COVID). This proposed research will be performed in concert with the ICU outcomes task team, by accessing the Level 3 data, we hope to better identify novel methods to segment ICU episodes from each patient encounter that is both accurate and computable across various disease profiles that are proposed to be studied by this domain teams.", "accessing_institution": "Emory University" }, { "uid": "RP-1C470A", "title": "Impact of immunosuppressive regimens on COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-70ED6E1", "workspace_status": "CLOSED", "lead_investigator": "Mladen Rasic", "research_statement": "This project seeks to describe the relationship between immunosuppressive therapies with COVID-19 outcomes and sequelae in immunocompromised patients. The long term sequelae of patients with immunosuppression with decreased inflammatory response of patients exposed to COVID-19 is not yet clear. N3C enclave access will allow for enhanced clinical understanding for these patients who carry significantly increased COVID-19 risk.In order to elucidate this link, we will apply a number of exploratory data science and machine learning methods in order to predict immunosuppressive regiments associated with improved outcomes. This in part will be achieved by accounting for frequency, duration, and dosage of immunosuppressive drugs, as well as accounting for pre-existing comorbidities, and social determinants of health.These outcomes will provide groundwork for future insights in clinical management in immunocompromised patients.\n", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-90CED1", "title": "Predicting severe COVID-19 outcomes among younger US adults", "task_team": false, "dur_project_id": "DUR-8925EA4", "workspace_status": "CLOSED", "lead_investigator": "Christine Gray", "research_statement": "We aim to identify predictors (e.g., sociodemographics, comorbidities, etc.) of severe COVID-19 (e.g., hospitalization, ICU admission, mechanical ventilation, prolonged hospitalization, and death) among younger US adults (<50 years) in the N3C level 3 data. To achieve this aim, we will develop and validate predictive models of severe COVID-19. We will build and select predictive models using a ?training? dataset, which consists of a random subset of the data. After fitting and selecting all predictive models, we will test the performance of each model in the ?validation? dataset, which consists of the remaining data", "accessing_institution": "Duke University" }, { "uid": "RP-AE0021", "title": "Predictive Models for Long Covid-19 using Network and Patient Characteristics", "task_team": false, "dur_project_id": "DUR-7759603", "workspace_status": "CLOSED", "lead_investigator": "Kushagra Kushagra", "research_statement": "Many previous studies(e.g., Matricardi et. al.(2020), Jehi, et al. (2020)) have focused on predictive models related to short COVID-19 at the human or disease level using patient characteristics, patient?s history, etc. These studies don?t focus on the chronic repercussions of the virus, that is termed as ?long COVID-19.?Other models that make community-based predictions are also based on patient medical data and location descriptions. However, these models disregard the embedded connectedness(e.g., network characteristics)of various comorbidities to understand the long COVID-19 effects, which leads to underperforming models for health outcomes.\n\nThe proposed study will investigate the long-term effects of COVID-19 such as impact on heart and nervous systems, chronic fatigue, etc. while considering their network characteristics of the disease, individual patient characteristics, and health conditions of patients at the aggregate level. The proposed research aims to develop a comprehensive descriptive and predictive model for long COVID-19 by considering patient characteristics, demographics, medical history and their connectedness properties using network analytic approaches. Network features such as density, page rank, eigenvector centrality, betweenness centrality, etc., for each (sub) network that they belong to could provide features for the predictive model. Research questions that we will explore in this study are: \n\nQuestion1: Does patient belonging to a network (subgraph) has high risk propensity to develop particular types of long-COVID-19 symptoms than a patient belonging to a heterogeneous network(subgraph)?\n\nQuestion 2: Predictive model for long COVID-19that includes patient as well as their connectedness to comorbidities and conditions?", "accessing_institution": "Auburn University" }, { "uid": "RP-4B299E", "title": "Using Regression Methods to Study Health Outcomes of COVID-19", "task_team": false, "dur_project_id": "DUR-7A992B5", "workspace_status": "CLOSED", "lead_investigator": "Michelle Ripari", "research_statement": "COVID-19 has changed our health care systems and infrastructure dramatically. One of the most difficult decisions a health care worker can make is who to prioritize care to. By using regression methods to study health outcomes of COVID-19, we can predict who is most at risk and which course of treatment is most effective. In this research project, data will be used to identify the greatest risk-factors for severe COVID in a hospital setting and long COVID. Additionally, a comprehensive survival analysis will be done based on drug therapy, including a proportional hazards model. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-22B50A", "title": "Epidemiology of Acute and Chronic Kidney Injury Associated SARS-CoV-2 Infection", "task_team": false, "dur_project_id": "DUR-7B06C84", "workspace_status": "CLOSED", "lead_investigator": "Chirag Parikh", "research_statement": "Clinical acute kidney injury (AKI) is common in COVID-19 and is strongly associated with severity of disease and outcomes. Preliminary reports indicate that AKI occurs in approximately 20%-40% of patients hospitalized with COVID-19 and in 30-50% of those admitted to the intensive care unit. Moreover, the mortality rate in patients that experience AKI in the setting of COVID-19 is 2- to 10-fold higher than patients without AKI. The severity of AKI in COVID-19 has been overwhelming the resources needed for acute renal replacement therapy. \nThis study propose to assess the incidence, severity, and clinical predictors of major adverse kidney events during hospitalization and at 90 days following discharge. AKI and AKI Stages will be defined using KDIGO creatinine criteria. Baseline serum creatinine will be defined as the last available creatinine measurement between 7 and 365 days before admission. All in-hospital serum creatinine measurements will be used to determine AKI. MAKE-D (Make Adverse Kidney Event) within 90 days (+/- 30 days) will be defined as the need for dialysis or a ?50% increase in serum creatinine OR post-discharge death.\n", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-4DE1EC", "title": "Examining the influence of local policies on the relationship between type 2 diabetes and COVID-19 mortality", "task_team": false, "dur_project_id": "DUR-7B1123A", "workspace_status": "ACTIVE", "lead_investigator": "James Groh", "research_statement": "COVID-19 mortality is more than two times greater among those with diabetes compared to those without. However, there is a gap in understanding the impact local of community-level policies on reducing COVID-19 mortality among those with type 2 diabetes (T2D). While inclusionary zoning (IZ) and Complete Streets policies are recognized approaches to improve community health by preventing and managing chronic disease, it is unclear whether their benefits extend to COVID-19 mortality. This research project addresses this gap by examining the influence of local policies on the relationship between T2D and COVID-19 mortality. Using a limited data set data from the National COVID Cohort Collaborative, this project will examine the 1) individual impact of IZ policies and 2) Complete Streets policies on the relationship between T2D and COVID-19 mortality as well as 3) their combined impact. ", "accessing_institution": "University of Wisconsin?Milwaukee" }, { "uid": "RP-DC7498", "title": "Liver Function as a Predictor of COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-7D4C6A2", "workspace_status": "ACTIVE", "lead_investigator": "Bijun Kannadath", "research_statement": "Coronavirus Disease 19 (COVID-19) is primarily a disease affecting the respiratory system. The major cause of morbidity and mortality resultant in infected patients is from the development of Acute Respiratory Distress Syndrome (ARDS). However, The affinity of the virus for ACE2 receptors and the presence of ACE2 receptors in other body systems such as the liver, heart, skeletal muscle and nervous system has resulted in various non-respiratory manifestations of COVID-19. These non-respiratory manifestations include anosmia, myocarditis, prolonged myalgia, fatigue, memory loss. The liver is also vulnerable similarly. Virus particles can multiply within the liver to cause direct cytopathic effects. COVID-19 associated liver injury is now well documented in patients without pre-existing liver disease with elevated Liver Function Tests (LFTs) being reported frequently. Additionally, the cytokine storm associated with severe disease, systemic hypoxia and Drug Induced Liver Injury (DILI) may also contribute to deterioration in liver function. Thus we hypothesis that the temporal analysis of the changes in LFTs over the course of a patient?s illness and treatment will reveal early markers of clinical deterioration and/or mortality. \n\nThis study aims to study the patterns of progression of liver injury in COVID-19 patients and to discover associations with any patient co-morbidities and to determine if LFTs can serve as a predictor of COVID-19 disease severity and progression.", "accessing_institution": "University of Arizona" }, { "uid": "RP-8EA0A9", "title": "Healthcare Resource Utilization Patterns in COVID-19 Patients with Ostomies: Predictive Modeling of Hospital Admission Rates, Length of Stay, and Resource Allocation Using N3C Data", "task_team": false, "dur_project_id": "DUR-8AD90DD", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "This study aims to develop predictive models using the National COVID Cohort Collaborative (N3C) database to analyze resource utilization patterns in COVID-19 patients with ostomies. By utilizing N3C data, including patient demographics and clinical diagnoses, machine learning techniques will identify significant predictors for hospital admission rates and length of stay (LOS). Factors such as age, comorbidities, and ostomy type will be explored for their influence on resource allocation. The study's outcomes will inform targeted interventions and resource planning for improved healthcare delivery during the pandemic and future health crises.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-884840", "title": "Evaluating Protective Interventions Against Long COVID through Targeted Machine Learning", "task_team": false, "dur_project_id": "DUR-80D09B6", "workspace_status": "ACTIVE", "lead_investigator": "Zachary Butzin-Dozier", "research_statement": "Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a condition that includes a broad range of symptoms across a range of biological systems. Investigators have hypothesized a range of mechanistic pathways that may contribute to Long COVID. We aim to evaluate interventions that may prevent Long COVID through a Targeted Machine Learning approach. A Targeted Machine Learning approach allows investigators to leverage developments in data science, biostatistics, and causal inference in order to determine intervention effectiveness in preventing Long COVID. These methods can flexibly estimate parameters of interest while making minimal parametric assumptions. We have selected interventions that the literature support as potentially reducing the risk of PASC as well as interventions that may provide evidence regarding the underlying causal mechanisms of PASC. These interventions include COVID-19 vaccination, metformin, immune-modulating drugs, and selective serotonin reuptake inhibitors. We will evaluate PASC as both a binary outcome and as a range of individual post-COVID symptoms. These analyses will primarily rely on a combination of Super Learner and Targeted Maximum Likelihood Estimation. As the temporality of these exposures is important to these analyses, we will use Limited (Level 3) data, as described in our IRB protocol.", "accessing_institution": "University of California, Berkeley" }, { "uid": "RP-922E82", "title": "Chronic Pain in Long COVID", "task_team": false, "dur_project_id": "DUR-816C147", "workspace_status": "ACTIVE", "lead_investigator": "Joel Gagnier", "research_statement": "We aim to determine the existence of chronic pain those with a history of an acute COVID-19 infection and predictors of these conditions. ", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-7E0442", "title": "Management and Outcomes of Acute Appendicitis in the United States during the COVID-19 pandemic: A retrospective cohort study", "task_team": false, "dur_project_id": "DUR-81B1D12", "workspace_status": "ACTIVE", "lead_investigator": "Karthik Raghunathan", "research_statement": "The COVID-19 pandemic has had a major impact on surgical management of various conditions, with some procedures being delayed or cancelled. The worry about laparoscopy being an aerosol-generating procedure led to the resurgence of open appendectomy. There has also been a move to treat appendicitis with antibiotics during the pandemic and a recent study from the UK showed that during the early part of the pandemic, 54% of patients presenting with acute appendicitis were initially treated conservatively and only 10% of those required a subsequent appendicectomy.\nLittle is known about how Acute Appendicitis was managed in the US during the COVID-19 pandemic. Hence the aim of this study, using data from the NC3 database was to describe the management of acute appendicitis and outcomes of therapy in the US during the Covid pandemic and to determine whether management and outcomes changed over time.\n", "accessing_institution": "Duke University" }, { "uid": "RP-2B0054", "title": "Vascular complications and clinical severity in patients with COVID and AF", "task_team": false, "dur_project_id": "DUR-820DABC", "workspace_status": "CLOSED", "lead_investigator": "Meleeka Akbarpour", "research_statement": "Recent epidemiological data have demonstrated that hospitalized COVID-19 patients are at an increased risk of developing atrial fibrillation (AF) acutely during the infective stages of COVID-19. Furthermore, the infectious stages of COVID-19 associated with AF adversely affects hospitalized patient outcomes. A study done by Sanz et al. showed that COVID-19 patients with new-onset AF were significantly older, hypertensive, and had acute coronary syndrome and renal dysfunction. This group also had longer hospital stays (p<0.001) and a higher incidence of thromboembolic events, such as stroke, myocardial infarction, pulmonary embolism, and disseminated intravascular coagulation. However, little research has been done on these vascular outcomes among severely ill COVID-19 patients with new-onset of AF. Our research question is if there is an increased risk for the development of adverse complications in severe COVID patients with atrial fibrillation compared to sinus rhythm. Therefore, first we will look at COVID-19 outcomes as defined by the CDC criteria, as well as vascular outcomes among all COVID-19 patients with and without AF. Then, we will analyze vascular outcomes in those with and without atrial fibrillation among those with severe COVID-19 at baseline. \nAF and COVID-19 have shown to elicit a strong inflammatory and immune response, thereby increasing chances of developing microvascular and vascular dysfunction. Therefore, we plan to analyze de-identified data related to biomarkers such as D-dimer, troponin, C-reactive protein, and IL-6. Furthermore, we will look at anticoagulation therapy in mitigating the development of vascular dysfunction among this patient group. AF complicates the trajectory of severely ill COVID-19 patients and warrants further investigation in order to guide interventions in preventing vascular complications.", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-A8A2C3", "title": "Smell, taste and COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-86B0910", "workspace_status": "ACTIVE", "lead_investigator": "Ashish Bhargava", "research_statement": "Loss of smell and/ or altered sense of taste have been described as an early cardinal symptom of COVID-19, occurring even before the onset of respiratory symptoms. Several studies show that gustatory and olfactory dysfunction occur in a large proportion of patients with COVID-19 disease. Some studies indicate that these symptoms are associated with a lower risk of hospitalization when compared to patients without these symptoms. SARS-CoV-2 tropism for olfactory mucosa, can serve as a portal of entry to the central nervous system via olfactory sensory neurons and its projections to the olfactory cortex and other parts of limbic cortical areas. SARS-CoV-2 associated acute inflammation in these areas can be associated with neurological findings in the acute phase of COVID-19 while its persistence and associated inflammation in the limbic cortex can account for the symptoms of long COVID-19. The goal of this study is to compare the epidemiology, clinical features, outcomes and complications (neurological as well as non-neurological) among hospitalized patients with acute COVID-19 who developed changes in smell and/ or taste sensations compared to those who did not develop those changes. Also, we will assess the association of the development of the neurological complications and various post COVID conditions among those who developed changes in smell and/ or taste sensations compared to those who did not develop those changes.", "accessing_institution": "Ascension St John Hospital" }, { "uid": "RP-02B157", "title": "Investigation of COVID-19 outcomes in patients with urological cancers", "task_team": false, "dur_project_id": "DUR-8738284", "workspace_status": "CLOSED", "lead_investigator": "Richard Moffitt", "research_statement": "The aim of this project is to identify the potential association between urological cancers (e.g. prostate cancer and bladder cancer) and COVID-19 outcomes. In particular, we will investigate if therapies associated with these cancers affect outcomes from SARS-CoV-2 infections. ", "accessing_institution": "Stony Brook University" }, { "uid": "RP-E43550", "title": "Validation of the COVIDNoLab and COVIDSimpleLab risk scores", "task_team": false, "dur_project_id": "DUR-873B23A", "workspace_status": "CLOSED", "lead_investigator": "Mark Ebell", "research_statement": "We previously developed and internally validated two simple risk scores to predict mortality in patients hospitalized with COVID-19 (Ebell, et al. Development and validation of the COVID-NoLab and COVID-SimpleLab risk scores for prognosis in 6 US Health Systems. J Am Board Fam Med 2021; 34: S127-35). In the current study we hope to validate these risk scores in the N3C cohort, assessing classification accuracy (predictive value and likelihood ratio), discrimination (area under the ROC curve), and calibration in the large (observed: expected calibration plot). We also plan to validate a two-step risk score developed by myself and my colleague Ye Shen (manuscript in review). ", "accessing_institution": "University of Georgia" }, { "uid": "RP-C01915", "title": "Rural Health Domain Team General Workspace", "task_team": false, "dur_project_id": "DUR-8AEE6DD", "workspace_status": "CLOSED", "lead_investigator": "Will Beasley", "research_statement": "The Rural Health Domain Team seeks to understand the epidemiology, utilization, treatment, and outcomes of the COVID-19 pandemic in rural communities. The overarching goal is to develop better evidence for potential differences in the COVID-19 epidemic response and outcomes for rural health care centers and rural dwellers. Studying the at-risk populations and unique challenges faced by rural communities during and after the pandemic may provide opportunities for improving rural health and health care.\n\nAdapted from https://covid.cd2h.org/rural-health", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-64A965", "title": "An evaluation of Direct Acting Anticoagulants and Dexamethasone in Patients with COVID-19 Infections", "task_team": false, "dur_project_id": "DUR-8B2C4B6", "workspace_status": "ACTIVE", "lead_investigator": "Richard Boyce", "research_statement": "The purpose of this study is to examine if the incidence of thromboembolic events is higher in COVID-19 patients exposed to dexamethasone and apixaban or rivoroxaban as compared to those taking either of the anticoagulants alone. Secondary aims are to examine the dose of apixaban and rivoroxaban among patients with COVID-19, with and without exposure to dexamethasone and other medications that may affect hepatic metabolism of direct acting anticoagulants (DOAC).", "accessing_institution": "University of Pittsburgh" }, { "uid": "RP-878D57", "title": "Trajectories of Long COVID", "task_team": false, "dur_project_id": "DUR-8DAC8A7", "workspace_status": "CLOSED", "lead_investigator": "Mei-Sing Ong", "research_statement": "Post-acute sequelae of SARS-CoV2 infection (PASC), also known as Long COVID, is a multisystem dysfunction affecting the cerebrovascular, autonomic, peripheral, respiratory, and inflammatory systems. The clinical trajectory of patients who developed PASC is highly variable, and delay in the diagnosis of PASC has been widely reported. The goal of this study is to evaluate the diagnostic and symptom trajectories of patients who developed PASC, and to identify those at risk of experiencing persistent symptoms.", "accessing_institution": "Harvard University" }, { "uid": "RP-C8C231", "title": "Unsatisfactory Outcomes after Spine Surgery are Associated with New-Onset Depression and Substance Abuse Disorder in the First Year After Surgery", "task_team": false, "dur_project_id": "DUR-90D6334", "workspace_status": "CLOSED", "lead_investigator": "henry hoang", "research_statement": "\nUnexpected outcomes following spine surgery, such as the persistence of pain or lack of anticipated relief, can lead to significant psychosocial distress for patients. Surgical interventions are often considered a final option after exhaustive non-surgical treatments, and when surgery falls short of mitigating pain, it can have profoundly adverse effects on patients and their families. Existing research has highlighted pre-operative psychological distress, opioid utilization, and substance abuse disorders as factors contributing to the failure of achieving satisfactory clinical outcomes post-spine surgery. Nevertheless, the current understanding of how an unfavorable surgical outcome might trigger new-onset psychological or substance abuse disorders in patients without a prior history of such diagnoses remains limited.\n\nIn particular, the ongoing COVID-19 pandemic introduces a unique context within which these dynamics are unfolding. The pandemic has disrupted healthcare systems, limited access to in-person care, and heightened stress levels due to health concerns, social isolation, and economic uncertainties. These pandemic-related factors could potentially interact with the challenges posed by unsatisfactory surgical outcomes, further exacerbating the risk of developing new psychological or substance abuse disorders in patients. Understanding how the convergence of these factors impacts patient outcomes is a crucial aspect of this study.\n\nThe overarching objective of this research is to comprehensively investigate the degree to which the failure to achieve desired outcomes after spine surgery might increase the susceptibility of patients to being diagnosed with new-onset depression or substance abuse disorders. By analyzing data from the National Covid Cohort Collaborative (N3C), which captures a wide range of clinical observations during the COVID-19 era, the study seeks to unveil the intricate relationship between surgical outcomes and mental health/substance abuse outcomes. This investigation will contribute to a more nuanced understanding of the psychological and physiological ramifications of unexpected surgical outcomes, especially within the context of the ongoing pandemic.", "accessing_institution": "Albert Einstein College of Medicine" }, { "uid": "RP-E8FD97", "title": "Association Between COVID-19 infection and fracture morbidity and mortality in the geriatric population in the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-9231249", "workspace_status": "ACTIVE", "lead_investigator": "Xiangxue Xiao", "research_statement": "Fragility fractures are an age-related disease among the most commonest injuries sustained by patients over 50 years of age, with an overall incidence of 1.1% in the United States. Patients are usually elderly, with limited physiological reserves and multiple comorbidities. With the emergence of the COVID-19 pandemic in early 2020, COVID-19 has become the third leading cause of death in individuals ages 65 and older. Elderly individuals are particularly vulnerable during pandemics. Fewer visits from relatives and caregivers result in a significant increase in the number of elderly living alone, which can increase the risk of falls. Moreover, elderly individuals have waning immune responses due to aging and chronic comorbidity that makes them more susceptible to infection. COVID-19 atypical symptoms such as falls, delirium, confusion, dizziness, and unusual fatigue in older patients could potentially increase the risk of fracture. Therefore, this project aims to explore the association between COVID-19 infection and fracture incidence and mortality in the elderly aged 50 years and above using the Limited data set.", "accessing_institution": "University of Nevada, Las Vegas" }, { "uid": "RP-D92EDC", "title": "Neurologic sequelae covid - Jennifer Blase", "task_team": false, "dur_project_id": "DUR-9377C95", "workspace_status": "CLOSED", "lead_investigator": "Jennifer Blase", "research_statement": "N3C PHASTR has identified the need to validate, expand and improve upon the work performed by Xu et al. examining the long-term neurological outcomes of Covid-19 (COVID). Our primary objective is to use the N3C Enclave to build a cohort of individuals with COVID, a contemporary control, and a historic control to compare hazard ratios and relative burdens of neurological sequelae of COVID infection. By leveraging our existing knowledge and experience working within the N3C Enclave, we will be able to build upon Xu et al.?s study with a larger, more diverse and extended temporal range of data to quantify differences between populations and contribute to the growing body of COVID research. Our proposal also aims to address limitations in the Xu et al. study including: \n\n* To have a better understanding of the impact of COVID strain on neurological sequelae, we will not only generate hazard ratios for the entire COVID cohort, but also stratify the cohort into time intervals to evaluate temporal trends, while overlaying information on the dominant strain.\n* To examine the impact of different treatment methodologies: vaccination and medications such as remdesivir (Veklury), paxlovid (Nirmatrelvir with Ritonavir) and monoclonal antibodies (Bebtelovimab). Treatments will be examined for impact on hazard ratio and disease burden of neurological sequelae.\n* To create an improved understanding of the co-occurrence of neurological conditions, we will train an unsupervised K-modes clustering algorithm on all neurological disorders in each cohort and then compare and characterize cluster membership.\n* Multiple COVID infections will be considered by further grouping the COVID cohort into subgroups based on quantity of reinfections.", "accessing_institution": "Ruvos" }, { "uid": "RP-30DBD1", "title": "Effect of the pandemic on pulmonary practive", "task_team": false, "dur_project_id": "DUR-94335ED", "workspace_status": "CLOSED", "lead_investigator": "Scott Helgeson", "research_statement": "Pulmonary medicine diagnostic procedures were significantly effected during the COVID-19 pandemic, along with other specialties. We aim to show the impact that the pandemic had on the quantity of pulmonary diagnostic procedures performed during the year 2020. We will use the limited data set for this study.", "accessing_institution": "Mayo Clinic" }, { "uid": "RP-12358B", "title": "[N3C Operational] CMS Claims Data and N3C Clinical data Provider Harmonization Project", "task_team": false, "dur_project_id": "DUR-9454169", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The [N3C Operational] CMS Claims Data and N3C Clinical data Provider Harmonization Project, Data User Request (DUR) submission is an application for data harmonization work needed to leverage two datasets the CMS Claims data and the N3C Clinical Data provider information. Access to the operational data will include NCATS staff, NCATS Contractors, SME form ASPE/AHRQ, N3C John Hopkins DH&I team, and Palantir. The team will curate CMS provider data and harmonize both provider types i.e., MD vs DO and Providers Characterization i.e., Family Physicians, vs Surgeons vs anesthesiologist. \nThe [N3C Operational] CMS Claims Data and N3C Clinical data Provider Harmonization Project data access is strictly to be used for the administrative, methodological functional implementation of CMS and N3C data. Once CMS provider types and characterizations have been harmonized it will be made available all to the N3C investigators for N3C COVID research. N3C investigators as before will not have access to specific provided information or the providers location/health center\n", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-9E38E2", "title": "Neurological sequela of COVID-19 in persons with multiple sclerosis", "task_team": false, "dur_project_id": "DUR-95ADCB1", "workspace_status": "ACTIVE", "lead_investigator": "Michelle Chen", "research_statement": "Multiple sclerosis (MS) is a neurodegenerative disorder characterized by a variety of symptoms, including difficulties with ambulation, visual problems, fatigue, cognitive impairment, and mood disturbances. Many of these are symptoms commonly reported by patients suffering from long COVID. Given MS individuals? pre-existing neurological illness, they may be at increased risk of experiencing more severe or longer lasting neurological sequela from COVID-19. The current study aims to examine the acute and long-term neurological sequela of COVID-19 in persons with MS. Moderating effects of MS disease (e.g., MS phenotype, level of neurological disability, type of disease-modifying therapies used) and demographic characteristics (social determinants of health such as age, race, and socioeconomic status) will also be investigated.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-059EDE", "title": "Disparities in COVID-19 treatment", "task_team": false, "dur_project_id": "DUR-95E2121", "workspace_status": "ACTIVE", "lead_investigator": "Bryan Wilder", "research_statement": "Our project examines racial and ethnic disparities in Paxlovid treatment rates for COVID-19 positive patients within the NCATS N3C cohort. There is aggregate evidence that racial or ethnic minority patients receive such resources at lower rates. However, comparatively little scholarship elucidates the contributing factors driving these disparities. Are differences in resource allocations explained by particular observable characteristics of the patient and setting at the time a clinical decision is made? Or are different allocations made to patients who appear equally ?at-risk? ex ante? Different diagnoses concerning the cause of disparities might yield different implications about the appropriate mechanism for remedying the disparities, e.g., changes to guidelines or methods for assessing risk vs reducing segregation of minority patients into particular facilities. ", "accessing_institution": "Carnegie Mellon University" }, { "uid": "RP-12C7A7", "title": "Does Covid-19 Infection Cause Skin Graft Complications in Full Thickness and Partial Thickness Grafts", "task_team": false, "dur_project_id": "DUR-96954A6", "workspace_status": "CLOSED", "lead_investigator": "henry hoang", "research_statement": "\nThe potential relationship between COVID-19 infection and skin graft complications in full-thickness and partial-thickness grafts has garnered attention in the medical community. This abstract aims to provide a concise overview of the current understanding of this topic.\n\nSkin grafting is a common procedure used in the management of various skin defects, such as burns, trauma, and chronic wounds. However, the impact of COVID-19 infection on the outcomes of skin grafts remains an area of concern.\n\nLimited evidence suggests that COVID-19 infection may contribute to an increased risk of complications in both full-thickness and partial-thickness skin grafts. The systemic effects of the viral infection, including cytokine dysregulation, hypercoagulability, and compromised immune response, may potentially disrupt the delicate process of graft healing and integration.\n\nComplications commonly associated with skin grafts, such as graft failure, infection, delayed healing, and poor cosmetic outcomes, may be exacerbated in the presence of COVID-19 infection. The inflammatory response triggered by the virus could disrupt the vascular supply to the graft, impede neovascularization, and compromise the graft's overall survival.\n\nFurthermore, the immune-suppressive effects of COVID-19 infection might interfere with the body's ability to mount an effective immune response against potential pathogens, leading to an increased risk of graft-associated infections.\n\nHowever, it is important to note that the available literature on this specific topic is limited, and more research is required to establish a definitive causal relationship between COVID-19 infection and skin graft complications. The impact of various factors such as disease severity, patient comorbidities, and management strategies also need to be considered.\n\nIn conclusion, while preliminary evidence suggests a potential association between COVID-19 infection and skin graft complications in both full-thickness and partial-thickness grafts, further investigation is warranted. Healthcare providers should remain vigilant and consider the potential impact of COVID-19 infection on graft outcomes when managing patients requiring skin grafting procedures during the ongoing pandemic.", "accessing_institution": "Albert Einstein College of Medicine" }, { "uid": "RP-9273B6", "title": "COVID-19 Reinfection Rates in Immunocompromised versus non-Immunocompromised Individuals", "task_team": false, "dur_project_id": "DUR-96B7078", "workspace_status": "CLOSED", "lead_investigator": "Esther Melamed", "research_statement": "We are requesting access to de-identified patient data from N3C to study whether immunocompromised individuals may experience more frequent and/or more severe COVID-19 re-infections compared to individuals who are not considered immunocompromised. The results of this study will aid in strategies for vaccination and treatment of immunocompromised individuals during the COVID-19 pandemic. We will primarily use R scripts for data analysis.", "accessing_institution": "The University of Texas at Austin" }, { "uid": "RP-33E16E", "title": "Pharmacological therapies for critically ill patients with COVID-19", "task_team": false, "dur_project_id": "DUR-96E1012", "workspace_status": "ACTIVE", "lead_investigator": "Asad Ebrahim Patanwala", "research_statement": "The pharmacological management of COVID-19 has rapidly evolved after results from randomized controlled trials. Although mortality in the critically ill has improved, it still remains high. There is possibility for heterogeneity of treatment effects based on subpopulations and combinations of therapies used. In addition, there may be changes in effect and risks over time due to new variants. Using the N3C database, we will evaluate the comparative effectiveness of pharmacological therapies in the critically ill to complement and inform clinical trials and guide practice.", "accessing_institution": "University of Sydney" }, { "uid": "RP-39C52B", "title": "Demographic Indicators of Long COVID Subphenotypes", "task_team": false, "dur_project_id": "DUR-97A87E7", "workspace_status": "CLOSED", "lead_investigator": "Katelyn Van Dyke", "research_statement": "Recent research on the post-acute sequelae of SARS-CoV-2 has suggested the existence of four main long COVID subphenotypes correlated with patient demographics. This study utilizes the N3C level 1 dataset and a multilayer perceptron network to determine which demographics have the strongest correlation with long COVID subphenotypes.", "accessing_institution": "University of Missouri" }, { "uid": "RP-B91D17", "title": "COVID-19 and Percutaneous Catheterization Interventions: Modeling Risk of Serious Adverse Outcomes", "task_team": false, "dur_project_id": "DUR-98C972A", "workspace_status": "CLOSED", "lead_investigator": "John Davis", "research_statement": "Little is known about the safety of percutaneous catheterization interventions (PCI) with subsequent COVID-19 infection. Because of the numerous thrombotic complications associated with COVID-19 infection, it is hypothesized that patients receiving PCI and subsequently infected with COVID-19 (within 90 days of PCI) will experience increased rates of stent thrombosis, sudden death, and new cardiovascular disease. Using the N3C database, we aim to assess the odds of suffering a composite endpoint -- PCI complication (defined as stent thrombosis, sudden death, or new myocardial infarction) -- in patients who were infected with COVID-19 within 90 days following PCI, as compared to patients who were not infected by COVID-19 within the same time frame. Logistic regression analysis is planned to assess for comparative odds of this severe composite outcome, while controlling for relevant co-morbidities and demographic factors.", "accessing_institution": "The University of Texas Medical Branch at Galveston" }, { "uid": "RP-B42C9D", "title": "Development and validation of prognostic risk models for COVID-19 in the US incorporating geo-matched information", "task_team": false, "dur_project_id": "DUR-A1C70D8", "workspace_status": "CLOSED", "lead_investigator": "Bingnan Li", "research_statement": "Our preliminary study demonstrated the feasibility of deriving simple-to-use risk scores to predict mortality among hospitalized COVID-19 patients. In this project, we hope to validate and improve our previously developed COVID-19 prognostic risk score in the US population, with further incorporation of geo-matched information. Population-level heterogeneity in COVID-19 mortality and vaccine rates, and the dominant variants of SARS-CoV-2 will be included in the predictive models. Since mid-2020, we have been tracking and collecting national and state-level COVID-19 data, including the daily increased numbers of infected patients, mortality, hospitalization, ICU patients, vaccination uptakes, and dominant variants of SARS-CoV-2, etc. By geomapping such information to individual-level patient electronic health records, we believe further improvement of prognostics models is possible and also in demand, as we have seen dramatic changes in COVID-19 mortality and severe outcomes over time. Functional data analysis and dynamic machine learning approaches will be utilized. We will also develop a fast and practical two-stage clinical prediction rule for a time- and cost-saving triage of COVID-19 patients.", "accessing_institution": "University of Georgia" }, { "uid": "RP-71C163", "title": "Risk modeling for new variants of Covid-19", "task_team": false, "dur_project_id": "DUR-A211D41", "workspace_status": "ACTIVE", "lead_investigator": "Ian Weimer", "research_statement": "The objective of this project is to build a reliable, accurate model for predicting risk for new variants of Covid-19. This is especially important when infected humans are asymptomatic. Various model algorithms will be tested. Depending on the chosen algorithm(s), the most important predictors may be investigated further. N3C data will be used to perform exploratory data analysis, and to train and evaluate the model. Relevant research questions regarding prevalence, general trends, most important variables, most effective algorithms, and optimization will be investigated.", "accessing_institution": "Boston Strategic Partners Inc" }, { "uid": "RP-019E6A", "title": "Post-COVID Outcomes in Patients with Hypertension: Healthcare Utilization, Blood Pressure Management, and Mortality", "task_team": false, "dur_project_id": "DUR-A217B08", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "This research project seeks to examine the post-COVID outcomes in patients with a history of hypertension, focusing on healthcare utilization, blood pressure management, and mortality rates. By leveraging data from the N3C enclave, we will analyze a cohort of individuals diagnosed with COVID-19 who had at least one healthcare visit within one year post-infection. Our primary objective is to determine whether patients with pre-existing hypertension experience worse outcomes compared to those without hypertension. This study will utilize statistical analyses to compare mortality rates, frequency of healthcare visits, and effectiveness of blood pressure management between the two groups. The insights gained from this research will provide valuable information for clinicians and healthcare policymakers to improve post-COVID care for hypertensive patients.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-0CD88E", "title": "Long COVID risk factors for people with and without immune dysfunction", "task_team": false, "dur_project_id": "DUR-A2218D3", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "This study aims to investigate the association between immune dysfunction and Long COVID (Post-Acute Sequelae of SARS-CoV-2 infection - PASC). The objectives are to identify primary risk factors for Long COVID in patients with immune dysfunction compared to those without, and to assess how immune dysfunction influences the severity and prevalence of Long COVID symptoms. Through analyzing patient data, including demographics, clinical history, and symptomatology, the study seeks to uncover distinct risk factors and understand the impact of immune dysfunction on Long COVID outcomes. The findings will contribute to enhancing our understanding of Long COVID, particularly among patients with immune dysfunction, informing tailored interventions and care approaches for this vulnerable population.\n", "accessing_institution": "Axle Informatics" }, { "uid": "RP-04C3DC", "title": "Effect of COVID-19 on Pregnancy Outcomes: Associations with Maternal and Fetal Health", "task_team": false, "dur_project_id": "DUR-A2CCDD6", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "This research project aims to evaluate the impact of COVID-19 on pregnancy outcomes, with a focus on maternal and fetal health. Using data from the N3C enclave, we will assess the associations between COVID-19 infection during pregnancy and key outcomes such as miscarriage, preterm birth, birth weight, maternal health complications, and postpartum mental health. Our primary objective is to determine whether pregnant individuals with a history of COVID-19 face higher risks of adverse pregnancy and postnatal outcomes compared to those without COVID-19. Statistical analyses will be employed to compare these outcomes while adjusting for relevant confounders such as maternal age, comorbidities, and socioeconomic factors. The findings from this study will provide valuable insights for clinicians, policymakers, and maternal health specialists to improve prenatal and postnatal care strategies in the context of infectious disease management.\n", "accessing_institution": "login.gov" }, { "uid": "RP-6ECC47", "title": "Confirming Clinical Trial Results Using Real World Data", "task_team": false, "dur_project_id": "DUR-A692B61", "workspace_status": "CLOSED", "lead_investigator": "Stephen Lee", "research_statement": "The N3C contains a massive store of real world data. Much recent work has focused on the utility of real world data in replicating or augmenting the results of clinical trials. During the COVID-19 pandemic many early and preliminary clinical trials have emerged, we hope to use the N3C database to augment the results of clinical trials within COVID-19.", "accessing_institution": "Saskatchewan Health Authority" }, { "uid": "RP-EAA620", "title": "Data-based evaluation of pediatric COVID-19 treatment guidelines", "task_team": false, "dur_project_id": "DUR-A828856", "workspace_status": "CLOSED", "lead_investigator": "Lorne Walker", "research_statement": "The COVID-19 pandemic has been responsible for severe illness in many vulnerable populations, including amongst the elderly, immunocompromised, and those with chronic medical comorbidities. Relative to adults, children have lower rates of severe disease or mortality, but the impact of COVID-19 on medically high-risk children remains unclear. Providers are therefore limited in counseling patients and families and weighing the risk and benefits of therapeutic interventions. Despite this, due to the emergent nature of the COVID-19 pandemic, certain groups of vulnerable children have been identified as candidates for anti-SARS-CoV-2 therapy. We propose to use the timely and broad inpatient and outpatient data in the N3C data enclave to better characterize the impact of COVID-19 on high-risk groups identified in pediatric COVID-19 guidelines and the adherence of providers to these guidelines.\n\nWe aim to determine the rates of two key outcomes: 1) hospitalization and 2) severe COVID-19 in children infected with SARS-CoV-2 and identified by consensus guidelines as ?high-risk.? We will then compare these rates to children without a high-risk condition. These high-risk groups will include children with immunosuppressive diseases or therapies, obesity, neurodevelopmental disability, dependence on medical technological supports or other chronic disease states, as defined by the National Institutes of Health COVID-19 guidelines. We will also determine what proportion of these children with COVID-19 were given an anti-SARS-CoV-2 therapy, including monoclonal antibodies or antiviral medications. Together, these analyses will help us understand how frequently high-risk children are receiving COVID-19 therapies, and whether the conditions identified in consensus guidelines correspond to children who benefit the most from these therapies. This analysis can be done with level 2 de-identified clinical data offered by the N3C data enclave.\n", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-CA3365", "title": "N3C ImmunoSuppressed/Compromised Task Team: The Impact of COVID-19 on the ISC Population", "task_team": false, "dur_project_id": "DUR-A975976", "workspace_status": "CLOSED", "lead_investigator": "Amy Olex", "research_statement": "Individuals with compromised or suppressed immune systems (ISC) are considered high-risk for developing severe or life-threatening symptoms due to viral infections; however, little is known about the impact of COVID-19 on ISC populations. The ISC population is diverse in the levels, types, and durations of immunosuppression, including individuals with autoimmune diseases, immune compromising medical conditions, such as HIV, and solid organ transplant (SOT) patients that require therapeutics to suppress rejection responses against the allograft. Our team is requesting access to the Level 2 de-identified data to gain a better understanding of how COVID-19 affects ISC sub-populations. Our initial research will focus on a subset of target populations: persons with HIV (PWH), SOT, and autoimmune disorders including skin diseases such as atopic dermatitis and eczema. For each of these, we will ask the following questions: 1) What is the incidence of COVID-19 infection in the target population, and is it higher than in those without the ISC conditions?; 2) Is there an association between the target population and severity of COVID-19 infection?; 3) Is there a difference in severity of COVID-19 infection depending on the type/class of immunosuppressive drug used across the target population?; 4) What is the outcome of the target population after treatment for COVID-19 within those that are hospitalized (e.g. ventilator, LOS, death)? Through this research, we will gain a better understanding of how various types, levels, and durations of immunosuppression or compromise contribute towards ISC patient outcomes upon COVID-19 infection.", "accessing_institution": "Virginia Commonwealth University" }, { "uid": "RP-ABB71D", "title": "Alzheimer's Disease", "task_team": false, "dur_project_id": "DUR-A97711D", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "Problem statement: \nAlzheimer?s has a very long prodromal history prior to diagnosis and on average ~ 7-year life span after diagnosis.  The impact of this illness is enormous not only to the patient but the family.  AD is a growing problem in the USA as the population ages.  AD doubles every 5 years so by age 85 > 60 % of the population will have signs of dementia. At the present time the diagnosis of the types of dementia (vascular vs Alzheimer?s, vs Lewy Body vs Temporal Frontal lobe vs Metabolic (B12/Thyroid)  vs Prion vs Other causes)  is very imprecise. Lists of symptoms of different types of disorders are overlapping and with few pathognomonic bio markers available.  Increasing the precision of the etiology of dementia diagnosis would have large impact for multiple interested parties from better inclusion criteria for research studies, improved patient care, resource utilization, and family support. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-0108E6", "title": "Knockoff generators for feature selection with complex data structures in identifying predictors for COVID 19 acute outcomes", "task_team": false, "dur_project_id": "DUR-AA74812", "workspace_status": "CLOSED", "lead_investigator": "Yushu Shi", "research_statement": "COVID-19 is an infection caused by the SARS-CoV-2 virus, a novel coronavirus causing acute respiratory infection. It is considered a pandemic, causing more than 600 thousand deaths in the US and 4.19 million deaths worldwide. Multiple risk factors have been associated to poor outcomes following COVID-19 infection, including advanced age and cardiac and pulmonary conditions. However, the predictive value of many prognostic factors has not been robustly evaluated and remains unclear and inconsistent across studies. Because these prognostic factors will influence medical decisions and policymaking, the use of unreliable predictors of post COVID-19 outcomes may have widespread deleterious effects. We propose a novel statistical method for feature selection using knockoff filtering techniques and Bayesian modeling that can reliably identify important prognostic factors among complex clinical datasets including both continuous and categorical covariates from a potentially heterogeneous population. The method guarantees good false discovery control while significantly improving the power of detecting true predictive variables compared with existing methods.\n", "accessing_institution": "University of Missouri" }, { "uid": "RP-351A22", "title": "Studying the effect and the risk of adverse outcomes of COVID-19 infection in pediatric and adult patients using statistical and machine learning methods", "task_team": false, "dur_project_id": "DUR-ADC7B3C", "workspace_status": "CLOSED", "lead_investigator": "Corneliu Antonescu", "research_statement": "Coronavirus disease 2019 (COVID-19), a contagious infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a global pandemic by the World Health Organization (WHO) in March 2020. The ongoing pandemic has affected almost every country and every population in a way that is unprecedented in human history. The natural history, response to interventions (including for example vaccines and medical treatments), as well as prognosis and outcomes vary across age groups, with substantial differences between pediatric and adult patient population. This study aims to examine the effect of COVID-19 infection on pediatric and adult patients, to understand factors associated with adverse outcomes, and identify interventions that could potentially improve outcomes after COVID-19 infection. By studying both the pediatric and the adult populations, it will also allow the comparison of these patient groups that hopefully will lead to insights guiding further studies. In addition, given the complexity of medical data and the challenges of working with multi-institutional large-scale healthcare data, we also aim to develop methodological innovations using statistical and machine learning tools to address these challenges, with the hope that our method development will lead to more efficient and effective utilization of rich clinical data both within the N3C platform and a broader health community outside the consortium.", "accessing_institution": "University of Arizona" }, { "uid": "RP-8DE1C3", "title": "Covid -19 Vaccine Efficacy & Disparities in Vaccine Access & Failure in Patients with Hematological Malignancies ", "task_team": false, "dur_project_id": "DUR-B2B18BE", "workspace_status": "CLOSED", "lead_investigator": "Jagar Jasem", "research_statement": "In clinical practice, a relatively high rate of Covid-19 vaccine failure is noticed among patients with hematological malignancies. This can be postulated to be due to the inherently defective immune function of those patients. However, no real-world data are available on the actual vaccine failure rates among patients with various hematological malignancies, and the its potential risk factors ", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-416047", "title": "Revealing Associations Between Cyanobacteria Toxins and COVID Outcomes", "task_team": false, "dur_project_id": "DUR-B4B85BE", "workspace_status": "CLOSED", "lead_investigator": "Anne Thessen", "research_statement": "Toxin-producing cyanobacteria blooms are increasing globally and have caused many illnesses and deaths in humans and animals through an array of secondary metabolites that have hepatotoxic, neurotoxic, and skin irritant properties. Highly-visible acute mortality and illness events have been studied using field reports and laboratory investigations, but less is known about the public health consequences of repeated, low-level exposures over long periods of time. Even less is known about the consequences of these chronic exposures on COVID outcomes. Here we propose to combine observations of cyanobacteria and wind patterns to assign patients to a cyanobacteria exposure risk category. Patients will be divided into cohorts based on these risk categories and analyzed for increased incidence of chronic liver disease, respiratory disease, neurological disease, and COVID severity. We will examine a number of confounding variables and use a propensity matching strategy to ensure adequate considerations for social determinants of health, access to food and healthcare, etc. This pilot will evaluate the hypothesis that chronic, low-level cyanobacteria toxin exposure negatively influences public health and COVID outcomes.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-015A06", "title": "Neurologic Post-Acute Sequelae of COVID-19", "task_team": false, "dur_project_id": "DUR-B6376E5", "workspace_status": "CLOSED", "lead_investigator": "Teshamae Monteith", "research_statement": "Post-Acute Sequelae of COVID-19 may manifest as persistent neurological symptoms including headache, cognitive impairment, autonomic dysfunction, stroke and neuromuscular disorders to name a few. The neuro-domain will search for acute neurologic manifestations during the initial admission and correlate them with short and long-term outcome. We also plan to use population-based National COVID Cohort Collaborative data to examine risk factors, biomarkers, and clinical profiles of PASC with neurological consequences. ", "accessing_institution": "University of Miami" }, { "uid": "RP-6A7A8D", "title": "Identification of Novel COVID-19 Subphenotypes Using Temperature Trajectories", "task_team": false, "dur_project_id": "DUR-C558ADB", "workspace_status": "CLOSED", "lead_investigator": "Sivasubramanium Bhavani", "research_statement": "The coronavirus disease 2019 (COVID-19) pandemic has affected over 35 million people and caused over 1 million deaths as of October, with numbers continuing to rise. Fever is one of the hallmarks of COVID-19, and body temperature is one of the most frequently obtained clinical measurements. Since temperature is closely\ntied to the immune response to infection, the pattern of temperature over time (i.e., temperature trajectories) could predict mortality in COVID-19. Further, temperature trajectories could predict which patients will respond to what therapies. Using large inpatient data from hospitals around the country, I am applying\nmachine learning to identify clusters of patients following distinct temperature trajectories. Understanding the different body temperature responses to COVID-19 will allow us to use this age-old clinical measurement to prognosticate and identify subgroups. These COVID-19 subgroups could lead to the precision medicine\napproach to treating this severe disease.", "accessing_institution": "Emory University" }, { "uid": "RP-5EB760", "title": "Predicting organ failure in patients with COVID-19", "task_team": false, "dur_project_id": "DUR-B7B0496", "workspace_status": "CLOSED", "lead_investigator": "Chengda Zhang", "research_statement": "Patients with COVID-19 may demonstrate completely different clinical course. While most patients remain asymptomatic or mildly symptomatic, some patients could experience progressively worsening multi-organ failure, requiring intensive care. The underlying pathophysiology is unclear, however, early recognition of patients with high risk for deterioration could significantly help with clinical decision making, resource allocation, and potentially shed light on further pathophysiological studies. This project aims to develop machine learning algorithms to select patients' of high risk for respiratory failure, renal failure and heart failure from COVID-19.\nThe data that would be necessary to train the machine learning algorithm would include COVID-19 positive patients' demographics (age, gender, ethnicity, smoking history), existing medical conditions, home medication, vital signs, common lab results (CBC, chemistry, troponin, lactate, arterial/venous blood gas). In order to determine patients' outcome, we would need to access patient's lab results such as serum creatinine, N-terminal (NT)-pro hormone BNP (NT-proBNP), BNP, urinanalysis, arterial/venous blood gas; documentation regarding whether patients required mechanical ventilation, and patients' daily intake-output; and imaging studies such as chest CT, chest Xray, echocardiogram with interpretation. In order to manage the level of noise and increase algorithm performance, potential confounding factors may need to be considered in the algorithm, such as dose and frequency of medications that patients are receiving 2 weeks prior to the onset of acute organ failure, severity of patients' pre-existing organ failure, such as chronic renal failure, COPD/asthma and chronic heart failure (which can be assessed by certain lab results, previous pulmonary function tests, echocardiogram, as well as patient's living situation (home vs long-term care facility)). Ideally, all medication record, vital signs and lab results should be timestamped. ", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-BD7614", "title": "Sleep disorders as risk factors for COVID complications", "task_team": false, "dur_project_id": "DUR-BA5A672", "workspace_status": "ACTIVE", "lead_investigator": "Diego Mazzotti", "research_statement": "Optimal sleep health is essential to modulate immune responses. Sleep disorders such as obstructive sleep apnea and insomnia are highly prevalent in the population and might impact the risk of COVID-19 infections and clinical consequences. In this study, we will explore whether patients diagnosed with sleep disorders are at increased risk of presenting positive COVID-19 diagnosis and worse complications. We will explore the de-identified data available on N3C Enclave to assess the hypothesis that underlying sleep disorders are associated with increased risk of a positive results, as well as worse complications such as increased length of stay, major respiratory diseases readmission rates and mortality.", "accessing_institution": "University of Kansas Medical Center" }, { "uid": "RP-1141A0", "title": "Outcomes of Acute Pulmonary Embolism in COVID-19", "task_team": false, "dur_project_id": "DUR-BAAD38B", "workspace_status": "CLOSED", "lead_investigator": "Muhammad Gul", "research_statement": "Higher incidence of acute pulmonary embolism has been noted in in COVID-19 than in other viral infections. Although it is assumed that pulmonary embolism has an impact on the prognosis in COVID-19 patients, there is scarcity of data in favor of this assumption. N3C database (Level 2) data will be explored to determine outcomes of acute pulmonary embolism in a logistic regression analysis. ", "accessing_institution": "University of Kentucky" }, { "uid": "RP-392493", "title": "Simulation of existing FDA- approved active compounds against COVID protein primary and sub-structures to interrupt protein activity followed by epidemiological, in-vitro and in-vivo validation", "task_team": false, "dur_project_id": "DUR-BCC6ACD", "workspace_status": "CLOSED", "lead_investigator": "Joy Alamgir", "research_statement": "Our overall project attempts to find a viable cocktail of SARS-CoV-2 protein interruption compounds from among 1,251 FDA approved compounds to reduce COVID-19 disease severity or outcome. We plan to do this via (a) our novel high performance quasi-quantum simulation platform using these FDA-approved compounds against SARS-CoV-2 protein structures and sub-structures, (b) epidemiological analysis of discovered candidates, (c) in vitro bio-physical, bio-chemical, and antiviral assays and (d) in vivo small animal model assays. Each of the above steps serve as a selection funnel so that by the time we are ready for in vivo tests a reasonable basis for success will be established. Step (b) above ? the epidemiological analysis step - is the focus on this DUR as we have already conducted super-computing simulation results on 7 of 25 SARS-CoV-2 proteins. We are requesting HIPAA Safe Harbor data without any address, name or birth data information but inclusive of age which is effectively de-identified Level 2 data per our understanding.", "accessing_institution": "ARI Science" }, { "uid": "RP-9506A7", "title": "Assessing Temporal Lab Value Changes and Medications as Predictors of Health Outcomes for COVID19+ Patients", "task_team": false, "dur_project_id": "DUR-BD19A5C", "workspace_status": "ACTIVE", "lead_investigator": "Michael Patton", "research_statement": "Despite significant recent advances in defining the molecular pathophysiology of SARS-CoV2, healthcare professionals still lack a set of easily orderable clinical tests that can predict severity of disease and/or high mortality outcomes in COVID19+ patients. In order to identify predictive trends, we will conduct a temporal analysis of key biometric and laboratory value changes with respect to critical outcome timepoints: \n\n1) First COVID19 positive test \n2) First admission to a critical care unit \n3) Respiratory failure & first required use of mechanical ventilation \n4) Thrombotic events \n5) Sepsis onset \n6) Hospital discharge or patient expiration\n\nParallel to the analysis of predictive biomarkers, we will further expand our investigation by stratifying patient cohorts by select pre-existing conditions and use of select medications to determine their effect on high mortality outcomes.", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-DD281D", "title": "Investigating new-onset neurocognitive complications in COVID-19 patients", "task_team": false, "dur_project_id": "DUR-BECEA2D", "workspace_status": "CLOSED", "lead_investigator": "Jineta Banerjee", "research_statement": "The physiological impact of COVID-19 on various segments of the population has been divergent. While some COVID-positive patients have developed serious cardio-pulmonary complications, others have shown relatively mild pulmonary symptoms. Recent studies in the UK and Spain have shown that a notable percentage of patients showed significant impact to the central nervous system. Whether these impacts only affected the patients in the short term, or if they have longer term consequences is still not well understood. Recent reports suggest that neurological complications of COVID-19 also exist in the US population and may be at a higher prevalence than seen in Europe. We plan to use machine learning and related computational methods to identify features that may be predictive of the new-onset neuro-cognitive complications in people who tested positive for COVID-19 in the US population. We hope that this project will lay the foundation to preemptively identify and monitor new-onset neurocognitive complications due to COVID-19, and assist patients in receiving appropriate and necessary prophylactic care.", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-68403B", "title": "[N3C Operational] Privacy Preserving Record Linkage, PPRL Implementation Data User Request", "task_team": false, "dur_project_id": "DUR-C040ECA", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The [N3C Operational] Privacy Preserving Record Linkage, PPRL Implementation Data User Request submission is an application for access to limited data set for NCATS, Contract staff and community members such as the honest broker staff at Regenstrief Institute, the Datavant team, Johns Hopkins harmonization team, and Palantir staff to test the ability of the external datasets to be linked to the N3C data. The work on the [N3C Operational] Privacy Preserving Record Linkage, PPRL Implementation Data User Request once completed and implemented will be used for ONLY N3C COVID research. However, the [N3C Operational] Privacy Preserving Record Linkage, PPRL Implementation Data User Request is strictly to be used for the administrative, methodological functional implementation of the PPRL technology.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-E80A12", "title": "Understanding Pain and Substance Use Impact During PASC (UPSIDe-PASC)", "task_team": false, "dur_project_id": "DUR-C0D3875", "workspace_status": "ACTIVE", "lead_investigator": "Meredith Adams", "research_statement": "Patients with chronic diseases and conditions are at increased risk of acquiring and having adverse outcomes from COVID-19 infection. Persistent symptoms, including worsening pain and mental health, are being reported among COVID-19 survivors, even among individuals who initially experience a mild acute illness. While we know that social isolation and stigma can amplify and reinforce SUD (e.g., the 19% increase in opioid overdoses during COVID-19), it is unclear how COVID-19 influences pain and substance use treatment adherence and utilization outcomes. Building on existing work, our proposal will characterize the impact of COVID-19 and PASC on pain and substance use treatment outcomes using NIH NCATS N3C (National Covid Cohort Collaborative) data, which has the largest COVID-19 cohort within the U.S with more than 5M patients and out of those, 1.2M are COVID-19 Positive. The rationale for the proposed research is that understanding the most relevant factors for CP and SUD differences in COVID/PASC treatment responses (something that is currently unknown) is critical to designing the most effective interventions. The objective of this research is to characterize CP/SUD interactions with COVID-19/PASC to identify multiple risk factors, subgroup differences, and rare outcomes for SUD treatment adherence and healthcare utilization. Our central hypothesis is that COVID-19/PASC disrupts SUD trajectories and amplifies age, sex/gender, racial, and socioeconomic differences in treatment adherence and recovery. Specifically, we seek to answer the following research objectives: Aim 1) Evaluate timing, duration, and severity of PASC among people with chronic pain, substance use disorder, and co-morbid mental health diagnoses and evaluate any differences compared to an age- and race/ethnicity-matched cohort. This research will support NIH?s goal to enhance the clinical impact of existing treatments at the critical intersection of COVID-19 and pain/substance use conditions and provide the foundation for manuscripts and grants on this topic.", "accessing_institution": "Wake Forest University Health Sciences" }, { "uid": "RP-B9DD40", "title": "Assessment and Impact of Augmented Renal Clearance in Patients with COVID19", "task_team": false, "dur_project_id": "DUR-C118EA7", "workspace_status": "CLOSED", "lead_investigator": "Nicholas Nelson", "research_statement": "Background: The novel SARS-CoV-2 virus (COVID-19) has affected over 14 million patients in the United States since its discovery in December 2019. There have been several reports and studies describing the development of acute kidney injury (AKI) and renal failure as well as augmented renal clearance (ARC) in these patients. The pathophysiology of COVID-19 associated AKI and ARC has not been fully elucidated, but could be mediated through direct injury from viral entry via angiotensin receptors expressed in the kidney, an imbalance in the renin-angiotensin-aldosterone system, pro-inflammatory cytokines expressed in response to viral infection, and/or thrombotic events. The development of AKI and ARC have impact on drug selection and dosing of renally cleared medications including antimicrobials, anticoagulants, and sedatives. \n\nObjectives: The primary objective of this study is to characterize the renal function in patients presenting with COVID-19 and identify risk factors for the development of AKI and ARC in these patients. Secondary objective is to evaluate the impact of renal function alterations in COVID19 patients on medication optimization through:\n?\tAssessing vancomycin dosing differences to target concentration achievement\n?\tCorrelate anticoagulant dosing with markers of anticoagulation\n?\tDetermine differences in dosing of sedative medications to target standard sedation scales\n\nMethods: This study will use data from the National COVID Cohort Collaborative (N3C) for patients diagnosed with COVID-19 by laboratory testing identified by ICD10 codes admitted to participatory sites.\n-\tPrimary\no\tRenal function characterization: The first phase of this study will evaluate the incidence of AKI using AKIN/RIFLE criteria, ARC using calculated creatinine clearance >130 mL/min/1.73m2 via Cockroft-Gault with measured serum creatinine, and normal renal function\no\tModel development for predicting AKI and ARC: Input variables collected from N3C including demographic and clinical data will be used to identify risk factors for the development of AKI and ARC and create a predictive model based on previous literature and COVID19 specific data.\n-\tSecondary\no\tCharacterization of antimicrobial, anticoagulant, and sedative drug selection, dosing, and efficacy\n\n", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-A74026", "title": "Risk for Severe Hypothyroidism", "task_team": false, "dur_project_id": "DUR-C153437", "workspace_status": "CLOSED", "lead_investigator": "Linda Lester", "research_statement": "Determine if the COVID pandemic has increased the risk for severe hypothyroidism, myxedema coma. If there is an increase determine if this is a direct affect of the viral infection versus alterations in access to health care during the pandemic.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-7A17F1", "title": "Management of Acute Cholecystitis in the Post-Pandemic Era", "task_team": false, "dur_project_id": "DUR-C183D98", "workspace_status": "ACTIVE", "lead_investigator": "ASANTHI RATNASEKERA", "research_statement": "The SARS-CoV-2 (COVID-19) pandemic led healthcare resource limitations in response to infection containment, and personnel and resource constraints. As such, the healthcare community worldwide responded by managing some acute care surgical diseases, such as biliary pathologies and acute appendicitis, non-operatively.1 Several societal guidelines had recommended non operative management with observation and intravenous antibiotic administration for acute cholecystitis. Organizations such as the British Intercollegiate General Surgery Guidance (BIGSG),2 the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES),3 and the European Association for Endoscopic Surgery (EAES)4 have stated that a more conservative approach to surgery, defined as antibiotic therapy, drainage of the gallbladder and ?watchful waiting,? is preferred in order to limit resource utilization and exposure risk. Some studies have even revealed that the conservative approaches for acute cholecystitis during this time frame was preferred due to possible increases in length of stay, mortality and cost. 5,6 A systematic review performed by Stavridis et al demonstrated that a shift towards using non operative management occurred during the pandemic as compared to the pre-pandemic times, with an increase in length of stay in those patients managed non operatively and with drainage with a percutaneous cholecystostomy tube.7 However, prior to the pandemic the standard of care for acute calculous cholecystitis was operative management with laparoscopic or open cholecystectomy.8,9 \n\nAs we enter the post-pandemic era, there is a paucity of research on the current practice patterns of surgeons given the previously reported outcomes of increased non-operative management for those with acute cholecystitis. We seek to perform a large nationwide database review in order to further delineate current practice patterns along with outcomes for those patients with acute cholecystitis managed in the post-pandemic period.\n\n", "accessing_institution": "Christiana Care Health System" }, { "uid": "RP-36C523", "title": "Racial Disparities and Technology Use Associated with Shorter Hospitalization for Covid-19 Patients", "task_team": false, "dur_project_id": "DUR-C29B618", "workspace_status": "ACTIVE", "lead_investigator": "Nirup Menon", "research_statement": "There is evidence at the population level of disparities in overall health outcomes during the Covid-19 pandemic. The goal of this research is to study disparities in two ways. First, the study will aim to determine if there is evidence of systematic biases in services/treatments provided at the patient level. Second, a predictive model for services provided/treatments will be developed with demographic and clinical predictor variables. The predictions produced by the model will be analyzed for disparities.", "accessing_institution": "George Mason University" }, { "uid": "RP-0CAE0C", "title": "Machine Learning for Disparities in COVID-19 Patient Outcomes", "task_team": false, "dur_project_id": "DUR-C55165C", "workspace_status": "CLOSED", "lead_investigator": "Vicki Hertzberg", "research_statement": "Racial disparities in the clinical course of COVID-19 illness exist. In particular, Blacks experience more severe disease and higher case fatality rates than whites. The reasons for these differences are not readily apparent. A better understanding of patient-specific factors predicting COVID-19 outcomes in Black hospitalized patients will improve understanding of racial disparities in this disease. To this end we propose to use machine learning techniques to develop predictive models for adverse clinical outcomes among Black and white patients hospitalized with COVID-19, addressing the following specific aims and research questions:\n1. Aim 1: Predict adverse patient outcomes in Black and white hospitalized COVID-19 patients using machine learning.\n? How well do existing risk scores predict patient outcomes in this population?\n? Can two machine learning models, one trained on Blacks and the other on whites, provide better\npredictions of patient outcomes?\n? How do Blacks and whites compare for existing risk scores as well for our model(s)?\n2. Aim 2: Develop an open-source software toolkit.\nThis project will lay the foundation for future refinement of existing machine learning methods as well as development of new methods to improve prediction of COVID-19 outcomes for Black hospitalized patients.", "accessing_institution": "Emory University" }, { "uid": "RP-50C3D6", "title": "COVID-19 medications prescribing habits throughout the pandemic", "task_team": false, "dur_project_id": "DUR-00D3C48", "workspace_status": "ACTIVE", "lead_investigator": "Scott Helgeson", "research_statement": "COVID-19 has infected nearly 11 million people in the US and there is limited effective treatments for the disease. There has been many potential treatments that have come and gone and have not proven to be effective. This study is aimed towards determining the use of specific medications and how frequently they were used throughout the pandemic and how prescribing habits were effected by publications in the medical literature. We will also be looking at how social media impacted these prescribing habits.", "accessing_institution": "Mayo Clinic" }, { "uid": "RP-DEB00E", "title": "Understanding Disparities in Post-Acute Sequelae of SARS-CoV-2 (PASC) Care: An Analysis of Community-Based Variances", "task_team": false, "dur_project_id": "DUR-C65B810", "workspace_status": "ACTIVE", "lead_investigator": "SUCHETHA Sharma", "research_statement": "This project seeks to conduct a comprehensive investigation into the disparities in healthcare provision during the acute infection period among PASC patients, with a focus on communities delineated by race, ethnicity, socioeconomic status, and other relevant factors. The study cohort includes COVID positive patients with either PASC/non-PASC (Post-Acute Sequelae of SARS-CoV-2): with an ED visit (defined as any ED visit that occurred in the 16 days after or 1 day prior to the COVID index date) and COVID associated hospitalization. The response variables in this study are whether a patient has PASC or not. We leverage the Bayesian Hierarchical Model in understanding these disparities. We first look at PASC patients with PASC diagnosis and later compare the results to the computable phenotype[1] PASC patients.\n", "accessing_institution": "University of Virginia" }, { "uid": "RP-155D61", "title": "Incidence of Severe Maternal Morbidity among Admitted Pregnant Patients with COVID-19", "task_team": false, "dur_project_id": "DUR-C6B633E", "workspace_status": "CLOSED", "lead_investigator": "Claire Aucoin", "research_statement": "The goal of this project is to determine if pregnant patients with COVID-19 were more likely to develop severe maternal morbidity (SMM) compared to pregnant patients without COVID-19. Considering that COVID-19 can cause severe illness in high-risk groups and multi-systemic effects, the effects of COVID on pregnancy are essential to determine. The current literature on SMM incidence in COVID-19 patients is limited, with different articles contradicting the others? results, and lack of studies that compare patients to healthy controls. This study aims to build upon existing descriptive studies from specific geographic regions of the by comparing serious maternal morbidity and coexisting admission diagnoses among SARS-CoV-2-affected pregnant patients to uninfected pregnant patients. Using the Level 3 limited dataset from the N3C Enclave, we plan to evaluate the clinical courses and outcomes of pregnant patients recorded in the Enclave. This would allow us to see a diverse set of patients from multiple hospitals, which would include a large number of severely ill pregnant patients. We would also like to determine if there are any shared risk factors and demographics between COVID-19 and SMM.", "accessing_institution": "Medical University of South Carolina" }, { "uid": "RP-3C74AB", "title": "COVID-19 prognostic factors and outcomes on adult inpatients to guide management", "task_team": false, "dur_project_id": "DUR-CAD1802", "workspace_status": "CLOSED", "lead_investigator": "Eileen Lee", "research_statement": "Although the COVID-19 pandemic is the largest emergent health crisis of the 21st century, the reasons for variation in clinical severity remain poorly understood. Accurately predicting COVID-19 prognosis early can lead to better patient outcomes and more appropriate resource utilization. We have developed several models for predicting COVID-19 prognosis from a retrospective cohort of 969 hospitalized COVID-19 patients at Robert Wood Johnson University Hospital (RWJUH) during the first pandemic wave in the United States. One of two main models uses age and five widely-available laboratory values to predict COVID-19 mortality. This model was better able to predict mortality (AUC ROC=0.793, F1=0.564) than a commonly used clinical prediction rule for pneumonia severity (CURB-65; AUC ROC=0.722, F1= 0.547). The other major initiative is investigating how a patient?s C-reactive protein levels can be used to predict COVID-19 prognosis in conjunction with demographic data. With increasing age, or with a CRP value > 10 within the first five hospital days, patients had increased odds of severe COVID (OR: 1.02 per 1-year increase in age, p <0.001, OR: 3.78, p < 0.001, respectively). Using data derived from NC3, we now aim to validate our models on patients outside of our home institution and publish an open source version of our finalized models, so that front-line clinicians can utilize these algorithms in their own patient population.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-0CBC12", "title": "Identifying comorbidity risk factors for brain fog and associated cognitive decline by COVID-19", "task_team": false, "dur_project_id": "DUR-CE080B4", "workspace_status": "CLOSED", "lead_investigator": "Thanaphong Phongpreecha", "research_statement": "This research aims to leverage multiple data sources to identify pre-existing comorbidities that render an individual more susceptible to developing brain fog after having long or severe COVID-19. We also aim to identify which cognitive domain is most affected by this condition.", "accessing_institution": "Stanford University" }, { "uid": "RP-845AAA", "title": "Health Disparities Associated with COVID-19", "task_team": false, "dur_project_id": "DUR-CE6DC3B", "workspace_status": "CLOSED", "lead_investigator": "Zhen Cong", "research_statement": "The COVID-19 pandemic has deepened health disparities by race/ethnicity and socioeconomic status. In particular, the inequity in allocation of and access to resources has disproportionately affected racial/ethnic and socioeconomically disadvantaged populations. This study aims to (1) identify the complex pathways that shape health disparities during the COVID-19 pandemic, and (2) develop effective and equitable resource planning strategies to minimize the health impacts of COVID-19 across the entire population and disparities in vulnerable populations. We will use N3C data to conduct hierarchical multivariate analysis for the health disparity analysis (Aim 1) and inform a decision analytics approach that combines agent-based simulation, network modeling, and mathematical optimization to support decision-making in resource planning (Aim 2). The Level 3 Limited Data Set is requested. ", "accessing_institution": "The University of Texas at Arlington" }, { "uid": "RP-18E485", "title": "Utilization of the National COVID Cohort Collaborative (N3C) Data Enclave to Evaluate the Association of Alpha- 2 Adrenergic Receptor Agonist Dexmedetomidine Use and Mortality in Patients with COVID-19", "task_team": false, "dur_project_id": "DUR-D0DE010", "workspace_status": "ACTIVE", "lead_investigator": "John Hamilton", "research_statement": "We have previously demonstrated at Rush University System for Health (RUSH) hospitals that use of alpha-2 adrenergic receptor (?2 AR) agonist dexmedetomidine (DEX) in critically ill patients with COVID-19 is associated with reduced mortality on retrospective cohort analysis. In this previous study, a limitation of our study was restriction of patient population to one hospital system (RUSH) assessing a total of n = 214 patients. In our current study, we plan to repeat our retrospective assessment of DEX use and COVID-19 mortality. However, instead of using only RUSH patient data, we will utilize a national database. The National COVID Cohort Collaborative (N3C) Data Enclave provides an electronic health record repository for COVID-19 positive patients across a large number of health systems nationally. We hypothesize use of the N3C data enclave will further demonstrate that DEX use is associated with reduced mortality in critically ill patients with COVID-19 on retrospective analysis. ", "accessing_institution": "Rush University Medical Center" }, { "uid": "RP-49C096", "title": "Changes in diabetic retinopathy severity and diabetic macular edema in COVID-19 patients", "task_team": false, "dur_project_id": "DUR-DAC5B6B", "workspace_status": "CLOSED", "lead_investigator": "Fritz Kalaw", "research_statement": "This project aims to determine the possible effects of COVID-19 in terms of changes in the severity of diabetic retinopathy and diabetic macular edema among COVID-19 patients. Diabetic retinopathy is one of the leading causes of preventable blindness. As certain microvascular changes affect the retina, patients with diabetes and concomitant retinopathy may be at risk of progressing to a more severe type of diabetic retinopathy or even developing diabetic macular edema, which may need intraocular treatment (laser photocoagulation, intravitreal injection, or vitrectomy). ", "accessing_institution": "University of California, San Diego" }, { "uid": "RP-CA8E68", "title": "COVID-19 Symptoms and Outcomes in Black, Latinx, and White Populations (HOPE: Health Equity Outcomes across Diverse Populations)", "task_team": false, "dur_project_id": "DUR-D199CEB", "workspace_status": "CLOSED", "lead_investigator": "Celeste Schultz", "research_statement": "COVID 19 is a disease state stemming from the SARS-Co-V-2 virus, which presents with poorly understood and variable symptom presentations. Research suggests that Black and Latinx populations have higher rates of SAR CoV-2 infections and mortality compared to non-Hispanic White populations. Black and Latinx populations are also at greater risk for experiencing comorbid states that are likely related to social determinants of health. Hence, these populations are considered socially vulnerable and often experience poorer health outcomes, especially for those who have contracted COVID-19. COVID-19 symptom presentation and symptom clusters in Black, Latinx and White populations, and the mediating or moderating influence of social vulnerability on the association between COVID-19 symptom presentation, symptom clusters, and health outcomes among these populations remain to be elucidated. Our research questions are: 1) Do COVID-19 symptom presentation and the clustering of those symptoms impact health outcomes among Black, Latinx, and White populations? 2) Does social vulnerability directly or indirectly, i.e., mediate or moderate, COVID-19 symptom presentation and symptom clustering to predict health outcomes in Black, Latinx and White populations? To answer these questions, we specifically aim to a) determine symptom presentation prevalence, b) determine COVID-19 symptom clusters, c) determine if COVID-19 symptom cluster membership predicts clinical outcomes in Black, Latinx and White populations and d) determine whether social vulnerability mediates or moderates the association between COVID 19 symptom presentation, symptom clusters, and health outcomes in Black, Latinx and White populations. \nUsing the limited data set we will access: (1) full zip codes, (2) dates of service reflective of the duration of hospitalization, and (3) 14 COVID-19 symptoms identified by the CDC. We will link zip codes to the CDC/ATSDR Social Vulnerability Index, which is associated with census tracts using the Geospatial Research, Analysis and Services Program (GRASP) database1. Because multiple zip codes may be associated with a census tract, we will link census tracts to zip codes using the HUD USPS Zip Code Crosswalk Files2 to achieve an accurate representation of those who are socially vulnerable. We will conduct: a) descriptive statistics to determine symptom prevalence, b) latent class analysis to determine COVID-19 symptom clusters, c) regression analysis to determine if COVID-19 symptom cluster membership predicts clinical outcomes in Black, Latinx and White populations, and d) generalized linear models with interaction terms and structural equation models to determine whether social vulnerability mediates or moderates the association between COVID-19 symptom presentation, symptom clusters and health outcomes in Black, Latinx and White populations.\n\n", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-2AD3C8", "title": "Immune response in preexisting autoimmune patients to SARS-CoV-2 variants: A retrospective study focus on severity, long COVID and vaccination status based on N3C data. ", "task_team": false, "dur_project_id": "DUR-D29D94E", "workspace_status": "ACTIVE", "lead_investigator": "Arjun Yadaw", "research_statement": "Our preliminary study demonstrated that pre-existing autoimmunity is associated with increased severity in COVID-19 patients. There are several unanswered questions (e.g., association of pre-existing autoimmunity for different variants severity, impact of vaccination status on different variants, its relationship with long COVID etc.). In this project, we hope to validate our pre-existing autoimmune and patients on immunosuppressant cohorts for different variants, vaccination status, long COVID and usage of antiviral treatments. Functional data analysis and stats model will be used to answer relevant questions associated with different variants and autoimmune/immunosuppressants patients. We need PPRL data access to answer these questions. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-71ACAB", "title": "COVID-19 in women with Polycystic Ovary Syndrome", "task_team": false, "dur_project_id": "DUR-D56AD89", "workspace_status": "CLOSED", "lead_investigator": "SNIGDHA ALUR-GUPTA", "research_statement": "Polycystic ovary syndrome is the most common endocrine disorder in reproductive age women. It is associated with several comorbidities including obesity, diabetes, dyslipidemia, metabolic syndrome and possibly cardiovascular disease. Recent data from the UK suggests that women with PCOS have an increased risk of being diagnosed with COVID-19. There is no information on symptomatology, hospital or ICU admissions or outcomes in women with PCOS infected with COVID-19. We propose to examine the outcomes of COVID-19 in women with PCOS compared to age and BMI matched controls. In this retrospective study we will examine the risk of admissions and associated complications in women with PCOS accounting for typical predictors such obesity, age, race and cardiometabolic risk factors to see if and by how much they differ from those without PCOS. ", "accessing_institution": "University of Rochester" }, { "uid": "RP-13052B", "title": "Understanding non-invasive ventilation treatment failures in COVID-19", "task_team": false, "dur_project_id": "DUR-D6A31CA", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Chow", "research_statement": "Intubation and invasive mechanical ventilation (IMV) is widely recognized as a necessary intervention for patients suffering from respiratory failure. Critically ill COVID-19 patients often present with severe hypoxia, prompting the need respiratory support. However, early reports revealed a high rate of mortality among COVID-19 patients who received mechanical ventilation. Additionally, intubation presents risks for COVID-19 transmission to providers. This has prompted further investigation into noninvasive mechanical ventilation (NIMV) to avoid intubation if possible, such as high flow nasal cannula (HFNC) or noninvasive positive pressure ventilation (NIPPV). Both HFNC and NIPPV are proven modalities in non-COVID-19 pathologies, such as flash pulmonary edema and COPD exacerbations. NIPPV was shown to reduce intubation by 70% for critically ill SARS-Cov-1 patients. Data is still limited on whether NIPPV reduces intubation in SARS-Cov-2. This study aims to better understand the characteristics of patients with COVID-19 that are most likely to benefit from NIMV in order to guide clinical decision making. ", "accessing_institution": "George Washington University" }, { "uid": "RP-951C17", "title": "Unsupervised Machine Learning Method for the Discovery of Latent Clusters in COVID-19 Patients in the US and comparison with New York.", "task_team": false, "dur_project_id": "DUR-D6E5694", "workspace_status": "CLOSED", "lead_investigator": "Jinyan Lyu", "research_statement": "A recent unsupervised machine learning research using Electronic Health Records (EHR) data from Mount Sinai Health System in New York, USA helped determine the relationship between patients with chronic diseases who tested positive for covid-19 and mortality rate.\nThe purpose of this paper is to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients in the nationwide dataset. Since we have conducted similar projects using EHR data from New York, we could compare the difference between the US and NY. To explore the similarity and difference of how covid-19 influences patients with medical conditions.", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-3EF323", "title": "Probenecid as a treatment for COVID-19", "task_team": false, "dur_project_id": "DUR-D7CB14A", "workspace_status": "CLOSED", "lead_investigator": "David Martin", "research_statement": "Probenecid is a uricosuric agent that was originally approved in the early 1950?s as a treatment for gout. More recently, it has been shown to have potent, in vitro and in vivo antiviral activity against a range of respiratory viruses, including SARS-CoV-2, influenza, and RSV. Importantly, data from a Phase 2 dose-range finding study in non-hospitalized patients with symptomatic, mild-to-moderate COVID-19 confirmed the potent antiviral activity. In that study, treatment with probenecid resulted in a significant, dose-dependent decrease in the time to viral clearance and a significantly higher proportion of patients reporting complete symptom resolution by day 10. Given the changing dynamics of COVID-19, it is becoming very challenging in demonstrating a clinically meaningful reduction in hospitalizations or death associated with more severe disease. The purpose of this proposal is to use the N3C database to evaluate the potential effect of probenecid on the rate of infection and severity of disease COVID-19 disease using hospitalizations or death as the endpoint. ", "accessing_institution": "Tripp Bio" }, { "uid": "RP-C00017", "title": "Orthopaedic Surgery in the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-D893C74", "workspace_status": "CLOSED", "lead_investigator": "Eli Levitt", "research_statement": "The purpose of this study is to analyze the admission rate and inpatient hospitalization measures associated with orthopedic patients who presented during the global COVID-19 pandemic.", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-B0C8C9", "title": "Analysis of previous exposure to influenza vaccine and influenza virus to COVID-19 Patient Outcomes", "task_team": false, "dur_project_id": "DUR-17514A6", "workspace_status": "CLOSED", "lead_investigator": "Paul Grant", "research_statement": "We hypothesize that exposure to either an influenza infection, or the seasonal influenza virus has a positive influence on COVID-19 positive patients and their documented medical outcomes. This study will explore influenza vaccine and infections and their relationships to the outcomes of COVID-19 positive patients.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-33331B", "title": "Impact of Co-morbid Sleep Disorders and Post-Acute Sequelae of SARS-CoV-2 infection (PASC) ", "task_team": false, "dur_project_id": "DUR-D8BD0E0", "workspace_status": "CLOSED", "lead_investigator": "Eilis Boudreau", "research_statement": "One out of every three people will be impacted by a sleep disorder at some point in their lives leading to daytime fatigue and dysfunction, chronic health consequences, and even occupational and motor vehicle accidents. Obstructive sleep apnea (OSA), one of the most common sleep disorders, is present in up to 20% of the general population, and is associated with increased risk of cardiovascular disease, fatigue, and impaired daytime function. OSA has also been linked with increased COVID-19 hospitalization and respiratory failure but little is known about long-term outcomes in individuals with OSA and PASC. In this study, which will be undertaken with graduate students and post-doctoral fellows in the OHSU Department of Medical Informatics and Clinical Epidemiology as part of course-related projects or exploratory research rotations, we would like to explore the synthetic data in the N3C Enclave to evaluate the following questions:\n\n(a) Is PASC associated with increased adverse cardiovascular and functional outcomes in individuals with OSA? \n(b) Does treatment with continuous positive airway pressure (CPAP) prevent adverse PASC outcomes in individuals with OSA?\n(c) Identify data elements available in the Enclave which are available to test sleep/fatigue hypotheses related to PASC and identify any key missing data elements that if added would facilitate our understanding of PASC. \n(d) Explore the mapping of N3C data elements to some of the existing sleep-related terminologies.\n", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-AEDE40", "title": "NIRVANA", "task_team": false, "dur_project_id": "DUR-D9A68E8", "workspace_status": "CLOSED", "lead_investigator": "Joel Michalek", "research_statement": "Title:\tNIcotinamide Riboside in SARS-CoV-2 pAtients for reNAl protection (NIRVANA)\nStudy Description: A pilot, double-blind, placebo-controlled, multicenter, interventional clinical trial with oral nicotinamide riboside (NR) 500 mg (versus placebo) twice daily for a total of 10 days in hospitalized participants with COVID-19.\nObjectives:\n\tThe primary objective is to determine the effect of NR on AKI free days during 10-day intervention\n\tThe secondary objective is to evaluate the effect of NR on MAKE criteria (development or progression of chronic kidney disease, the initiation of long-term dialysis, or death from any cause) at 30 and 90d post randomization\n\tThe exploratory objectives are (i) to determine the safety of NR in participants admitted with COVID-19 and AKI compared to placebo during the 10-day intervention; (ii) to determine the effect of NR on degree of kidney impairment at 30 and 90d post randomization\nEndpoints: The primary endpoint is an ordinal categorical outcome with four categories (labeled 1, 2, 3, 4), defined as, in order from least to most severe disease, 1. Serum creatinine AUC below the median, 2. Serum creatinine AUC greater than or equal to the median, 3. Dialysis, 4. Death. \n\tSecondary Endpoint: MAKE criteria (doubling of serum creatinine, the initiation of long-term dialysis, or death from any cause) at 30 and 90d post randomization\n\tExploratory endpoints: (i) safety of NR in participants admitted with COVID-19 and AKI compared to placebo during the 10-day intervention; (ii) eGFR and proteinuria at 30 and 90d post randomization\nStudy Population: Participants will be >18 years old and admitted to hospital with a laboratory diagnosis of COVID-19 infection and AKI\n", "accessing_institution": "The University of Texas Health Science Center at San Antonio" }, { "uid": "RP-3E423B", "title": "Climate as a Risk Factor for Long Covid", "task_team": false, "dur_project_id": "DUR-DA30595", "workspace_status": "CLOSED", "lead_investigator": "Robert Cockrell", "research_statement": "Several pathological conditions demonstrate correlations between the prevalence of disease and climate-related variables; notable among these is Multiple Sclerosis. We hypothesize that a similar correlation exists relating climate-related variables and the likelihood of developing long covid. In this work, we will examine the correlation between temperature patterns, total sunlight exposure, and the development of Long Covid, indicated by the ICD-10 code, U09.9, using patient locations defined by zip code and weather patterns between the initial covid diagnosis and the diagnosis of Long Covid. We are requesting Level 3 data in order to obtain more precise patient locations and actual diagnosis dates; the inclusion of the 5-digit zip code and actual dates of diagnoses will allow us to rigorously define climate/weather states over time for each patient. \n", "accessing_institution": "University of Vermont" }, { "uid": "RP-158053", "title": "Design Data-driven Prescriptive AI Models to Support Clinical Decision-Making for the Treatment of COVID-19", "task_team": false, "dur_project_id": "DUR-DA69D59", "workspace_status": "CLOSED", "lead_investigator": "Ujjal Mukherjee", "research_statement": "There are several risk prediction models that aim to predict the risk of hospitalization, severity, and death from COVID-19. However, prediction of risk of hospitalization, severity, and death does not automatically translate into clinical decision making that has the potential to direct treatment and clinical management of COVID-19 patients. Precise clinical decisions on effective treatment of COVID-19 patients require computational data-driven optimization of the individual patient level risks versus potential benefits associated with therapeutic management such as medication, ventilation, early ED admissions, etc. Additionally, such decisions need to dynamically account for the patient?s existing general health conditions, comorbidities, and treatment-induced emerging response and adverse events. In this project, we intend to develop a data-driven optimal prescriptive model that can inform health care providers to undertake the right interventions at the right time for individual patients. Furthermore, the models will focus on patients from traditionally underserved populations.", "accessing_institution": "University of Illinois at Urbana Champaign" }, { "uid": "RP-EDA0C8", "title": "RADx Long COVID Prediction Challenge (Continuation)", "task_team": false, "dur_project_id": "DUR-DAB5E59", "workspace_status": "CLOSED", "lead_investigator": "Parker Combs", "research_statement": "The emergence of post-acute sequelae of SARS-CoV-2 (PASC) is presenting serious and ongoing impact on people?s health and the American health care system. While details on the prevalence, causes, treatment and consequences of PASC are actively being researched, growing evidence suggests that more than half of COVID-19 survivors experience at least one symptom of PASC at six months after recovery of the acute illness. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial COVID-19 disease. Advancements in the software tools using Artificial Intelligence (AI)/Machine Learning (ML) approaches may enable the potential for providing clinical decision support on candidate prognostic factors and assessments of a patient?s risk to developing PASC.\n\nTo that end, we are conducting a community challenge within the National COVID Cohort Collaborative (N3C) enclave sponsored by the Rapid Acceleration of Diagnostics (RADx) initiative to engage with the machine learning community to develop risk prediction models for identifying COVID patients who are at risk of developing long COVID. We will establish a gold standard true positive dataset against which risk prediction models will be benchmarked. Using N3C data, challenge organizers will identify viable challenge questions focused on predicting long COVID and the associated risks. Participants in this challenge will build models on a training dataset established by the challenge organizers. Those trained models will then be tested on a holdout set to establish initial model accuracy. These trained models will be evaluated against a battery of accuracy and generalizability tests including longitudinal generalizability, cross-site generalizability, hold-out dataset accuracy, and prospective evaluations.", "accessing_institution": "University of North Dakota" }, { "uid": "RP-E05C7B", "title": "COVID-19 Trend Analysis and Predictive Modeling with N3C Data", "task_team": false, "dur_project_id": "DUR-DBA1255", "workspace_status": "ACTIVE", "lead_investigator": "I-Jou Chi", "research_statement": "Background: Technological advances enable more effective prevention and control of emerging infectious diseases, such as COVID-19, through big data analysis and artificial intelligence (AI). These tools enhance the accuracy of predictions, accelerate disease monitoring, and improve treatment outcomes. Our project will leverage the NIH National COVID Cohort Collaborative (N3C) database to analyze the evolutionary trends of COVID-19, establish a comprehensive model for monitoring, and apply the model parameters to Taiwan's clinical data. This project aims to enhance the understanding of COVID-19 evolution, establish a model that provides real-time insights into disease trends, and support external clinical decision-making.\nMethods and Objectives:\n1.\tAnalyze COVID-19 Evolutionary Trends: Utilize the NIH N3C database to study the evolutionary trends of COVID-19, focusing on variant emergence, disease progression, and patient outcomes. Using Python tools within the Palantir platform, we will perform a Quantifying Transmission Efficiency analysis?our published method for analyzing non-linear causal relationships in time series?to depict relationships between outbreak sources.\n2.\tDevelop a Monitoring Model: Build a robust, AI-driven model within Palantir?s modeling module to monitor COVID-19 trends. This model will integrate tier 2 big data from clinical, demographic, and N3C sources to improve predictive accuracy.\n3.\tApply the Model to Taiwan?s Clinical Data: Adapt the developed model to Taiwan?s clinical context by exporting key parameters (but not data) from the N3C model. These parameters will be applied to local datasets to generate region-specific insights into disease evolution and patient management.\nAnticipated Results:\n1.\tComprehensive Insights into COVID-19 Evolutionary Trends: By leveraging the NIH N3C database, we expect to gain a detailed understanding of the evolutionary trends of COVID-19, including variant emergence, disease progression, and patient outcomes. The Quantifying Transmission Efficiency analysis will reveal the non-linear relationships between outbreak sources, providing insights into the transmission dynamics of the virus.\n2.\tCreation of a Robust Monitoring Model: Using the AI-driven modeling tools within Palantir, we anticipate developing a highly accurate, real-time monitoring model for COVID-19 trends. This model will integrate diverse N3C data sources to predict and track disease patterns, enabling proactive public health responses and enhanced predictive accuracy for critical outcomes.\n3.\tSuccessful Application of Model Parameters to Taiwan?s Clinical Data: By exporting critical parameters from the N3C-based model and adapting them to Taiwan?s clinical datasets, we expect to generate region-specific insights into COVID-19 trends. This will facilitate improved patient management, tailored interventions, and more accurate risk predictions, enabling healthcare providers in Taiwan to make informed, data-driven decisions.", "accessing_institution": "National Yang Ming Chiao Tung University" }, { "uid": "RP-DA2B0B", "title": "COVID 19 outcomes in Cardiooncology patients", "task_team": false, "dur_project_id": "DUR-DCEA74B", "workspace_status": "CLOSED", "lead_investigator": "Brijesh Patel", "research_statement": "Preliminary data showed that cancer and heart diseases are amongst common comorbidities in patients who died of COVID-19. However, data on patients suffering from both cancer and heart diseases is unknown. Recent and concomitant chemotherapy use can mitigate immune response and make patients with cancer more prone to infection. Certain cardiac conditions such as heart failure could also worsen inflammatory state and subsequently blunting immune response. Thus, outcomes of patients with cancer and cardiac conditions could be worse. To date, no large scale data has looked at the outcomes of patients with cancers and co-existing cardiac conditions. ", "accessing_institution": "West Virginia University" }, { "uid": "RP-52BE69", "title": "COVID-19 impact on pregnancy and fetal wellbeing", "task_team": false, "dur_project_id": "DUR-DD584A3", "workspace_status": "CLOSED", "lead_investigator": "Li Li", "research_statement": "Scientific Goals: To determine the impact of COVID-19 on of fetal loss rates, preterm birth, and growth restriction. Secondary outcomes include preterm birth and other complications of pregnancy that have underlying placental abnormalities.\nObjectives, designs, and plans: The COVID-19 pandemic has caused substantial mortality. Sema4 carried out a pilot study using COVID-19 data from the Mount Sinai Health System EMR. Our pilot study results are detailed in a manuscript entitled ?Analysis of hospitalized COVID-19 patients in the Mount Sinai Health System using electronic medical records (EMR) reveals important prognostic factors for improved clinical outcomes? (MedRxiv and accepted by BMJ open). We realized that a significant number of critical data fields are needed in EMR, which will lead a better systematic evaluation on the safety and efficacy of different treatments, as well as identification of additional risk factors for prognostic outcomes. We believe the N3C COVID data provides the opportunity for a more expansive assessment of the impacts of COVID-19 on human disease and wellness, and a richer dataset to build and test predictive models that may better assess the risk of complications from COVID-19. Recent studies have raised significant concern for pregnant women and the adverse impacts on the fetus as a result of COVID. Therefore, we will apply state-of-art machine learning methods to analyze pregnancy journey with deliveries on multi-centers? data from N3C to assess clinical outcomes impacted by COVID-19 and subgroup analysis as well as to identify the risks of prediction models for better managing outcomes in clinical practice.", "accessing_institution": "Mount Sinai Genomics Inc" }, { "uid": "RP-55129C", "title": "Statistical Methods for Incorporating Observational Data on Covid-19 Infected individuals into sub-group analyses for randomized controlled trials evaluating therapeutics for Covid-19", "task_team": false, "dur_project_id": "DUR-E443D39", "workspace_status": "CLOSED", "lead_investigator": "Thomas Murray", "research_statement": "This project focuses on the development of Bayesian methods that allow for the incorporation of observational data into randomized clinical trials with the aim of improving power for key comparisons and precision of estimates. These methods aim to account for potential systematic biases as well as measurable selection biases. ", "accessing_institution": "University of Minnesota" }, { "uid": "RP-4FA13D", "title": "Black race and Pulmonary Fibrosis after COVID-19 Hospitalization", "task_team": false, "dur_project_id": "DUR-E46AA3E", "workspace_status": "ACTIVE", "lead_investigator": "Rachel Baccile", "research_statement": "Death rates from chronic respiratory diseases have recently increased, largely driven by the rising burden of interstitial lung diseases (ILDs) doubling mortality rates over the past 4 decades.1,2 Pulmonary fibrosis (PF), a form of ILD, is characterized by destruction of lung tissue and accounts for the highest increase in mortality rates.3,4 The disproportionate impact exerted by ILD on PF-related outcomes such as respiratory-related deaths is a function of its epidemiological burden, greater disease severity, and an increasingly aging population, culminating in widespread recognition of ILD as the foremost indication for lung transplant in the US.5,6\n\nRacial and ethnic minority populations face the greatest risk of morbidity and mortality from health disparities and preexisting socioeconomic inequities.7-9 Black patients have high rates of respiratory impairment and more frequent pulmonary involvement with autoimmune disease, are three times as likely to die of obstructive lung diseases like asthma, and have differential survival patterns in ILD when compared with White individuals.10-14 Black race, in particular, has been associated with increased symptomatic burden, frequent hospitalizations, and reduced life expectancy in numerous respiratory diseases.10-13 While these disparities impact factors that span the spectrum from diagnosis to the time of death or lung transplant, poor enrollment of racial and ethnic minority individuals in ILD registries and clinical trials has limited our understanding of the interrelationship between health disparities and racial and ethnic differences in outcomes among patients with PF.\n\nThe primary objective of this study is to test the hypothesis that Black race is associated with an increased incidence of PF diagnosis following hospitalization with COVID-19. The secondary objectives aim to explore if Black race is also linked to increased mortality after hospitalization with COVID-19 and PF diagnosis. These objectives are vital to understanding racial disparities in PF outcomes, particularly in the context of the COVID-19 pandemic.\n", "accessing_institution": "University of Chicago" }, { "uid": "RP-5EADA9", "title": "Knowledge Enhanced Interpret-able Heterogeneous Graph on radiology images, clinical notes and EHR dataset", "task_team": false, "dur_project_id": "DUR-E4C5A0C", "workspace_status": "CLOSED", "lead_investigator": "Qiyuan An", "research_statement": "We will work on constructing a multi-modal heterogeneous graph model to link the current radiology images from medical imaging datasets, clinical notes, lab tests and medicine from the EHR datasets. This constructed heterogeneous graph can be used for cross-modal medical image and text retrieval, precisional lung diseases diagnosis and treatment design.\n\nWe will first train a multi-modal graph model for lung disease diagnosis and interpretation using the chest X-ray images from MIMIC CXR, NIH14 https://arxiv.org/pdf/1705.02315v5.pdf and stanford CXR https://arxiv.org/abs/1901.07031 and EHR dataset from MIMIC EHR https://physionet.org/content/mimiciii/1.4/. Based on the model trained from MIMIC CXR and EHR, we will further develop a transfer learning method to adapt the current model to the N3C dataset for COVID-19 related disease diagnosis using EHR and image dataset if available. We want to study the relationship between the novel COVID-19 to the conventional lung diseases by linking the MIMIC CXR, EHR to the N3C dataset. We will also explore the few-shot and zero-shot learning on diagnosis of novel and rare lung diseases by transferring the model learned from popular lung diseases with large scale datasets. We would like to access both the EHR and images in the N3C enclave database. ", "accessing_institution": "The University of Texas at Arlington" }, { "uid": "RP-14A571", "title": "Association Between 10-Year ASCVD Risk Score And COVID-19 Morbidity and Mortality", "task_team": false, "dur_project_id": "DUR-E4E0D93", "workspace_status": "CLOSED", "lead_investigator": "Rasha Khatib", "research_statement": "The SARS-CoV-2 outbreak is challenging to health care systems due to wide variation in health risk with most patients experiencing few or no symptoms while a minority of patients suffer high complication rates and death. Complications and mortality from SARS-CoV-2 seem to be highest among patients with underlying conditions, including atherosclerotic cardiovascular disease (ASCVD). Patients with prior myocardial infraction (MI), stroke, cardiovascular disease, and coronary artery disease have an increased risk of mortality compared to those without. Less is known about patients at risk of ASCVD (i.e. hypertensive, diabetic, elevated cholesterol levels) who have not yet developed an event. Although, preliminary findings indicate that these patients may be at an increased risk of getting infected and develop complications, including death. We propose to use data from the National COVID Cohort Collaborative (N3C) to evaluate the association between 10-year ASCVD risk scores and COVID complications. ", "accessing_institution": "Advocate Health Care Network" }, { "uid": "RP-0C89CA", "title": "[N3C Operational] Synthetic Data Validation Use Cases ", "task_team": false, "dur_project_id": "DUR-E863B57", "workspace_status": "CLOSED", "lead_investigator": "Randi Foraker", "research_statement": "Description \n1.\tDefine characteristics of the study population\nWe will compare data distributions (e.g., mean, median, range, proportions) of variables of patients (e.g., results of COVID tests, symptoms, demographics, comorbidities, lab results, medications, procedures, hospitalization rates, ICU transfer rates, length of stay, mortality, zip code) \n\n2.\tPredict illness severity\nWe will use a spectrum of methodologies in terms of traditional, biostatistics, machine learning and deep learning approaches to predict the illness severity from the variables listed above plus ventilation and risk scores (i.e., SOFA, APACHE, OHDSI). More features will be gradually fed to the models to increase complexity. More strategies would be applied, for example, by training the models on the synthetic data and testing on the real data, as well as validate the developed models by using data across sites/regions. \n\n3.\tDetermine the geospatial distribution of COVID cases and outcomes\nWe will calculate rate differences by zip code and identify patterns of social determinant of health (SDoH) and relationship with COVID positivity and outcomes. ", "accessing_institution": "Washington University in St. Louis" }, { "uid": "RP-924490", "title": "Comorbidity Comparisons, Including Patients with Immune Dysfunction, Between COVID-19 Negative and COVID-19 Positive Cohorts in N3C", "task_team": false, "dur_project_id": "DUR-EA6BD63", "workspace_status": "ACTIVE", "lead_investigator": "Alfred Anzalone", "research_statement": "This project will compare comorbidity rates across both the COVID-19 negative and COVID-19 positive patients in N3C. The utility\nof this analysis is to determine if comorbidity incidence, which will facilitate more trips to the hospital for routine care and condition management, is indicative of a higher or lower incidence of COVID-19 in the N3C population. Preliminary evidence suggests that patients with comorbidities, particularly those with immunosuppressive and/or low survival over 10 years, may have a lower incidence of COVID-19 due to better adherence to social distancing practices.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-02AA27", "title": "Development of an algorithm for the discrimination of multisystem inflammatory syndrome in children (MIS-C) vs. Kawasaki disease in hospitalized children", "task_team": false, "dur_project_id": "DUR-EB44931", "workspace_status": "CLOSED", "lead_investigator": "Alexander Tang", "research_statement": "The novel inflammatory condition of multisystem inflammatory syndrome in children (MIS-C) has presented clinicians with a significant diagnostic dilemma, with significant therapeutic implications. The primary objective of this study is to design and validate a predictive decision support system for the identification, treatment and management of SARS-CoV-2 associated with MIS-C. To develop this system, we will adapt and retrain machine learning algorithms which we have previously trained in patients with Kawasaki Disease, a pediatric inflammatory vasculopathy with multiple similarities to MIS-C. This study, performed in collaboration with the International Kawasaki Disease Registry (IKDR) consortium, will consist of two phases, first a large-scale data collection and algorithm development effort and second, the prospective evaluation of the performance and clinical utility of the algorithm ahead of large-scale deployment. The initial phase will consist of retrospective review of de-identified data of patients diagnosed and treated for Kawasaki Disease and MIS-C, using data from the IDKR and the National Covid Cohort Collaborative.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-7DA150", "title": "Use, Safety and Effectiveness of Therapies to Treat COVID-19", "task_team": false, "dur_project_id": "DUR-EBC6C0C", "workspace_status": "ACTIVE", "lead_investigator": "Hemalkumar Mehta", "research_statement": "The coronavirus pandemic has generated enormous interest in identifying effective treatments that reduce morbidity and mortality from COVID-19. Since the beginning of the pandemic, important discoveries regarding potential treatments for COVID-19 have been made, ranging from the role of products such as remdesivir and dexamethasone in treating individuals with active infection to the safety of ambulatory use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) among individuals who may be at risk of COVID-19 infection. Despite these insights, innumerable questions remain. In part, this is because while clinical trials typically represent the gold standard method to assess therapeutic efficacy, few trials have been completed and real-world use often differs from the context in which products are studied. In addition, randomized trials are generally designed to quantify efficacy but often do not allow for rigorous assessment of potentially uncommon but clinically important safety concerns. Thus, the importance of the work of the Pharmacoepidemiology Task Team, namely, to evaluate important, empirically testable hypotheses regarding the use, safety and effectiveness of therapies for COVID-19 using a limited data set from the N3C. These data will provide urgently needed information regarding the use, safety and effectiveness of products used among patients with COVID-19. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-BBA785", "title": "Understanding the impact of HIV-infection on COVID-19 ", "task_team": false, "dur_project_id": "DUR-EE007BE", "workspace_status": "CLOSED", "lead_investigator": "Sandra Safo", "research_statement": "Several risk factors have been documented to be associated with severe COVID-19 including comorbidities such as cardiovascular diseases (CVD), chronic respiratory diseases (e.g., chronic obstructive pulmonary diseases [COPD]), and epidemiologic factors such as age, sex, and race. Meanwhile, the role of HIV infection and immunosuppression on the severity of COVID-19 remains unclear. Persons living with HIV (PLWH) oftentimes have CVD and COPD earlier and at rates higher than the general population. In addition, research suggests that inflammatory biomarkers are associated with severity of COVID-19. In PLWH, there is an increased levels of inflammatory biomarkers that are associated with poor outcomes such as death. Our team is requesting access to the Level 2 de-identified data to gain a better understanding of the impact of HIV infection on the severity of COVID-19, and to identify potential individuals and subgroups with increased risk for severe illness. ", "accessing_institution": "University of Minnesota" }, { "uid": "RP-191B3E", "title": "Training/Educational Experience with Synthetic Data", "task_team": false, "dur_project_id": "DUR-30386B2", "workspace_status": "CLOSED", "lead_investigator": "Daniel Harris", "research_statement": "This project tweaks Task 1 for the BARDA pediatric COVID-19 competition. Instead of predicting the need for hospitalization, we simply wish to explore the comorbid factors associated with hospitalization (or lack of hospitalization) for children with positive COVID-19 diagnoses under the age of 18. This project will utilize the synthetic data as it is exploratory and educational in nature.", "accessing_institution": "University of Kentucky" }, { "uid": "RP-F13A9F", "title": "Effect of the SARS-CoV-2 Vaccine on Chronic Liver Disease Progression and Outcomes", "task_team": false, "dur_project_id": "DUR-01B507C", "workspace_status": "ACTIVE", "lead_investigator": "Sarah Weiss", "research_statement": "Coronavirus Disease 19 (COVID-19) is primarily a disease affecting the respiratory system. However, The affinity of the virus for ACE2 receptors and the presence of ACE2 receptors in other body systems such as the liver, heart, skeletal muscle and nervous system has resulted in various non-respiratory manifestations of COVID-19. These non-respiratory manifestations include myocarditis, prolonged myalgia, fatigue, and memory loss. The liver is also vulnerable similarly. Virus particles can multiply within the liver to cause direct cytopathic effects. COVID-19 associated liver injury is now well documented in patients without pre-existing liver disease with elevated Liver Function Tests (LFTs) being reported frequently. Additionally, the cytokine storm associated with severe disease, systemic hypoxia and Drug Induced Liver Injury (DILI) may also contribute to deterioration in liver function. Cholangiopathy has also been reported as a late complication of severe COVID-19.\n\nChronic liver disease (CLD) patients face both increased COVID-19 mortality and reduced immune response to the SARS-CoV-2 vaccine. Patients with established cirrhosis have a 5-fold higher risk of COVID-related mortality, and patients with CLD have a 3-fold higher risk of COVID-related mortality. Through investigating the effect of the SARS-CoV-2 vaccine on CLD progression and outcomes, this study could shed light on the potential long-term impacts of COVID-19 on liver health, particularly among the more vulnerable population of CLD patients.", "accessing_institution": "University of Arizona" }, { "uid": "RP-19A806", "title": "Prevalence and Impact of Long Covid Among 22-35 Year Olds ", "task_team": false, "dur_project_id": "DUR-0347631", "workspace_status": "CLOSED", "lead_investigator": "Riley Nadolny", "research_statement": "Long-Haul Covid or Long Covid (LCVD) is a constellation of persistent symptoms and sequelae in individuals exposed to severe acute respiratory syndrome caused by coronavirus 2, commonly known as COVID-19 (CVD-19), weeks or months following the infection. LCVD affects multiple organ systems impacting function and quality of life in affected individuals. Neurologic symptoms and neurocognitive impairment, such as ?brain fog,? appear to significantly impact long-term performance and functionality. The objective of this study is to identify the prevalence of LCVD in 22-35 year olds, which is a high-risk age group when facing neurocognitive dysfunction. A large percentage of this population requires using higher order thinking because they are enrolled in university or have entered the workforce. This study will focus on the percentage of 22-35 year olds with LCVD, the common presenting symptoms, and the short and long-term impact in this population. To do this, we will look at the percentage of 22-35 year olds that were exposed to CVD-19, then those who reported symptoms of acute CVD-19 followed by LCVD. In this study we will also be looking at descriptive statistics like demographics, risk factors, chronic conditions, documented positive CVD-19 tests, acute and long-term symptoms, the percentage hospitalized, how long they were hospitalized for, mortality rate, treatments given and their outcomes, and the number of times a patient has been diagnosed with CVD-19. ", "accessing_institution": "Nova Southeastern University" }, { "uid": "RP-369293", "title": "Pulmonary - Chronic Lung Disease", "task_team": false, "dur_project_id": "DUR-0419C29", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "Problem statement: \nExacerbations are a common complication of COPD, and guidelines recommend treatment with oral steroids to improve time to recovery and prevent complications. The optimal dose and duration of oral steroids is unclear ? some guidelines1 (such as GOLD) recommend shorter courses (i.e. 5 day course) but others2 (including the ATS/ERS guideline) recommend longer courses up to 14 days. Additionally, several small studies suggest that patients with lower peripheral blood eosinophils may have a lower likelihood of benefit from steroids3,4. Corticosteroids have a potent effect on eosinophils both blood and sputum but can increase neutrophils in both compartments. Cigarette smoking, a common comorbid condition in COPD, is also associated with neutrophilic inflammation. Much of our data about COPD management comes from randomized trials with a few hundred participants. The N3C-Clinical Tenant Pilot is uniquely positioned to address these questions in a real-world cohort larger than any previous randomized trial. Our overarching goal is to examine the relationship between peripheral eosinophilia, smoking status, and steroid duration with real world data using the N3C-Clinical Tenant Pilot. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-57404D", "title": "Case control study on the association between first covid-19 infection, vaccination, and risk of diabetes", "task_team": false, "dur_project_id": "DUR-0452315", "workspace_status": "ACTIVE", "lead_investigator": "Arindam Basu", "research_statement": "It is believed that covid-19 is a risk factor for diabetes, but the extent to which severity of the initial infection and their interaction with vaccination status or subsequent infections, is not clear. The aim of this research is to answer the question if severity of first covid-19 is a risk factor for subsequent development of diabetes post-covid infection? What is the relationship between age, sex, severity of first covid-19 infection, vaccination status, medications, post acute covid symptoms, and subsequent risk of type I and type II diabetes mellitus. In order to answer this question, we will set up a study where we will study equal number of people with and without diabetes from this database. We will obtain the following \"features\" of individuals with and without diabetes: (1) their age, sex, socioeconomic status, date of onset of the first covid-19 infection, total number of covid-19 infections, comorbid conditions (heart diseases, all other diseases that are recorded in the database that are made available), severity of covid-19, vaccination status, medications received, their extent of post-acute covid symptoms (PACS, or long covid), diagnosis of diabetes mellitus, the type of diabetes, date of diagnosis. We will then develop statistical models to find the association between severity of first covid-19 infection and risk of diabetes after adjusting for their age, sex, socioeconomic status, vaccination history, and post acute covid syndromes. This study will enable use to study in depth in other populations the possible risk factors for incident (or new cases of) diabetes following covid-19 infection and possibly enable us to develop public health education or other approaches to address and stratify risks of diabetes for people with at least one bout of Covid-19 based on their severity of initial infection. \n", "accessing_institution": "University of Canterbury" }, { "uid": "RP-AB8693", "title": "Clinical Characterization of Critically-ill COVID-19 Patients based on Ventilation Strategy", "task_team": false, "dur_project_id": "DUR-04612C4", "workspace_status": "CLOSED", "lead_investigator": "Vignesh Subbian", "research_statement": "This project will define and characterize critical care cohorts based on ventilation strategies as well as clinical outcomes on COVID-19 patients that were mechanically ventilated versus those that received non-invasive ventilation therapies. Led by the N3C Critical Care Domain Team, the project will develop and evaluate a phenotyping algorithm to support the characterization of various cohorts based on ventilation therapy. As a feasibility study, the domain team will use de-identified data. ", "accessing_institution": "University of Arizona" }, { "uid": "RP-02ABD6", "title": "Impact of COVID Pandemic on Skin Cancer Treatment Patterns", "task_team": false, "dur_project_id": "DUR-04A7647", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "The COVID-19 pandemic significantly disrupted healthcare access, exacerbating disparities in treatment utilization across different communities. Delays in care due to pandemic related restrictions may have led to shifts in treatment patterns for basal cell carcinoma (BCC) in the use of Mohs surgery versus other interventions. This project aims to evaluate the geographic variability in the utilization of Mohs surgery for BCC of the head by analyzing diagnosis and procedure codes to categorize treatments and comparing intervention rates across zip codes and overtime. This will help to identify communities that may have faced greater barriers to optimal treatment during the pandemic and inform strategies to improve sustainable and equitable access to dermatologic care.", "accessing_institution": "University of Virginia" }, { "uid": "RP-5DC7B2", "title": "Outcomes of IV Anesthetic Agent Administration in Spine Surgery ", "task_team": false, "dur_project_id": "DUR-3C05C6C", "workspace_status": "CLOSED", "lead_investigator": "Comron Saifi", "research_statement": "The present study aims to evaluate the potential role of IV anesthetic agents in spine surgery. ", "accessing_institution": "Houston Methodist Research Institute" }, { "uid": "RP-80661B", "title": "Predicting Long-Term Psychiatric Effects of COVID-19 Infection", "task_team": false, "dur_project_id": "DUR-0725BD3", "workspace_status": "CLOSED", "lead_investigator": "Ben Coleman", "research_statement": "Many have predicted that infection with COVID-19 may result in increased risk for new onset mental illness. However, studies of these risks have found mixed results. We plan to use the N3C cohort to elucidate rates of new onset mental illness following COVID-19 infection compared to patients with a similar respiratory infection. Additionally, we will apply machine learning techniques to try to better predict patients that are more likely to develop a mental illness following COVID-19 infection. This project will function as a use case for the Machine Learning Clinical Domain Team to develop best practices and reproducible procedures for researchers to reapply within the N3C Data Enclave.", "accessing_institution": "The Jackson Laboratory" }, { "uid": "RP-B8D641", "title": "Ideal Approach to Higher Level Oxygenation in COVID19: Early vs. Late Intubation ", "task_team": false, "dur_project_id": "DUR-075A440", "workspace_status": "CLOSED", "lead_investigator": "Emma Nash", "research_statement": "The purpose of the study is provide information to help guide clinical decision making regarding respiratory support for CoVID-19 patients, specifically the effects of treating patients with high levels of non-invasive respiratory support (> 6L supplemental oxygen) vs early intubation (< 72 hours). This is an observational retrospective cohort study to determine the potential benefit of avoiding endotracheal intubation in CoVID-19 patients. Information used pertaining to the primary outcome of interest includes rate of intubation of those trialed on non-invasive methods, overall time on ventilator support, and length of stay in ICU. ", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-840DC4", "title": "Outcomes of Orthopaedic Procedures in COVID Patients", "task_team": false, "dur_project_id": "DUR-0900A64", "workspace_status": "ACTIVE", "lead_investigator": "Justin Chan", "research_statement": "The purpose of this study is to determine whether COVID infection status or history is correlated with different incidence of outcomes and perioperative complications after orthopaedic procedures", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-8B3D84", "title": "Outcomes of COVID-19 in Patients with Diabetes: Report from the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-0938D2D", "workspace_status": "ACTIVE", "lead_investigator": "Youping Deng", "research_statement": "This Research Project expect to address three questions. First, although some studies report\nCOVID-19 patients with diabetes suffer from higher risk of mortality than those without\ndiabetes, our research aims to get precise harmfulness of diabetes for COVID-19 patients. The\nresearch will figure out the disease severity level and death rate of patients with different types\nof diabetes (type 1 diabetes, type 2 diabetes, and Gestational diabetes) and different kinds of\nSARS-CoV-2 variants (Alpha, Beat, and Delta, etc). In addition, research project will address for\npatients with both COVID-19 and diabetes if different kinds of medicine and vaccination\nassociated with clinical outcomes. In order to address the listed questions, we require to access\nDe-identified Data (Level 2) from the National COVID Cohort Collaborative (N3C). The\ninformation of demographics, symptoms, comorbidities, laboratory tests,\ntreatments, vaccination, disease progression, and clinical outcomes of COVID-19 are\nessential for this Research Project.", "accessing_institution": "University of Hawaii System" }, { "uid": "RP-950CEB", "title": "Testing biomarkers and clinical associations", "task_team": false, "dur_project_id": "DUR-0B3D936", "workspace_status": "CLOSED", "lead_investigator": "Keith Crandall", "research_statement": "In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. The heterogeneous data poses a challenge for efficient extrapolation of information into clinical applications. We have rapidly collated 145 COVID-19 biomarkers through crowdsourcing efforts by leveraging a robust data model developed to capture cancer biomarker data. Our expanding resource has comprehensive data from multiple structured ontology databases. Most biomarkers are related to the immune (SAA, TNF-?, and IP-10) or coagulation (D-dimer, antithrombin, and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR, LDL). Given our progress in establishing a COVID-19 biomarker knowledgebase, we are now in a position to validate proposed associations between biomarkers and clinical outcomes with the exceptional N3C data enclave. We propose to test our biomarker associations using insights from our database for biomarker selection, but focusing solely on N3C data to test for associations of biomarkers with clinical outcomes. All analyses will be conducted within the N3C enclave. No data will be imported or exported. Our results will provide further evidence to support (or refute) biomarker association with clinical outcomes, leading to better application of biomarkers in clinical settings.", "accessing_institution": "George Washington University" }, { "uid": "RP-835D6B", "title": "The relationship between COVID-19 susceptibility, severity and mortality and Barrett?s Esophagus: a National COVID Cohort Collaborative (N3C) Study", "task_team": false, "dur_project_id": "DUR-0C5A382", "workspace_status": "CLOSED", "lead_investigator": "Bing Chen", "research_statement": "In this application, we outlined innovative proposals to investigate the relationship between COVID-19 susceptibility, severity and mortality and Barrett?s Esophagus. We will use the NCATS N3C Data Enclave to compare the incidence of COVID-19 infection, and its severity and mortality in Barrett?s Esophagus patients with that in non-Barrett?s Esophagus individuals. We need the variables to show whether patients have Barrett?s Esophagus, along with associated variables, such as therapy of Barrett?s Esophagus and stage of Barrett?s Esophagus, if available. We also need the variables to show if COVID-19 positive and COVID-19 related factors. Patients? age, gender, comorbidities, and other epidemiological information will also be needed to control confounding.", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-08D632", "title": "Maternal telehealth uptake during the COVID-19 pandemic and health disparities", "task_team": false, "dur_project_id": "DUR-0D13461", "workspace_status": "ACTIVE", "lead_investigator": "Peiyin Hung", "research_statement": "The Coronavirus disease 2019 (COVID-19) pandemic has inflicted severe social, healthcare, and economic devastation in the United States (US), potentially exacerbating existing maternal health disparities faced by rural birthing individuals and pregnant people of color. In this context, telehealth has emerged as a promising avenue to address these disparities in maternal health access, quality, and outcomes during the prenatal and postpartum periods. Telehealth encompasses various modalities, such as audiovisual synchronous and asynchronous encounters between patients and healthcare providers, remote patient monitoring, facilitating visual communication of evidence-based practices, and supporting clinical decision-making. Yet, multilevel barriers might hinder some underserved women from fully benefiting from telehealth. Expanded federal and state-level telehealth coverage through the Coronavirus Aid, Relief, and Economic Security (CARES) Act and state policies may be mitigating the detrimental effects of this unprecedented pandemic by reducing gaps in access to telehealth and quality maternity care. Although self-reported data indicated abrupt increases in telehealth uptake during the pandemic, limited real-world data are available regarding the role of perinatal telehealth uptake on the pandemic?s effects and the role of state-level policy in telehealth adaptation during COVID-19. The anticipated outcome of this study is to provide valuable insights into the effects of incorporating maternal telehealth services during pregnancy or in the immediate postpartum period for pregnant or birthing individuals. Through the exploration of these associations and the development of long-term predictive models, this study aims to generate compelling evidence that supports the implementation of telehealth care at both clinical and state policy levels. By understanding the timing of prenatal and postpartum care with and without maternal telehealth uptake, the findings have the potential to inform decision-makers, healthcare providers, and policymakers about the benefits and effectiveness of telehealth in improving maternal health outcomes and reducing disparities. By expanding our understanding of the impacts of telehealth in the perinatal period during and beyond the COVID-19 pandemic, this research can contribute to the development of evidence-based strategies to enhance access, quality, and equity in maternity care.", "accessing_institution": "University of South Carolina" }, { "uid": "RP-997772", "title": "Severity and Mortality Prediction for COVID-19 patients", "task_team": false, "dur_project_id": "DUR-0D80D99", "workspace_status": "CLOSED", "lead_investigator": "Ran Xu", "research_statement": "We propose to develop a deep learning model to learn the relationship among multiple health condition indicators, and severity and mortality, and in the end predicts the severity and mortality of each patient based on their symptoms, vital signs and etc., so that we can reasonably allocate medical resources to most urgent patients. Moreover, if the model could get high accuracy and could be possibly available to most people, then people could evaluate their health condition themselves and take actions accordingly.", "accessing_institution": "Emory University" }, { "uid": "RP-A33085", "title": "COVID-19 outcomes and health care utilizations for patients with opioid use disorders", "task_team": false, "dur_project_id": "DUR-0D94067", "workspace_status": "ACTIVE", "lead_investigator": "Hyojung Kang", "research_statement": "COVID-19 has negatively affected opioid-related overdoses and deaths. Opioid use affects respiratory and pulmonary health, which may increase the risk of adverse outcomes from COVID-19. This study aims to examine COVID-19 outcomes and health care utilizations for populations with opioid use disorders using a large, nationwide dataset. We will develop various analytical models that identify risk factors for poor COVID-19 related outcomes. ", "accessing_institution": "University of Illinois at Urbana Champaign" }, { "uid": "RP-3B724E", "title": "Protective Effects of Medications Against SARS-CoV-2 Infection in Patients with Existing Prescriptions", "task_team": false, "dur_project_id": "DUR-0DBA0D4", "workspace_status": "ACTIVE", "lead_investigator": "Steve Johnson", "research_statement": "A number of commonly used prescription medications have been identified as having potential activity against SARS-CoV-2. It is not practical to perform randomized controlled trials to evaluate the effectiveness of all of these medications. Therefore, we will assess the potential protective effects of these medications using observational data from patients who are taking these medications to see if they subsequently become infected with SARS-CoV-2 or develop COVID-19 symptoms at a different rate compared to controls. ", "accessing_institution": "University of Minnesota" }, { "uid": "RP-5777C9", "title": "Interventions and outcomes in COVID-19 patients: A National COVID Cohort Collaborative (N3C) study.", "task_team": false, "dur_project_id": "DUR-0E11439", "workspace_status": "ACTIVE", "lead_investigator": "Karthik Raghunathan", "research_statement": "It has been over a year since COVID was first detected, which has now become a soaring pandemic with no visible end. Health systems across the globe have suffered massively during the outbreak. Even after the persistent increase in cases, COVID vaccines are expected to be life-changing. Most of the surgical and acute care during the pandemic era is on the guidelines presented by expert clinicians and organizations. However, there is a need to understand the best practices and outcomes among the COVID-19 patients who had procedures to guide clinical decisions better. Conventionally, critically ill patients who are on mechanical ventilation are weaned off the ventilator as early as possible. There is compelling evidence in favor of the early tracheotomy following mechanical ventilation and its association with lower adverse outcomes. However, due to the concern of super-spreading incidents and higher morbidity and mortality among COVID patients, the experts recommended late tracheotomy after waiting for at least 2-3 weeks, despite the known higher rate of complications with a late tracheostomy. The primary objective of the study is to analyze differences in utilization of mechanical ventilation and timing to tracheostomy for Acute Respiratory Distress Syndrome (ARDS) in COVID-19 and non-COVID patients during the current pandemic. \n\n", "accessing_institution": "Duke University" }, { "uid": "RP-919499", "title": "Predicting COVID-19 long term sequelae. ", "task_team": false, "dur_project_id": "DUR-0E91560", "workspace_status": "CLOSED", "lead_investigator": "Diana Perkins", "research_statement": "COVID-19 infection is associated with potentially disabling long-term sequelae including cognitive impairments, fatigue, exercise-intolerance, and psychotic disorders. We propose to investigate predictors of emergence of these disorders.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-D7E3D9", "title": "Multimorbidity in COVID-19 using observational EHR data", "task_team": false, "dur_project_id": "DUR-1085026", "workspace_status": "ACTIVE", "lead_investigator": "Melissa Wei", "research_statement": "Multimorbidity is a risk factor for acute illness including influenza, pneumonia, and severe and fatal COVID. Vaccines are vital and have reduced COVID deaths by 63% worldwide. However, the ACIP condition list used by the CDC for COVID vaccine recommendations has not been rigorously evaluated. We will develop a novel EHR-based multimorbidity-weighted index (eMWI) and, using level 2 de-identified patient data linked with vaccine records, test the ability of eMWI to predict severe and fatal COVID in vaccinated vs. unvaccinated adults. We will compare the predictive ability of eMWI against current measures (ACIP condition list, Charlson comorbidity index, Elixhauser comorbidity score, disease count), as well as evaluating its performance after removing conditions in the most recent ACIP-designated condition list (eg: diabetes, asthma).", "accessing_institution": "University of California, Los Angeles" }, { "uid": "RP-98D994", "title": "Descriptive analysis in synthetic data of the use of drug treatments for patients with evidence of Post-Acute Sequelae of SARS-CoV-2 infection (PASC)", "task_team": false, "dur_project_id": "DUR-11BB39E", "workspace_status": "CLOSED", "lead_investigator": "Richard Boyce", "research_statement": "As described recently by Dr. Francis Collins \"Large numbers of patients who have been infected with SARS-CoV-2 continue to experience a constellation of symptoms long past the time that they?ve recovered from the initial stages of COVID-19 illness. Often referred to as ?Long COVID?, these symptoms, which can include fatigue, shortness of breath, ?brain fog?, sleep disorders, fevers, gastrointestinal symptoms, anxiety, and depression, can persist for months and can range from mild to incapacitating. In some cases, new symptoms arise well after the time of infection or evolve over time.\" It is currently unknown drug treatments pathways patients with Long Covid (aka of Post-Acute Sequelae of SARS-CoV-2 infection (PASC)) receive. We will apply existing EHR phenotypes for PASC and conduct a descriptive analysis in synthetic data of the use of drug treatments for patients with evidence of PASC as a first step to developing a more rigorous analysis on Safe Harbor of the Limited Dataset.", "accessing_institution": "University of Pittsburgh" }, { "uid": "RP-857A32", "title": "Risk factors relating to physical impairment", "task_team": false, "dur_project_id": "DUR-12B8326", "workspace_status": "CLOSED", "lead_investigator": "Seibi Kobara", "research_statement": "We aim to identify risk factors associated with physical impairments.", "accessing_institution": "Emory University" }, { "uid": "RP-27316A", "title": "Developing Mental Health Risk Model During Pandemic Related Outcomes from Large EHR Data", "task_team": false, "dur_project_id": "DUR-132C04A", "workspace_status": "ACTIVE", "lead_investigator": "Mohammad Arif Ul Alam", "research_statement": "There is a very high psychological health breakdown rate during pandemic which can be extracted from medical records. Intensive postdischarge case management programs can address the problem of post psychiatric hospital discharge but are not cost effective for all patients. This problem can be addressed by developing a mental breakdown risk model to predict which inpatients might need such a program. In this project, we aim to develop such mental breakdown risk model from short-term psychiatric hospital admission between early 2020 and till date. However, developing such model has many challenges including vector representation of medical objects from large scale EHR data which requires deep and causal learning. In this project, we aim to develop such deep causal vector representation of medical objects derived from EHR data, potentially with minimal human supervision via restricted Boltzmann machine (RBM) of causality.", "accessing_institution": "University of Maryland, Baltimore County" }, { "uid": "RP-E5B169", "title": "Vaccination and COVID-19", "task_team": false, "dur_project_id": "DUR-0CE3581", "workspace_status": "CLOSED", "lead_investigator": "NASIA SAFDAR", "research_statement": "This project aims to define and characterize patients with regard to vaccination status and its association with covid-19 with and without long-term sequelae of SARS-CoV-2 infection using the N3C Limited Data Set. There is an urgent need to examine the comparative effectiveness of various vaccines against covid, to help guide resource allocation and improve patient outcomes.", "accessing_institution": "University of Wisconsin?Madison" }, { "uid": "RP-E5590B", "title": "Estimation of Circadian Variation in Vital Signs and Symptoms of COVID-19, With a Focus on Body Temperature and Fever", "task_team": false, "dur_project_id": "DUR-136574D", "workspace_status": "CLOSED", "lead_investigator": "Charles Harding", "research_statement": "As part of the circadian rhythm, body temperature and other vital signs follow diurnal cycles. These diurnal cycles occur in both health and most diseases, and are also associated with time-of-day variation in disease symptoms. The consequences of diurnal variation have been analyzed for fever detection, both in general (Samples et al. Nurs Res 1985;34:377?9; Mackowiak et al. JAMA 1992;268:1578?80; Harding et al. West J Emerg Med 2020;21:909-17) and specifically using data from seasonal and pandemic influenza outbreaks (Harding et al. medRxiv. May 2020, doi:10.1101/2020.05.23.20093484). The aim of the current project is to estimate the diurnal variation of vital signs and symptoms in COVID-19, with a focus on body temperature and fever. Results are intended to provide evidence for best practices in symptom screening, symptom-based testing referral, and symptom monitoring during quarantine and isolation. Analyses may also include comparison between patients with COVID-19, influenza, and neither. Analyses will be performed using the De-Identified Data Set if feasible, and another data access level may be pursued otherwise.", "accessing_institution": "Harding Research" }, { "uid": "RP-2DA60E", "title": "A Causal Inference Framework to Translate COVID-19 Observational Data to New Knowledge", "task_team": false, "dur_project_id": "DUR-140E37D", "workspace_status": "ACTIVE", "lead_investigator": "Md Osman Gani", "research_statement": "The COVID-19 pandemic has placed tremendous pressure on clinical research and innovation. There is an urgent need to understand, prevent and treat COVID-19 disease. It has threatened the traditional models of knowledge translation and practice in treating patients. Small-scale clinical trials and cohort studies are reporting anecdotal evidence claiming contradictory therapeutic successes. On the other hand, large-scale trials are time-consuming, costly, and sometimes infeasible. The National Institute of Health (NIH) has made COVID-19 patient (observational) data available to researchers for faster translation to new therapies and knowledge to address the pandemic and prepare for other diseases in the future. The advances in causal inference, particularly Structural Causal Models (SCM) can help translate this data to knowledge. The goal of this proposed research is to develop a framework for estimation of treatment effect by modeling unobserved confounding in SCMs that specifically address the practical challenges of performing virtual experiments using COVID-19 patient data. The clinical application entails a timely and important research question, the effect of oxygen therapy on mortality in COVID-19 patients in the ICU. We will work closely with domain experts, critical care physicians, to incorporate evidence-based practices, recommendations and to validate the causal structure. The methods will help practitioners in the area of artificial intelligence, machine learning, data science, and healthcare to leverage the vast amount of COVID-19 data collected from multiple sources to extract knowledge and meaningful conclusions.", "accessing_institution": "University of Maryland, Baltimore County" }, { "uid": "RP-68DB83", "title": "National COVID Cohort Collaborative (N3C) Clinical Characterization for Kidney Disease and Hypertension", "task_team": false, "dur_project_id": "DUR-1458EE0", "workspace_status": "ACTIVE", "lead_investigator": "Farrukh Koraishy", "research_statement": "The purpose of this project is to characterize kidney disease (including acute kidney injury [AKI], chronic kidney disease [CKD and End-Stage Renal Disease [ESRD]) and hypertension in patients with COVID-19. We will study the association of patient data (including socio-demographics, comorbidities, medications, laboratory values and other investigations) with outcomes (including AKI, renal recovery, mechanical ventilation, and death) in COVID-19 patients. For all analyses, we will conduct studies to compare outcomes in patients with COVID-19 with those who tested negative for COVID-19 (control group).\nSpecific Aims:\n1. Determine the incidence/prevalence of AKI, AKI severity, renal recovery and incident/progressive CKD in patients with and without COVID-19 disease. \n2. Determine the factors association with AKI, AKI severity, renal recovery and incident/progressive CKD in patients with and without COVID-19 disease.\n3. Determine the short and long-term outcomes associated with AKI, AKI severity, renal recovery and incident/progressive CKD in patients with and without COVID-19 disease\n4. Determine the outcomes associated with ESRD in patients with and without COVID-19 disease\n5. Determine the outcomes associated with hypertension and anti-hypertensive medications in patients with and without COVID-19 disease\n", "accessing_institution": "Stony Brook University" }, { "uid": "RP-E19D5D", "title": "Neurosurgical procedure outcomes for COVID-19 positive patients", "task_team": false, "dur_project_id": "DUR-1591126", "workspace_status": "CLOSED", "lead_investigator": "Ashley Selner", "research_statement": "This project aims to assess the prevalence of neurosurgical interventions in patients with COVID-19 and their outcomes. The patient sample will consist of COVID-19 positive individuals in the N3C database reported to have underwent neurosurgical procedures. A control cohort of patients without COVID-19 undergoing neurosurgical procedures within the same time frame will be assessed. Demographics, history, comorbidities, imaging, laboratory, treatment, complications and disposition of de-identified patients will be reviewed in order to evaluate factors affecting clinical outcomes. Subgroup analyses for specific neurosurgical procedures such as external ventricular drain placement, craniotomy, spine and endovascular will be performed.", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-9549CE", "title": "Analysis of factors associated with survival from SARS-CoV-2 infections", "task_team": false, "dur_project_id": "DUR-163E57E", "workspace_status": "CLOSED", "lead_investigator": "Thomas Hartka", "research_statement": "This project will demographics and treatments associate with survival from SARS-CoV-2 infections. Demographics will include factors such as age, sex, comorbidity, and ethnicity. Treatment factors will focus on medications which have been shown in clinical trials to improve mortality.", "accessing_institution": "University of Virginia" }, { "uid": "RP-1225E6", "title": "Effects of drugs on COVID-19 trajectory", "task_team": false, "dur_project_id": "DUR-1718141", "workspace_status": "ACTIVE", "lead_investigator": "rachel melamed", "research_statement": "We propose that drugs taken for other purposes may alter the susceptibility to severe disease. We will apply causal inference methods to identify drugs that impact clinically observable COVID19 development. Our project will apply a number of machine learning methods in order to identify these effects, accounting for patient?s health states at the time that they received a particular medication. We will develop a systematic framework to evaluate the robustness of our method, using knowledge bases relating drugs and diseases to propose negative control relationships. ", "accessing_institution": "University of Massachusetts, Lowell" }, { "uid": "RP-4DF222", "title": "Associations between HIV infection and clinical outcomes of COVID-19 during the Omicron era", "task_team": false, "dur_project_id": "DUR-1742450", "workspace_status": "CLOSED", "lead_investigator": "Zhen Lu", "research_statement": "Existing evidence of the association between HIV infection and COVID-19 outcomes is based on data prior to the Omicron wave and demonstrates contradictory findings. Study including larger individual patient-level data amidst the ongoing Omicron wave is required to further understand severe and fatal clinical outcomes following hospitalization with COVID-19, and Omicron versus pre-Omicron era differences for people living with and without HIV. This study aims to use N3C enclave data to perform a large-scale retrospective observational study to characterize clinical outcome in COVID-19 patients with/without HIV infection during the Omicron versus pre-Omicron eras. Hence, our team is applying for access to De-identified Data set (Level 2) to gain a further understanding of how HIV infection affect COVID-19 outcomes during the Omicron versus pre-Omicron eras.", "accessing_institution": "Sun Yat-sen University" }, { "uid": "RP-C75889", "title": "Social determinants of health and health outcomes during COVID-19 pandemic", "task_team": false, "dur_project_id": "DUR-18D6B28", "workspace_status": "ACTIVE", "lead_investigator": "Xueying Yang", "research_statement": "The COVID-19 pandemic continues to affect the economy and well-being of people around the world. Historically, pandemics of infectious disease often disproportionately affect the poor and disadvantaged. Since the COVID-19 pandemic, millions have lost jobs or income, making it difficult to pay expenses including basic needs like food and housing. These challenges will ultimately affect people?s health and well-being, as they influence social determinants of health (SDOH). SDOH are the conditions in which people are born, grow, live, work, and age. They include factors like socioeconomic status, education, neighborhood, and physical environment, employment, and social support networks, as well as access to health care. Addressing social determinants of health is important for improving health and reducing longstanding disparities in health and health care. There are a growing number of initiatives to address SDOH during the COVID-19 pandemic. However, most existing studies explored the impact of individual SDOH indicators in the context of the COVID-19 pandemic, rather than systematically quantify the contribution of SDOH on worse COVID-19 outcomes. While the relationships between these variables need elucidating, this study aims to use N3C enclave data to perform a large-scale retrospective observational study to characterize SDOH in clinical settings across the US and explore the impact of SDOH on health outcomes. Our team is requesting access to level 2 de-identified data and level 3 Limited Data set (LDS) for data analysis.\n\n", "accessing_institution": "University of South Carolina" }, { "uid": "RP-53741A", "title": "Evaluating the Effect SARS-CoV2 has on the Incidence of Pre-eclampsia and Related Laboratory Biomarkers", "task_team": false, "dur_project_id": "DUR-1946863", "workspace_status": "CLOSED", "lead_investigator": "Mallory Isham", "research_statement": "Pre-eclampsia is a leading cause of maternal death and morbidity alone, but pre-eclampsia along with SARS-CoV2 infection increases the chances for pregnant women needing intensive care (Pirjani et al., 2020). Although their etiologies are different, both pre-eclampsia and SARS-CoV2 cause similar systemic problems, such as hypertension and damage to the liver, kidneys, and vascular endothelium (Naeh et al., 2021). Some research has already been performed regarding how SARS-CoV2 infection affects pre-eclamptic women, including maternal and neonatal complications; one study in particular presents evidence of a dose-response relationship between SARS-CoV2 infection and the incidence of pre-eclampsia (Lai et al., 2021). Research has also shown evidence of pre-eclampsia and SARS-CoV2 affecting the same laboratory biomarkers, as both conditions affect similar organs (Akbas & Koyuncu, 2020). More research needs to be performed regarding pre-eclampsia and SARS-CoV2 to answer the question: does SARS-CoV2 infection have an effect on the incidence of pre-eclampsia in pregnant women? Another question that needs researching is: does SARS-CoV2 infection severity have an effect on laboratory biomarkers related to both pre-eclampsia and SARS-CoV2? This research project will demonstrate the effect SARS-CoV2 infection has on the incidence of pre-eclampsia in pregnant women and compare laboratory biomarkers in pre-eclamptic women with differing severities of SARS-CoV2 infection. The hypothesis of this study is that there is a significant difference in the incidence of pre-eclampsia and laboratory results of women who develop pre-eclampsia based on the severity of SARS-CoV-2 infection. Results from this study will help clinicians to better understand how SARS-CoV2 infection affects the risk for developing pre-eclampsia in pregnant women and will give them a better understanding of the clinical picture of pre-eclampsia with SARS-CoV2 infection as it relates to laboratory findings. This study will utilize de-identified data (Level 2) for data collection and analyses.", "accessing_institution": "The University of Texas Medical Branch at Galveston" }, { "uid": "RP-F47D09", "title": "Investigating the link between olfactory dysfunction and cognitive decline in COVID-19 patients.", "task_team": false, "dur_project_id": "DUR-1BD1C5B", "workspace_status": "ACTIVE", "lead_investigator": "Valentina Bermudez", "research_statement": " A large number of genes related to olfactory functions have been found in genomic regions located near SNPs associated with Alzheimer's Disease and related dementias. The purpose of this current research project is to look further into the relationship between loss of olfaction and cognitive decline, including memory loss. With the emergence of COVID-19 numerous observations were made to unveil the virus? symptoms and their effects. One of the known symptoms of COVID-19 is the loss of smell, which can differ from person to person but was reported by many. This research project aims to use the N3C database to explore the possible relationship between cognitive impairment and loss of olfactory function. This will potentially be done by looking at the data separating patients who experienced loss of smell to those who didn't, and seeing which patients suffered cognitive decline along with olfaction dysfunction. Will there be a significant relationship between the two, or is cognitive decline another side effect of COVID-19 separate from olfactory dysfunction? ", "accessing_institution": "Case Western Reserve University" }, { "uid": "RP-EDA1E8", "title": "Developing an application to predict the severity of COVID-19 infections of individual patients by applying artificial intelligence methodologies", "task_team": false, "dur_project_id": "DUR-1C11D26", "workspace_status": "CLOSED", "lead_investigator": "Kathryn Monopoli", "research_statement": "COVID19 is perplexing in how it impacts different individuals with drastic differences in severity. Understanding the how different individuals vary in susceptibility is essential to treating individuals efficiently and to identifying those at greater risk to minimize the impacts of the disease. We will develop an application applying artificial intelligence to predict the severity of COVID-19 symptoms a patient will experience based on the patient's presenting condition. Our study will utilize lab values from urine and blood tests from both patients who tested positive for COVID-19 and those who did not along with information about symptom severity for COVID-19 positive patients. Lab values will be used for analysis as these are standardized, rapid, low-cost diagnostic tests that provide great insight into a patient?s current health state and have been proven valuable in predicting outcomes in a broad range of diseases. This application could be applied to recommend treatment course for a patient to enable healthcare providers to make decisions quickly and allocate limited resources efficiently. This study will elucidate the complexities of COVID19 symptom presentation and, further, will aid in identifying individuals likely to be asymptomatic carriers. ", "accessing_institution": "University of Massachusetts Medical School" }, { "uid": "RP-76A3B9", "title": "Hospitalization Rates Feb-April vs May-October", "task_team": false, "dur_project_id": "DUR-1C5BA5D", "workspace_status": "CLOSED", "lead_investigator": "Jeremy Harper", "research_statement": "Looking at the rates of hospitalization between early C19 and after C19 disease progression was better understood.", "accessing_institution": "Indiana University" }, { "uid": "RP-E72986", "title": "How HIV infection shape the healthcare utilization and disease severity of COVID-19 ", "task_team": false, "dur_project_id": "DUR-1C9D985", "workspace_status": "ACTIVE", "lead_investigator": "Xueying Yang", "research_statement": "The World Health Organization (WHO) and Center for Disease Control and Prevention (CDC) have issued health alerts and prevention guidelines for people at increased risk for severe health outcomes and death due to COVID-19. Chronic comorbidities appear to be driving factors for COVID-19 mortality. Although evidence to date does not suggest that people living with HIV (PLWH) have a markedly higher susceptibility to SARS-CoV-2 infection, chronic conditions are more prevalent in people living with HIV than non-infected individuals. Therefore, on top of the other common comorbidities (e.g., asthma, chronic lung disease, diabetes, cardiovascular conditions, chronic kidney disease, obesity), warnings to take extra precautions should also include persons who are immunocompromised, such as PLWH. Existing literature that investigated the health outcomes of COVID-19 patients who are co-infected with HIV are very limited, with the available data mainly appear in case reports and case series of COVID-19-HIV co-infected patients, and almost all are small-scale studies with findings inconclusive. This study aims to use N3C enclave data to perform a large scale retrospective observational study to characterize outcomes in COVID-19 patients with/without HIV infection. Our team is requesting access to level 2 de-identified data to gain a better understanding of how HIV infection affect COVID-19 outcomes.", "accessing_institution": "University of South Carolina" }, { "uid": "RP-DC3214", "title": "External cohort validation of the ARC score for COVID-19 28-day mortality and escalation in O2 therapy in hospitalized patients", "task_team": false, "dur_project_id": "DUR-1EB26DA", "workspace_status": "ACTIVE", "lead_investigator": "Benjamin Sines", "research_statement": "Purpose: Validation of an internally derived clinical risk prediction score for inpatients with COVID-19 pneumonia\nParticipants: Patients age 18 and older enrolled in the N3C database admitted to the hospital with COVID-19 pneumonia.\nProcedures (methods): Adult patients (age 18 and older) who are hospitalized with COVID-19 pneumonia will be selected from the broader N3C database. This population will be analyzed based on demographic data, medical co-morbidities, date of admission and location with the outcomes of 28-day mortality (primary end-point) and escalation of oxygen therapy (secondary end-point). The cohort will be used to validate the ARC score (Age, neutrophil to lymphocyte Ratio, and CRP on day 3) (preliminary data from the development cohort to be presented at ATS 2022).\nLogistic regression analysis will be used to assess this clinical prediction model in the validation cohort. Each participant from the N3C cohort will then be scored on the derivated ARC score. The primary outcome of interest is 28-day mortality. Secondary outcomes analyzed will be escalation in O2 therapy along ordinal scale of room air, nasal cannula, high flow nasal cannula or non-invasive ventilation, invasive mechanical ventilation, ECMO. Area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness of fit statistic will be used to assess model calibration and fit in the validation cohort.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-D84AE1", "title": "Predicting Long-Term Kidney Disease Effects of COVID-19 Infection", "task_team": false, "dur_project_id": "DUR-22361BD", "workspace_status": "ACTIVE", "lead_investigator": "Yongqun He", "research_statement": "The infection with COVID-19 may result in increased risk for kidney diseases. For example, Acute kidney injury (AKI) is a significant complication of COVID-19. The incidence of AKI in hospitalized patients varies from 0.5% to 75%. The mortality rate for patients with kidney disease is also significantly higher than the general infected population. However, the big variation of AKI incidence in COVID-19 patients appears to depend on many factors such as race, region, and disease severity. We plan to use the N3C cohort to detect, compare, and analyze the occurrences of kidney disease following COVID-19 infection. We will apply machine learning techniques to try to better predict patients that are more likely to develop a kidney disease following COVID-19 infection. This project will function as a use case for the Machine Learning Clinical Domain Team to develop best practices and reproducible procedures for researchers to reapply within the N3C Data Enclave. ", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-E466A5", "title": "Exploring health trajectories of sustained COVID-19-related Renal Injuries (Sus-CovRI) using deep representation of heterogeneous N3C EHR", "task_team": false, "dur_project_id": "DUR-22687DA", "workspace_status": "ACTIVE", "lead_investigator": "Jing Su", "research_statement": "The N3C electronic health records (EHR) provide numerus opportunities in understanding the nature and broad impact of COVID-19 on kidney health of COVID survivors, learning actionable knowledge about both acute and chronic COVID-related renal injury, and develop strategies and solutions to manage Sus-CovRI. However, the heterogeneous nature of EHR data impedes the meaningful and effective use of EHRs in clinical research. To address this challenge, we propose to delineate the renal health trajectories of COVID survivors by developing comprehensive and interpretable deep representations of the raw EHR data using artificial intelligence models. We will focus on the progressions of post-COVID renal injuries in the context of major comorbidities and risk factors including hypertension, diabetes, pulmonary conditions, cardiovascular diseases, cancers, substance abuse, COVID severity, and others. Our work will establish major trajectory patterns of sustained renal injury related with SARS-CoV-2 infection, build predictive models of kidney trajectories for precise management, and provide ready-to-use ?meta-EHR? datasets for N3C domain teams to investigate clinical and biomedical questions and empower the collective endeavors of N3C Consortium. ", "accessing_institution": "Indiana University" }, { "uid": "RP-2218B9", "title": "Association of Dupilumab with Protection from COVID-19 Respiratory Failure", "task_team": false, "dur_project_id": "DUR-24BD9B1", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "We hypothesize that IL-13 is a cause of acute hypoxic respiratory failure in COVID-19 by inducing type 2 inflammation in the lung. This study will explore Dupilumab rates of use and outcomes in COVID positive patients. This is a feasibility study using de-identified data. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-6EBE3C", "title": "[N3Clinical: Operational] Tenant Data Ingestion and Harmonization", "task_team": false, "dur_project_id": "DUR-25BEA42", "workspace_status": "ACTIVE", "lead_investigator": "Christopher Chute", "research_statement": "The [N3C Operational] Tenant Data Ingestion and Harmonization DUR serves a similar function to the N3C DH&I Operations DUR however it is restricted to people involved in the NCATS Tenant initiative and will be responsible for data harmonization, quality, and curation including but not limited to extract, transform, and load data from contributing sites into a common format, as well as phenotype testing and development, score card development.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-AFDD64", "title": "Otologic Sequelae of COVID-19 Infection", "task_team": false, "dur_project_id": "DUR-2633237", "workspace_status": "ACTIVE", "lead_investigator": "Shelley Batts", "research_statement": "Hearing loss, tinnitus, dizziness, and other otologic symptoms have been reported in the literature as complications following COVID-19 infection. The purpose of our study is to examine otologic symptoms that are newly arising after or exacerbated by COVID-19 infection to understand how common these symptoms are, who experiences them, and if and when they resolve post-infection. ", "accessing_institution": "Stanford University" }, { "uid": "RP-B85858", "title": "Ritonavir-Boosted Nirmatrelvir (Paxlovid) and Blood Pressure Change", "task_team": false, "dur_project_id": "DUR-2683A87", "workspace_status": "ACTIVE", "lead_investigator": "Bingya Ma", "research_statement": "This project aims to explore the blood pressure change associated with the use of Ritonavir-Boosted Nirmatrelvir.", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-7BE1AC", "title": "A machine learning strategy for identifying drugs that affect COVID-19 outcome", "task_team": false, "dur_project_id": "DUR-28D42B2", "workspace_status": "CLOSED", "lead_investigator": "Justin Reese", "research_statement": "We have constructed a COVID-19 knowledge graph and developed a graph machine learning method for identifying drugs that affect COVID-19 outcome. This effort has yielded a short list of drugs that warrant further investigation using clinical data. We propose to use the N3C cohort in a retrospective case-cohort study to identify drugs on our shortlist that have a statistically significant effect on COVID-19 outcome, after controlling for comorbidities and other confounding factors. ", "accessing_institution": "Lawrence Berkeley National Laboratory" }, { "uid": "RP-F51E80", "title": "Developing Informatics System for COVID-19 Stratification and Biomarker Discovery", "task_team": false, "dur_project_id": "DUR-417B4DA", "workspace_status": "CLOSED", "lead_investigator": "Yanfei Wang", "research_statement": "Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2, causing a huge economic and social burden on society. Although many effects have been devoted, the pathology of COVID-19 disease is still unclear, causing great challenge to medicine development. Meanwhile, some patients suffer some symptom after six months of recovering from the acute illness. The overall prevalence of post-acute sequelae of SARS-CoV-2 (PASC), also known as Long COVID, is currently unknown. Therefore, in this project we intend to use EHR de-identified data including demographics, symptoms, lab test results, procedures, medications/drugs, medical conditions, physical measurements to 1) assess the dynamic contribution of risk factors during the disease progression of COVID-19 2) estimate individual treatment effect (ITE) from observational data 3) predict the future likelihood of individual to develop long-covid with considering the existence of confounder", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-DE0563", "title": "Comparison and Assessment of outcomes among persons with SARS-CoV-2 through a lens of Charlson Co-Morbidity Index Deconstruction and Various Prioritization Schemes, including the NIH", "task_team": false, "dur_project_id": "DUR-298C806", "workspace_status": "ACTIVE", "lead_investigator": "Bradley Price", "research_statement": "Prioritization strategies for Anti-SARS-CoV-2 therapeutics have been used when logistical constraints limited the availability for non-hospitalized patients. The main goal of these prioritization schemes is to prioritize patients who are at a higher risk for developing severe COVID-19. Prioritization schemes such as those presented by the National Institute Health (NIH)1,2 are based on factors such as age, vaccination status, immune status2, and clinical risk factors as defined by the Centers for Disease Control. All individuals in the dataset with a reported positive a SARS-CoV-2 diagnostic test will be included in the analysis. Existing concept sets that include medications for COVID-19 patients will be used. The primary outcome is all-cause mortality at day 29 after the first recorded positive diagnostic test using a methodology similar to that previously employed, with assessment for the individual factors that comprise the Charlson Comorbidity Index (rather than the CCI variable). In addition to the factors that define NIH prioritization tiers, other variables that will also be accounted for in this study are age, race, ethnicity, vaccination status, and social deprivation index. Urban and rural outcomes will also be assessed. Secondary objectives will include comparing SARS-CoV-2 disease severity across time using the ordinal scale of disease severity developed for SARS-CoV-2 patients and assessing Long Covid diagnosis as an outcome.", "accessing_institution": "West Virginia University" }, { "uid": "RP-0F692E", "title": "Long COVID diagnoses among people with Alzheimer's Disease: Examining racial and ethnic disparities", "task_team": false, "dur_project_id": "DUR-2A09175", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "Alzheimer?s Disease (AD) is the seventh leading cause of death in the United States, impacting approximately 6.7 million Americans. The COVID-19 pandemic has introduced significant health challenges, especially for those with pre-existing conditions like AD. There is growing evidence that COVID-19 can worsen cognitive decline and neuropsychiatric symptoms in AD patients, raising concerns about its long-term effects, including the onset of long COVID.\nThis study aims to explore the intersection of AD, COVID-19, and the impact of demographic factors on long COVID diagnoses. Specifically, we will investigate whether individuals with AD from different demographic backgrounds experience varying rates of long COVID diagnoses, as indicated by UO99 codes. Utilizing the comprehensive, de-identified electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C).\nBy examining the prevalence of long COVID across diverse demographic groups, this research seeks to identify potential disparities in healthcare outcomes. Understanding these differences is critical for developing targeted interventions and enhancing clinical care for vulnerable populations. The findings will provide essential insights into the compounded challenges faced by AD patients during the pandemic and will contribute to more equitable healthcare practices.\n", "accessing_institution": "Axle Informatics" }, { "uid": "RP-920B06", "title": "Hospitalization and Mortality Rates for COVID-19 and other Respiratory Viruses from the US National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-2CD7234", "workspace_status": "ACTIVE", "lead_investigator": "Luca Giurgea", "research_statement": "The objective of this project is to determine patients infected with SARS-COV-2, influenza and other respiratory viruses and assess the rates of hospitalization and mortality (particularly mortality among hospitalized individuals) across multiple respiratory virus seasons.\n\nThe project will involve retrospective data analysis of NCATS' large database: N3C.\n", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-A26874", "title": "The Impact of Early Treatment of COVID-19 with Sotrovimab on Post-Acute COVID-19 Syndrome: An Analysis of National COVID Cohort Collaborative (N3C) Data", "task_team": false, "dur_project_id": "DUR-2D8A767", "workspace_status": "ACTIVE", "lead_investigator": "Tracy Guo", "research_statement": "There is emerging evidence that in some patients, infection with SARS-CoV-2 (the virus that causes COVID-19) leads to various long-term symptoms. These persistent and long-term symptoms are referred to as post-acute sequelae of COVID-19 (PASC). The present study will evaluate the impact of sotrovimab as well as the impact of being high-risk for severe COVID-19 on PASC. Multiple definitions of PASC will be identified and assessed in Phase 1 of the study. Up to 3 definitions will be used in Phase 2 of the study to compare risk of PASC among high-risk patients treated with sotrovimab (Cohort 1), high-risk untreated patients (Cohort 2), and non-high-risk untreated patients (Cohort 3) with patient data spanning from May 26, 2020 to the end of data availability. Appropriate statistical methods will be used to control for baseline differences across cohorts that could otherwise bias the resultss. Analyses will evaluate the risk of PASC in high-risk patients who received treatment with sotrovimab (Cohort 1) compared to high-risk patients who did not receive any treatment (Cohort 2). Analyses will also evaluate the risk of PASC in untreated high-risk patients (Cohort 2) compared to untreated non-high-risk patients (Cohort 3). ", "accessing_institution": "Analysis Group Inc" }, { "uid": "RP-8C7641", "title": "The Impact of Radiation Therapy on Covid-19 Outcomes in Cancer Patients who have Received Radiation Therapy", "task_team": false, "dur_project_id": "DUR-2E287E3", "workspace_status": "ACTIVE", "lead_investigator": "Sachin Jhawar", "research_statement": "Cancer patients are at a higher risk of contracting Covid-19 and experiencing a more severe course of illness. There is a known interplay between infection, cancer, and cancer therapy. This has been most well-defined for systemic therapy, but also exists for locoregional therapy including radiation. The impact of Covid-19 on patients with different cancer types, with a focus on the effects of systemic therapy, is being studied by multiple groups. Outcomes of the SARS-CoV2 specifically in patients receiving radiation therapy are currently unknown. Reciprocally, alterations in treatment courses/decision making for locoregional therapy during the ongoing pandemic are not well defined.\nNearly 50% of all cancer patients will get radiation as part of their cancer course. There is, as of yet, little understanding of the complex interplay between this virus and the delivery of locoregional therapy, though multiple studies have shown that delays or alterations in locoregional therapy can lead to worse clinical outcomes. We suspect that based on published guidance from professional societies and individual institutions that there has been a significant impact on the treatment of cancer patients in regard to decision making on the timing and type of radiation treatment patients receive. Reciprocally, Covid-19 also tends to be more aggressive for patients with a diagnosis of cancer, whether active or remote. Radiation therapy, specifically, has complex interactions with the immune system and organs at risk from active infection and the resultant cytokine storm that can result. The complex interplay between radiation and this particular infection lends itself to many important questions.\nAnecdotal evidence from multiple institutions suggests that the timing of locoregional therapy, in particular, has frequently been altered relative to systemic therapy because of concerns regarding PPE early in the pandemic. Radiation therapy specifically represents episodes of care with prolonged and multiple exposures to the healthcare system for patients. Many guidelines for hypofractionated courses have been published in order to decrease the risk of exposure for patients.\n", "accessing_institution": "The Ohio State University" }, { "uid": "RP-CC411C", "title": "ASCVD risk estimates and their association with COVID complications", "task_team": false, "dur_project_id": "DUR-2E32C4E", "workspace_status": "CLOSED", "lead_investigator": "Yousif Arif", "research_statement": "Proposal: Nowadays, clinicians have been using tools such as the ASCVD Risk Estimator + from American College of Cardiology to quantify a patient's potential risk for atherosclerotic cardiovascular disease (ASCVD). Most of these calculators look at different factors such as age, race, lipid levels, smoking status, etc. While these patients are not diagnosed with ASCVD, their risk score can give us insight into the patient?s current cardiovascular health status. This can also illuminate patients? future complications. Given the clotting potential and cardiovascular harm of the SARS-CoV-2 virus, there is potential for predicting a worse disease progression in those with ?at-risk? ASCVD scores. With the data present in the N3C Enclave we look to calculate/determine the 10 year ASCVD risk for patients diagnosed with COVID-19 and stratify the population to determine whether there is an association between ASCVD score and COVID-19 morbidity/mortality.", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-D93065", "title": "Statistical Analysis of Impact of COVID-19 and Medication Intake on Maternal and Neonatal Outcomes", "task_team": false, "dur_project_id": "DUR-319D4E4", "workspace_status": "CLOSED", "lead_investigator": "Neshat Mohammadi", "research_statement": "Statistical Analysis of Impact of COVID-19 and Medication Intake on Maternal and Neonatal Outcomes\nMachine learning and statistical methods have proven beneficial in analyzing numerous healthcare areas, including COVID-19. However, the short-term and long-term effects of COVID-19, especially among high-risk groups such as pregnant women, have not been thoroughly investigated and understood. We intend to study the impact of COVID-19 on maternal and neonatal outcomes and investigate the statistical and machine-learning approaches that can unravel the correlation between maternal medication intake during pregnancy (before and after COVID-19 diagnosis) and COVID-19 outcome impact on mothers and neonates. Leveraging machine learning techniques can allow us to explore temporal changes in maternal and neonatal outcomes from the beginning to the end of the pandemic. These techniques can assist us in extracting the relations between temporal changes in COVID-19 outcomes and geographical variations with the development and severity of respiratory and gastrointestinal symptoms in mothers and neonates. \nPrevious studies \\cite{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887302/}, which focused on Statins and SSRIs, show a small but statistically significant, decrease in mortality among patients prescribed Statins compared to matched COVID-19-positive controls. These results motivated us to use the N3C cohort in a retrospective matched case-control other study drugs/classes of drugs of interest on maternal and neonatal and the impact of maternal medication intake on these associations.", "accessing_institution": "Stanford University" }, { "uid": "RP-5E0130", "title": "Protective Effects of Medications Against SARS-CoV-2 Infection - Level 3", "task_team": false, "dur_project_id": "DUR-32C239A", "workspace_status": "ACTIVE", "lead_investigator": "Steve Johnson", "research_statement": "Diabetes, obesity, and metabolic syndrome are highly related diseases that post increased risk for poor outcomes from SARS-CoV-2 infection, including severe COVID-19 disease and post-acute sequelae of COVID (PASC). We endeavor to understand the specific risks associated with these conditions, as well as possible mitigations to that risk. For example, a number of commonly used prescription medications for treating diabetes, obesity, and metabolic syndrome have been identified as having potential activity against SARS-CoV-2 and COVID-19 disease. It is not practical to perform randomized controlled trials to evaluate the effectiveness of all of these medications. Therefore, we will assess COVID-19's effect on these conditions and the potential protective effects of these medications using observational data from patients who are taking these medications to see if they subsequently become infected with SARS-CoV-2 or develop COVID-19 symptoms at a different rate compared to controls.", "accessing_institution": "University of Minnesota" }, { "uid": "RP-60171E", "title": "COVID-19 as a Heterogenous Process: Varying Underlying Pathophysiological Mechanisms at Different Time Points and Severities ", "task_team": false, "dur_project_id": "DUR-32D7405", "workspace_status": "CLOSED", "lead_investigator": "Stephen Lee", "research_statement": "It is likely that the SARS-CoV-2 virus? disease course is a heterogenous process, ranging from viral entry via the ACE2 receptor to replication and finally to inflammatory responses that may underlie acute respiratory distress syndrome. Using a de-identified data set, we hope to better differentiate the different processes within the disease by 1) characterizing clinical characteristics of various time points and severities as well as 2) looking at effects of medications.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-0C3E93", "title": "Impacts of COVID -19 in Older Adults (Elder Impact Domain) Level 2", "task_team": false, "dur_project_id": "DUR-3490D8A", "workspace_status": "CLOSED", "lead_investigator": "Soko Setoguchi Iwata", "research_statement": "While older adults represent 24% of overall infections in the US, almost 80% of COVID-related deaths occur in this age group. However, fewer studies have focused on vulnerable older adults especially in those with signifying characteristics of older adults including multi-morbidity, polypharmacy, and reduced cognitive and physical function. It is also not clear if overall excess risk in older adults is fully explained by known individual risk factors. We hypothesize that the older adult population with underlying comorbid conditions will have worse COVID-19-related outcomes following infection with SARS-CoV-2.\nUsing N3C data, we will conduct a series of epidemiologic studies to understand the impact of COVID-19 in older adults defined as age >= 65. Some questions include: 1)describe morbidity and mortality in older adults with COVID-19 including with multi-morbidity, polypharmacy, dementia, or those with functional limitations and how characteristics, management and outcomes; 2) Compare COVID-19 among older adults to younger adults; 3) methodological studies to identify cognitive function, physical function, multimorbidity, and polypharmacy collaborating. with NLP group. We will design cohort studies using N3C data for descriptive and analytic studies and work with the NLP group on the methodologic studies. The results of the proposed studies will advance our understanding of impacts of COVID-19 in older adults and can be used by clinicians to protect and better manage older vulnerable adults.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-BDBA8E", "title": "Phenome-wide association study of COVID-19 outcomes using electronic health records and informatic resources ", "task_team": false, "dur_project_id": "DUR-34BA792", "workspace_status": "ACTIVE", "lead_investigator": "Tracey Ferrara", "research_statement": "We are requesting controlled-tier access to the National COVID Cohort Collaborative (N3C) data as part of an ongoing effort to elucidate comorbidities and hospital outcomes associated with COVID-19 using electronic health record (EHR) resources from January 1, 2018 to present day. It is vital to have controlled tier access to examine conditions that occur in the EHR during this specific time frame. We intend to use these data to phenotype comorbidities pre and post COVID-19 infection and longitudinally characterize the natural course of disease in the hospital setting. We seek to programmatically assess de-identified data in the N3C enclave to better understand phenotypic categorization of COVID-19 and we will rely on statistical methodology such a Phenome Wide Association Studies (PheWAS) and machine learning techniques to calculate phenotype summary statistics specific to the two years before and after COVID-19 reached the United States. We intend to complete this same, time-specific process in All of Us (AoU) Research Program. We want to compare the phenotype summary statistics of AoU, an outpatient data enclave, to N3C which has hospital-based outcomes. We intend to build models based on real-time COVID-19 exposure to track disease progression and its associated risks. We are requesting real time dates to compare how our analyses change from pre-pandemic times to present day. Notably, our analyses would benefit from comparison of the dates during which different COVID-19 variants were most prevalent.", "accessing_institution": "National Human Genome Research Institute" }, { "uid": "RP-2CDB54", "title": "COVID 19 disease outcomes in patients with Inflammatory bowel disease", "task_team": false, "dur_project_id": "DUR-0025887", "workspace_status": "CLOSED", "lead_investigator": "Yousaf Hadi", "research_statement": "Patients with inflammatory bowel disease are frequently prescribed biologic agents and steroids for their gastrointestinal disease. Some preliminary data has suggested that IBD patients may be at risk of severe disease. We wish to study the clinical outcomes of COVID 19 in these patients including those with ulcerative colitis and Inflammatory bowel disease. We will compare risk of severe disease in patients on different biologic agents and those on no biologic medication. ", "accessing_institution": "West Virginia University" }, { "uid": "RP-453C03", "title": "Examining the clinical utility of the OMOP2OBO clinical mappings and algorithmic framework", "task_team": false, "dur_project_id": "DUR-35BD6B7", "workspace_status": "ACTIVE", "lead_investigator": "Tiffany Callahan", "research_statement": "We have developed OMOP2OBO,1 the first health system-wide integration and alignment between Observational Medical Outcomes Partnership (OMOP) standardized clinical terminologies and several Open Biomedical Ontologies (OBO) ontologies. These mappings will be used in multiple Covid-focused research projects that require clinical data in OMOP to be analyzed to OBO ontologies such as the Human Phenotype Ontology (HPO) or knowledge graphs that incorporate multiple OBO ontologies. They will support several broad classes of research goals in the National COVID Cohort Collaborative (N3C) such as biomarker discovery, drug repurposing, and advanced/precision phenotyping. Several projects are planned or are already underway within N3C which will require this infrastructure. Implementation of them will meet a critical need for a linking strategy between N3C research using NIH Biomedical Data Translator tools with the clinical data in N3C, for example. Initial use cases to demonstrate the value of these mappings will include studies of the psychiatric consequences of COVID and the characterization of COVID-infected patients with acute kidney injury. Additionally, we will work with the Machine Learning working group to develop novel mapping algorithms, which are trained on the OMOP2OBO mappings and applied to novel diseases and subphenotypes, which has the potential to reduce the burden of manual annotation.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-2E0376", "title": "Trends in outpatient antibiotic prescribing in COVID-19 and upper respiratory infection patients over time from the start of the pandemic.", "task_team": false, "dur_project_id": "DUR-3603EB4", "workspace_status": "CLOSED", "lead_investigator": "Amanda Gusovsky", "research_statement": "In the U.S. annually, at least 2.8 million people are infected with antibiotic-resistant bacteria and >35,000 people die as a result 1. Antibiotic resistance is a growing threat to public health2 and its most important preventable risk factor is reducing inappropriate antibiotic prescribing, particularly in high-volume settings like outpatient clinics1. \nInappropriate antibiotic prescribing is common when treating viral upper respiratory infections (URIs) - self-limited irritation and swelling of the upper airways with associated cough with no proof of pneumonia or other infection, and no history of lung disease - particularly in the outpatient setting3. \n\tThe coronavirus disease 2019 (COVID-19) pandemic has spread rapidly across the globe and led to acute hospitalizations and death. The treatment of patients with COVID-19 poses potential threats to antimicrobial stewardship activities and may contribute to antimicrobial resistance4. Although the latest World Health Organization (WHO) guidance on clinical management of COVID-19 does not recommend antibiotic therapy or prophylaxis for COVID-19 unless there are signs of a bacterial infection, many of these patients still receive antibiotics5. One study showed that 72% of hospitalized COVID-19 patients received antibiotics, but only 8% demonstrated bacterial or fungal co-infections6. Additionally, in the early days of the pandemic antibiotic use increased for some specific drugs (i.e., azithromycin) which may be a reflection of its early promotion as a potential COVID-19 therapy, despite its ineffectiveness against the disease4.\n\tIn the absence of bacterial co-infections, neither non-specific URIs nor COVID-19 should necessitate antibiotics4,7. No studies to our knowledge have examined the likelihood of inappropriate antibiotic prescribing among COVID-19 versus non-specific URIs over time after the pandemic started. The specific aims of this study are to:\n1)\tIdentify real world characteristics of patients with mild/moderate COVID-19 and non-specific URI in outpatient settings over time from April 1, 2020?September 30, 2021.\n2)\tCalculate antibiotic prescribing rates over time among patients with COVID-19 and non-specific URIs who sought care at outpatient clinics from April 1, 2020?September 30, 2021.\n3)\tEvaluate whether patients with COVID-19 or non-specific URI from April1, 2020?September 30, 2021 have a higher likelihood of receiving an unnecessary antibiotic prescription overall and over monthly time intervals.", "accessing_institution": "University of Kentucky" }, { "uid": "RP-A34123", "title": "COVID-19 and Adolescents Risk Factors", "task_team": false, "dur_project_id": "DUR-36D801C", "workspace_status": "ACTIVE", "lead_investigator": "Wei Chen", "research_statement": "The FDA CDER Office of Translational Sciences (OTS) aims to explore risk factors contributing to adolescents' heath specifically related to COVID-19 and attempt to identify their potential correlation of opioid use disorder (OUD) in this specific population.", "accessing_institution": "Food and Drug Administration" }, { "uid": "RP-CDE854", "title": "COVID-19: Simple or Complex Febrile Seizure Association", "task_team": false, "dur_project_id": "DUR-37CE4D6", "workspace_status": "CLOSED", "lead_investigator": "Sean Hanlon", "research_statement": "Febrile seizure has long been a common presentation to pediatric and adult emergency departments. It is the most common form of childhood seizure. Furthermore, these events occur in 2 ? 5% of children in the United States. During the COVID-19 pandemic, there have been case reports of febrile seizure and status epilepticus secondary to COVID-19 (10, 11). However, there has not been a study looking at a potential increased association between COVID-19 and febrile seizures to date. The research question is as follows: Does COVID-19 have increased association with febrile seizure, both simple and/or complex, as compared to other common upper respiratory viral infections? To answer this question, this study is designed as a retrospective case-control. Cases are defined as patients diagnosed with febrile seizure, which will be sub-grouped into simple and complex febrile seizure. Exposure is COVID-19 antigen and/or PCR positivity. The febrile seizure diagnosis will come from ICD-9 coding at the same facility and time as the COVID testing. Inclusion criteria are defined as children 6 to 60 months of age. Exclusion criteria are only for age < 6 months and age > 60 months. Controls will be patients with ICD-9 diagnosis of viral syndrome and acute upper respiratory infection. The same inclusion/exclusion criteria apply to the controls. Exposure will again be COVID-19 antigen and/or PCR positivity. The time period will be July 1, 2020 to December 31, 2020. This time was chosen as testing was more widely available during the second half of the year to allow for more data points. Finally, the data set will be the De-Identified Dataset (Level 2). The Indiana University IRB deemed this not requiring review. ", "accessing_institution": "Indiana University" }, { "uid": "RP-B5D391", "title": "Examining the dynamics in social determinants of health and environmental exposures with regards to differential COVID-19 incidence, morbidity, and mortality across the United States", "task_team": false, "dur_project_id": "DUR-3812B9B", "workspace_status": "ACTIVE", "lead_investigator": "Cavin Ward-Caviness", "research_statement": "Purpose: The purpose of this study is to examine differences in morbidity and mortality of\nCOVID-19 by social, demographic, and environmental factors.\nParticipants: This study will use data from the National COVID Cohort Collaborative (N3C),\nconsisting of partner institutions that provide electronic health records data for COVID and\nnon-COVID patients.\nProcedures (methods): We will examine associations between social and chemical environmental\nexposures and SARS-CoV-2 infection as well as COVID-19 incidence, morbidity, and mortality\nusing individual-level patient demographics and medical information from electronic health records\nand environmental exposures using regression analyses, factor analysis, and multi-variate\ntime-series analysis appropriate for social and environmental variables.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-887B81", "title": "Association between duration of Heated High Flow and Non-Invasive Ventilation and duration of Mechanical Ventilation in COVID-19 Patients who require Invasive Mechanical Ventilation", "task_team": false, "dur_project_id": "DUR-0102668", "workspace_status": "CLOSED", "lead_investigator": "David Douin", "research_statement": "An increasing number of COVID-19 patients are avoiding invasive mechanical ventilation by utilizing both heated high flow nasal cannula (HHFNC) and non-invasive ventilation (NIV) such as BiPAP and CPAP. However, many patients fail these oxygenation modalities and still require invasive mechanical ventilation. Patients who fail HHFNC and NIV may have an increased work of breathing during. This may make them more vulnerable to ventilator induced lung injury than those who were intubated earlier in their disease course. We hypothesize increased duration of HHFNC or NIV in patients who later require invasive mechanical ventilation is associated with increased duration of invasive mechanical ventilation. ", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-1C11A5", "title": "Examining the dynamics in social determinants of health with regards to differential COVID-19 incidence across the United States", "task_team": false, "dur_project_id": "DUR-3884992", "workspace_status": "ACTIVE", "lead_investigator": "Charisse Madlock-Brown", "research_statement": "We intend to focus on the relationship between social determinants of health (SDoH) and COVID-19 using patient cohorts and geographic units of analysis using data from the National COVID Cohort Collaborative (N3C). We have developed protocols around local policy around Covid-19, impact on disadvantaged groups, and the impact of the pandemic on inequalities. We currently have developed several protocols to determine the social determinants related to vulnerability to high incidence and poor COVID-19 outcomes. We have identified SDoH at the individual and zip-code/county level related to social deprivation, environmental factors, food access, racial characteristics, health status, and access to care. We will derive SDoH from both the limited dataset and external datasets with aggregated measures for geographical regions. In addition to identifying associations between SDoH and COVID-19, we will assess the modifying effects of COVID-19 policies and shelter-in-place behavior on those associations.\n", "accessing_institution": "University of Tennessee Health Science Center" }, { "uid": "RP-DD0EDC", "title": "Investigating COVID-19 burden in neurofibromatosis type 1 patients", "task_team": false, "dur_project_id": "DUR-398A059", "workspace_status": "CLOSED", "lead_investigator": "Jineta Banerjee", "research_statement": "Pre-existing conditions like diabetes, cardiovascular diseases, or pulmonary vulnerabilities influence the prognosis of COVID-19 in the human population. However, the prognosis of COVID-19 in populations diagnosed with rare diseases, specifically neurofibromatosis (a family of diseases like NF1, NF2, and Schwannomatosis), is not well understood. Understanding the prevalence and co-morbidities of COVID-19 in people diagnosed with neurofibromatosis may help identify new detection criteria, isolation criteria, or treatment candidates for COVID-19. We aim to implement machine learning methodologies that are robust towards identification and analysis of rare samples to elucidate the prevalence and co-morbidities of COVID-19 in people with Neurofibromatosis.", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-D0B805", "title": "COVID Impact on Mental Health and Substance Use and Abuse among Older Adults ", "task_team": false, "dur_project_id": "DUR-3A2527C", "workspace_status": "CLOSED", "lead_investigator": "Lisa Roberts", "research_statement": "Nationwide, older adults are overrepresented among COVID-19 infections and death. According to SAHMSA, approximately 20% of older adults misuse alcohol, over-the-counter medications, and prescription drugs. Like other age groups, substance use and abuse among older adults may have increased substantially during the pandemic, further compounding mental health issues. Approximately 15% of older adults suffer a mental health issue. Anxiety, depression, and substance abuse are the most common, with increasing prevalence as age increases. \nSocial isolation, loneliness, sleep disturbance, disabilities, lacking socioeconomic resources, and weakening of the healthcare system during the pandemic are among the main factors that negatively affect older adults? mental health. Almost a third of older adults live alone, and a growing number live in institutional settings such as nursing homes, and are thus disproportionally affected by COVID-19 safety and prevention measures (i.e. physical distancing, stay at home orders, quarantine). A systematic review found high rates of sleep disturbance among patients hospitalized with COVID-19 (33.3 ? 84.7%) and survivors who were discharged (29.5 ? 40%), with higher rates among older adults, who are already at increased risk for sleep disturbance. Additionally, sleep disturbance is associated with increased mental health symptomology, thus adding to the complexity of mental health among older adults. Social isolation and loneliness are known to have negative effects on mental and emotional health, issues which may be exacerbated among older adults who may find digital means of maintaining social connections difficult to manage. Older adults also have a substantially higher rate of disability (including disabilities related to physical or functional limitations, cognitive and sensory impairment). Older adults with disabilities also have higher rates of mental health issues. Older adults lacking socioeconomic resources are at increased risk of loneliness and disability, which may have been further impacted by COVID-19 (in terms of both economic security and social isolation) again increasing their risk of mental health issues. Moreover, the pandemic weakened and overwhelmed healthcare systems, adversely affecting older adults. Not only was access to care reduced, telehealth may have been the only available option at times. Telehealth, while improving access to mental health care in a safe social distancing manner, is fraught with a number of barriers for older adults (the foremost being technical literacy). Therefore, the use of telehealth to treat mental health issues among older adults may be limited.\n", "accessing_institution": "Loma Linda University" }, { "uid": "RP-62B145", "title": "Sex Differences in COVID-19", "task_team": false, "dur_project_id": "DUR-3A59997", "workspace_status": "ACTIVE", "lead_investigator": "Luca Giurgea", "research_statement": "Differences in clinical outcomes between sexes have been identified with numerous infectious diseases, including influenza and most recently, COVID-19. Unlike in influenza, where females have worse outcomes, COVID-19 seems to be more severe in males. However, the underlying factors contributing to this difference are unknown. This study aims to disentangle the effects of underlying medical problems, some of which may be more frequent in males, from inherent biological differences, like the hormone milieu. ", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-363C32", "title": "COVID Related Sepsis: Clinical Course, Epidemiology, Neglected Populations, and Therapy (CRESCENT)", "task_team": false, "dur_project_id": "DUR-3AAA153", "workspace_status": "CLOSED", "lead_investigator": "Michael Katehakis", "research_statement": "Bacterial and fungal sepsis has emerged as a global public health threat to patients hospitalized for COVID-19. Using the N3C database combined with state of the art statistical analysis we will investigate key questions on diagnosis, risk factors, outcome, treatment, and the impact to the underserved, vulnerable, and special needs populations on the development of sepsis in patients hospitalized with COVID-19. In addition, we will explore how economically disadvantaged populations fare when they face sepsis with COVID-19. ", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-E63E7E", "title": "COVID-19 cardiovascular patients and vaccination", "task_team": false, "dur_project_id": "DUR-3B419A9", "workspace_status": "ACTIVE", "lead_investigator": "Maryam Khodaverdi", "research_statement": "Aim of this project is to study individuals with cardiovascular diseases who have taken covid-19 vaccine. Patients with cardiovascular diseases are demonstrated strong risk factors for COVID-19. However, there is little if any analysis after vaccination for high risk group of patients. Analysis steps that were planned contains: studying characteristics of this patients infected by SARS-CoV2, and studying/explain their outcomes. ", "accessing_institution": "West Virginia University" }, { "uid": "RP-E8DA40", "title": "Study effects of COVID-19 Infection on cancer patients", "task_team": false, "dur_project_id": "DUR-3BEC532", "workspace_status": "ACTIVE", "lead_investigator": "Umit Topaloglu", "research_statement": "Recent studies highlight the importance of COVID-19-cancer precision medicine. Published on Lancet, Lee and colleagues(Lee, Cazier et al. 2020) find that the only primary cancer type that demonstrates statistically significant mortality changes due to COVID-19, after adjusted for age and sex, is leukemia (OR: 2.25; 95% CI: 1.13 ? 4.57; p-value: 0.023; ICD10: C51-C58. COVID-19. Another study published on Nature Medicine, Robillotti et al. (Robilotti, Babady et al. 2020) found that, in the cancer cohort of Memorial Sloan Kettering Cancer Center (423 symptomatic COVID-19 cases out of 2,035 cancer patients tested between 3/10/2020 and 4/7/2020), immune checkpoint inhibitors and age > 65 together are predictors for hospitalization and severe disease of COVID-19 among cancer patients. These studies prove the concept of the heterogeneity of COVID-19?s impact on cancer patients. A systematic data mining on a more representative, broader cohort with rich clinical, demographic, and socioeconomic features will provide more insights on the risk population and actionable knowledge. Correspondingly, our Specific Aims are:\n1- Identify risks factors for cancer patients based on primary site and morphology on a larger scale due to varying results (e.g. Gustave Roussy and CCC19): Death and severity of COVID-19 (e.g. ICU use, WHO Ordinal Scale) will be used for outcome,\n2- Development of cancer phenotype definitions on Palantir to be used for this study. Atlas has already 96 predefined concept sets for carcinoma.\n3- Study of outcomes in cancer patient from healthcare utilization effect perspective: including delays in treatment due to COVID-19 and worsening effects including AE (i.e. CTCAE Diagnosis initially and NLP generated AE symptoms)", "accessing_institution": "Wake Forest Baptist Medical Center" }, { "uid": "RP-32EC87", "title": "Disparate health care, covid-19 and long-term sequelae", "task_team": false, "dur_project_id": "DUR-3C56D21", "workspace_status": "CLOSED", "lead_investigator": "Padmini Varadarajan", "research_statement": "Background: The novel Covid-19 virus has infected more than a million worldwide. Cardiac involvement is seen in about 20-30% of hospitalized patients, probably contributing to a large proportion of deaths. Cardiac injury can occur either by direct infiltration of the virus into the myocardium or indirectly due to stress induced systemic inflammation and thus causing cardiac injury. Patients from largely underserved populations tend to have higher risk factors imposing a higher infection with Covid-19. We would like to evaluate effect of racial, gender, economic disparities in patients with covid-19 infection and relate to adverse prognosis and long-term sequelae.\nHypothesis: Patients living in underserved areas are prone to higher rates of comorbid conditions such as hypertension (HTN), diabetes mellitus (DM), obesity, renal dysfunction. They are known to have higher risk of covid -19 infection. We hypothesize that racial, economic, gender disparities could lead to later presentation, delay in treatment which may have adverse effect on their survival. \nMethods: We would like to access the national Covid database. Complete demographics, including gender, location via zip code, hospital admission data, symptoms, clinical data, biological markers, serum chemistries, medications, device history, covid-19 genetic types if available, blood group will be collected. Information regarding hospital length of stay, discharge or death will be collected. \nAnalysis: Univariate analysis showing adverse effects of Covid 19 will be computed from the database. Variables that are statistically significant will be entered in multivariate analysis to seek out independent predictors of adverse effect on survival, prolongation of hospitalization, reinfection rates in the underserved population. Mathematical modeling will be used to predict long term effect of Covid-19 in the under served population. \nConclusions: We expect to show the effect of health care disparities in Covid -19 related cardiac disorders. Such disparities can lead to delay in presentation, diagnosis, treatment and survival. Mathematical modeling would be helpful in identifying factors leading to disparities and predict effect on mortality and morbidity. Such modeling can be helpful in understanding handling of future pandemics in underserved communities.\n", "accessing_institution": "University of California, Riverside" }, { "uid": "RP-B05DC5", "title": "Pediatric Compications of COVID ", "task_team": false, "dur_project_id": "DUR-3C61977", "workspace_status": "ACTIVE", "lead_investigator": "Lee Pyles", "research_statement": "Pediatric Domain Group is investigating issues of morbidity and mortality in children in N3C. We are interested in impact of obesity on respiratory illness in COVID but also need to explore the observations that were presented in 2022 Pediatric Academic Society Meeting (Khodaverdi). Hypertension and heart failure predicted increased morbidity but with small numbers. Prevalence of baseline clotting disorder such as Hemophilia or Factor V Leiden has not been investigated and this is under consideration in a domain subgroup led by Dr. Marie Steiner of University of Minnesota. UMN is a N3C partner.", "accessing_institution": "West Virginia University" }, { "uid": "RP-3E92BB", "title": "Beyond the Virus: Exploring the Intersection of COVID-19 and Obstructive Sleep Apnea", "task_team": false, "dur_project_id": "DUR-3D39968", "workspace_status": "ACTIVE", "lead_investigator": "Mirna Elizondo", "research_statement": "This research project aims to investigate the correlation between SARS-CoV-2 (COVID-19) and Obstructive Sleep Apnea (OSA) using data sourced from the NCATS N3C Enclave system. The primary research question focuses on understanding the relationship between COVID-19 infection and OSA, with the objective of identifying potential comorbidities and risk factors associated with COVID-19. Access to electronic health records (EHRs) containing COVID-19 and OSA-related data is essential from the N3C Enclave system, including patient demographics, clinical notes, laboratory results, diagnostic codes, and treatment information. Advanced machine learning techniques, including gradient boosting and deep learning methods, will be employed to analyze the data. Performance metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC) will be used to evaluate the models. This research project aims to contribute to the understanding of the interplay between COVID-19 and OSA, ultimately improving patient care and public health interventions.", "accessing_institution": "Texas State University" }, { "uid": "RP-DE8CEA", "title": "Smoking and COVID-19 Outcomes in U.S. Adults (SCOTUS)", "task_team": false, "dur_project_id": "DUR-3DB3125", "workspace_status": "CLOSED", "lead_investigator": "Jay Atanda", "research_statement": "Recent systematic reviews exploring the association between smoking and COVID-19 related outcomes provide conflicting evidence. Farsalinos et al (2020) for example raise the hypothesis that nicotine may have a beneficial effect on COVID-19 in their meta-analysis of 13 published papers. Their preliminary analysis did not support the argument that current smoking is a risk factor for hospitalization for COVID-19. Glantz et al (2020) on the other hand, in their own review of 12 published papers report a significant association between smoking and COVID-19 progression. At least three earlier reviews also reached different conclusions or were inconclusive. All highlight these gaps 1) the importance of data where smoking status are accurately recorded, 2) the role of and possible influence of confounding factors and selection bias that underlying studies did not consider, and 3) the need for observational studies where smoking is the primary exposure of interest. Our objective is to conduct a retrospective case-control study using de-identified data from the National COVID Cohort Collaborative (N3C) and; 1) examine the prevalence of current smoking among U.S. hospitalized cases with COVID relative to the general population, and 2) examine the association between smoking status and COVID-19 clinical outcomes. This study will contribute to the literature on smoking association with COVID-19 outcomes and fill the gaps identified above, and is especially important here in the U.S. (only one of the studies in the existing reviews is from the U.S.) where 34.2M adults currently smoke and 16M live with a smoking-related disease.", "accessing_institution": "United States National Library of Medicine" }, { "uid": "RP-04D363", "title": "Postoperative complications following total joint arthroplasty in Covid+ patients", "task_team": false, "dur_project_id": "DUR-3E8E37E", "workspace_status": "ACTIVE", "lead_investigator": "Jami Pincavitch", "research_statement": "After Covid diagnosis, there are changes to the pro-inflammatory/pro-thrombotic state. In elective joint replacement surgery, this inflammation/thrombosis could lead to increased postoperative risks. Will be utilizing data to understand the safest timing for elective surgery following Covid infection and complication rates compared to cohorts.", "accessing_institution": "West Virginia University" }, { "uid": "RP-D1B6BE", "title": "Med-BERT for N3C", "task_team": false, "dur_project_id": "DUR-4034C2E", "workspace_status": "ACTIVE", "lead_investigator": "Laila Gindy Bekhet", "research_statement": "Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Tranformer-based models have achieved tremendous success in the natural language processing (NLP) domain. Similarly, The pretraining of Med-BERT on EHR data for more than 20 million patients generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by our earlier Med-BERT, and the latest advancement in training foundation models in the NLP domain, we propose to evaluate our pre-trained Med-BERT on the N3C data for two downstream prediction tasks: 1) COVID-19 patient outcomes prediction on admission, and we will compare the results against our previously published CovRNN model. 2) Patient risk for long Covid (PASC) at first diagnosis using the patient's previous history. Additionally, we will train a new and advanced version of Med-BERT using the whole N3C cohort, and compare its performance on the above-mentioned downstream tasks. ", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-916714", "title": "Identifying important risk factors for severe COVID-19 outcomes ", "task_team": false, "dur_project_id": "DUR-1E93A2B", "workspace_status": "CLOSED", "lead_investigator": "Mengyue Han", "research_statement": "Apply machine learning algorithms to identify potential risk factors for severe COVID-19 outcomes like death, ICU stay etc. For this aim, the project will include potential risk factors like demographics, medications, social characteristics. The primary outcome will be a discrete variable. Machine learning methods like random forest and boosting may be applied.", "accessing_institution": "Duke University" }, { "uid": "RP-AB8850", "title": "The relationship between cardiac arrhythmias and SARS-CoV-2; an incidence and mortality analysis", "task_team": false, "dur_project_id": "DUR-018D980", "workspace_status": "CLOSED", "lead_investigator": "Yousif Arif", "research_statement": "We already know that patients admitted to the ICU have an increased risk for developing cardiac arrhythmias (atrial fibrillation, atrial flutter, sinus tachycardia, etc.). Atrial fibrillation (AF) is a common arrhythmia in the critically ill and presents with an increased risk for thromboembolic stroke and heart failure. In the ICU, AF has been shown to be associated with adverse outcomes. The acute loss of atrial systole and onset of rapid ventricular rates associated with new-onset AF leads to hemodynamic instability in the critically ill. The etiology of new-onset AF explains why it can be a marker for patient outlook/status. \n\nMany patients infected with SARS-CoV-2 report palpitations as a symptom of the disease course. With many of the ICU beds currently being utilized to treat patients with a more severe case of SARS-CoV-2, we are interested in investigating whether there is a relationship between SARS-CoV-2 and the development of cardiac arrhythmias (such as AF). We are also interested in elucidating whether or not there is an association between cardiac arrhythmias and mortality amongst SARS-CoV-2 patients. \n", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-78C98E", "title": "Operational N3C Logic Liaison Education Workspace", "task_team": false, "dur_project_id": "DUR-019C1D5", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "This workspace will be used to develop and publish Logic Liaison templates in the Education Enclave. It will not be used for research purposes, but rather to develop tools that support researchers working in this enclave.", "accessing_institution": "Citizen Scientist" }, { "uid": "RP-0C4105", "title": "DEPTH: Developing evidence-based intervention pathways addressing COVID-19 adverse outcomes across the pregnancy continuum", "task_team": false, "dur_project_id": "DUR-07D08C4", "workspace_status": "CLOSED", "lead_investigator": "Naleef Fareed", "research_statement": "We propose to study the effects of COVID-19 among women of reproductive age to investigate its short-and long-term effects across the pregnancy continuum, with attention to changing effects caused by COVID-19 and its variants. We plan to use the deidentified data set from N3C for the following study aims:\nAim 1: Evaluate whether the COVID+ cohort was associated with higher adverse maternal and birth outcomes than the control cohort (women who do not test positive for COVID-19).\nAim 2: Assess whether any maternal and birth outcome effects among the COVID+ cohort varies by different categories of risk strata (i.e., race, area-level SDoH, or geographical region, and clinical risk). \nAim 3: Assess whether impacts of interventions to improve maternal and birth outcome effects among the COVID+ cohort vary by different categories of risk strata. \n", "accessing_institution": "The Ohio State University" }, { "uid": "RP-B742A5", "title": "Impact of booster dose frequency during the Omicron predominant period in the United States", "task_team": false, "dur_project_id": "DUR-0CC6AA4", "workspace_status": "ACTIVE", "lead_investigator": "Margaret Lind", "research_statement": "COVID-19 severity diminished after the emergence of the Omicron variant in the winter of 2021/2022. At the same time, the effectiveness of the COVID-19 vaccine diminished. As a result, the protective effect of COVID-19 vaccine booster doses against severe disease remains unclear. Recent data from a simulation from Park et al. suggests that more frequency (twice annual or annual) booster doses provide benefit over bi-annual (every other year) booster dose receipt when it comes to the risk of severe COVID-19 illness, especially within older people. However, the value of more frequent doses has yet to be shown in real-world data and existing, conditional vaccine effectiveness estimates against severe outcomes fail to account for the confounder-mediator relationship between vaccination and non-severe COVID-19. Our goal is to use g-computation and the N3C data to evaluate the impact of regular COVID-19 booster dose receipt on the risk of COVID-19 related hospitalization during the Omicron predominant period. We plan to perform this analysis among all adults and among age-group stratified subpopulations. This analysis will inform COVID-19 vaccination strategies for the general population and high-risk subpopulations. ", "accessing_institution": "Yale University" }, { "uid": "RP-E88192", "title": "COVID-19 Health Equity in at Risk Populations in West Virginia ", "task_team": false, "dur_project_id": "DUR-0EB78B1", "workspace_status": "CLOSED", "lead_investigator": "Adam Baus", "research_statement": "This project uses de-identified person level demographic and COVID-19 (i.e. testing and vaccination) related data gathered from five West Virginia health care systems and the National COVID Cohort Collaborative (N3C) to inform targeted West Virginia health systems in efforts to improve care and outreach for vulnerable populations including those who are racial and ethnic minorities, have underlying medical conditions (such as diabetes, heart disease, cancer, etc.), are 65+, and those with social determinants of health that put them at increased risk for contracting COVID-19 (i.e. low-income, limited access to healthcare).", "accessing_institution": "West Virginia University" }, { "uid": "RP-91E880", "title": "Acute pancreatitis in patients with COVID-19 ", "task_team": false, "dur_project_id": "DUR-17651E3", "workspace_status": "CLOSED", "lead_investigator": "Bing Chen", "research_statement": "Previous studies showed that SARS-CoV-2 might cause acute pancreatitis through multiple mechanisms, including direct virus-mediated injury, virus-induced lipotoxicity, and drug-induced injury. This study aims to study the severity, and outcome of acute pancreatitis in COVID-19 patients. ", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-A39709", "title": "Risk for Adverse Surgical Outcomes in Patients with Perioperative COVID-19", "task_team": false, "dur_project_id": "DUR-191969D", "workspace_status": "CLOSED", "lead_investigator": "Nathaniel Verhagen", "research_statement": "The surgical landscape has been greatly altered since the onset of the COVID-19 pandemic. Early research around COVID-19 revealed patients with acute, perioperative SARS-CoV-2 infection to be at increased risk for postoperative complications. This has led to institutions adopting policies that delay surgery for up to 7 weeks. While these programs have been effective in mitigating the risk associated with perioperative SARS-CoV-2 infections, they may be unnecessarily delaying surgery in select patients. In order to provide timely, high-quality surgical care safely in patients with prior COVID-19, we must consider the various factors that contribute to a patient?s surgical outcome including, surgical timing relative to SARS-CoV-2 infection, SARS-CoV-2 variant, COVID-19 severity (including long-COVID), age, gender, race, comorbidities, and vaccination status. \n\nThe specific aims of this study are: \n1-\tTo update research regarding optimal surgical delay following SARS-CoV-2 infection\n2-\tAssess the impact of COVID-19 on postoperative outcomes in a wide range of surgery types (emergent, cosmetic, transplant, etc.)\n3- Elucidate the impact of SARS-CoV-2 variant on surgical outcomes\n3-\tApply machine learning to develop risk-stratification tools with respect to individual preoperative factors to aid in clinical decision making\n", "accessing_institution": "Medical College of Wisconsin" }, { "uid": "RP-7EDA78", "title": "Fracture point: the impact of COVID caseload at the hospital and regional levels on the outcome of surgical patients.", "task_team": false, "dur_project_id": "DUR-1C80DE8", "workspace_status": "CLOSED", "lead_investigator": "Paul Kuo", "research_statement": "The primary objective of this study is to evaluate the dose-response relationship between (a) the volume of COVID patients at the hospital and regional levels and (b) clinical outcomes of surgical patients. As a secondary objective, we will determine which hospital characteristics might mediate the relationship between COVID caseload and the surgical patient outcomes. Our study will be based on a limited dataset version of N3C (National COVID Cohort Collaborative), enriched through the linkage of publicly available datasets through zip codes representing patients? residence location. These linked datasets will include the American Hospital Association (AHA) survey as well as a group of datasets providing information on social determinants of health (SDOH), which include the American Community Survey (ACS), The Environmental Quality Index (EQI), and the Food Access Research Atlas (FARA).\n\n", "accessing_institution": "University of South Florida" }, { "uid": "RP-477069", "title": "Evaluating the Incidence of Depression and PTSD Among Deaf or Hard-of-Hearing Patients Hospitalized with COVID-19", "task_team": false, "dur_project_id": "DUR-26035AE", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "The COVID-19 pandemic has significantly exacerbated mental health challenges worldwide, particularly among deaf or hard-of-hearing (D/HoH) individuals. This study evaluates the incidence of depression and post-traumatic stress disorder (PTSD) in D/HoH patients hospitalized with COVID-19. D/HoH individuals face unique stressors due to communication barriers, such as reliance on facial cues and sign language, which are hindered by mask mandates and reduced access to interpreters. Previous research has indicated significantly higher rates of PTSD and depression among individuals with hearing loss during the pandemic compared to their hearing peers.\n\nThis project employs data from the National COVID Cohort Collaborative (N3C) to examine the impact of demographic factors, including age, sex, race, and ethnicity, on mental health outcomes in D/HoH patients. By conducting a detailed analysis, the study aims to identify patterns and potential risk factors associated with the onset of PTSD and depression. The ultimate goal is to generate actionable insights that can inform targeted interventions and healthcare policies, thereby contributing to a more equitable and supportive healthcare system for D/HoH individuals in future public health crises. This research underscores the critical need for tailored mental health support and inclusive healthcare practices to mitigate the heightened psychological impact on D/HoH patients during and after hospitalization for COVID-19.\n", "accessing_institution": "Axle Informatics" }, { "uid": "RP-6A17C1", "title": "Cardiovascular differences amongst different racial groups who have been exposed to COVID-19", "task_team": false, "dur_project_id": "DUR-26B6AA0", "workspace_status": "CLOSED", "lead_investigator": "Nicholas Garcia", "research_statement": "We are currently aware that COVID-19 affects the pulmonary and cardiovascular system during infection and post-infection. This study aims to look deeper into the specific cardiovascular complications that affect patients who have been exposed to COVID-19 (i.e. changes in blood pressure, heart rate, RBC, platelets, etc). Considering that the pandemic has caused patients to postpone their regular visits with their cardiologist, we believe this analysis can help clinicians understand how to care for patients with cardiovascular complications post-pandemic. This study can also help us understand which patients may be at greater risk for future, greater cardiovascular complications.\n\nMore importantly, we aim to stratify the data and analyze the cross-section of cardiovascular health and racial differences. This is really important due to recent public health studies showing that minority communities are more negatively affected by the pandemic. This focused racial analysis can help clinicians find more personalized treatments for patients and could help highlight the need for greater COVID relief to at-risk communities.\n", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-6A939B", "title": "Study Phenotype of COVID-19 in Patients with Hemophilia: A National Cohort Study", "task_team": false, "dur_project_id": "DUR-2840D30", "workspace_status": "CLOSED", "lead_investigator": "Anjali Sharathkumar", "research_statement": "Hypercoagulable state plays pivotal role in pathogenesis of Novel Coronavirus 2019 SARS-CoV-2 (COVID-19) disease. It is characterized by clinical thrombosis in up to 30% of patients, which in turn is associated with poor outcome. In general, the prothrombotic milieu is mediated through activation of coagulation and elevation in procoagulant proteins such as factor VIII(FVIII), factor IX(FIX), Von Willebrand factor (VWF) and fibrinogen. Established risk factors for COVID-19 including age, gender, cancer, diabetes and hypertension are known to be associated with elevation of these procoagulant proteins implying their role in determining the disease severity. Factors VIII and IX are critical for thrombogenesis as hemophilia patients with deficiency of these proteins, hemophilia A and B respectively, experience life-threatening bleeding diathesis. Therefore a subset of hemophilia patients with severe bleeding phenotype require treatment with regular administration of FVIII and FIX concentrates to prevent and treat bleeding. Never the less, it is unclear if patients with hypo-coagulable state associated with congenital deficiency of FVIII and FIX proteins are less susceptible to SARS-CoV-2 infection and or have milder course if infected by this virus. The proposed study leverages upon the wealth of N3C Data Enclave and its powerful analytic capabilities. The deidentified dataset from N3C data will be used to characterize the phenotypic variability and outcomes of COVID-19 in hemophilia patients B. The research activities in this proposal will not only enable us to develop tailored strategies for better prevention and treatment of COVID-19 in hemophilia population but will guide us about appropriate regimen for factor replacement therapy during this pandemic.", "accessing_institution": "University of Iowa" }, { "uid": "RP-DA737D", "title": "Time from symptom onset to diagnosis of COVID-19 during the Omicron era.", "task_team": false, "dur_project_id": "DUR-286AAB7", "workspace_status": "CLOSED", "lead_investigator": "Fatima Jones", "research_statement": "The Omicron variant of COVID-19 has emerged to become the dominant SARS-CoV-2 strain circulating in the U.S. and across the world. This study aims to use clinical, demographic, and laboratory data from patients infected with COVID-19 during the Omicron period to investigate the time from infection and onset of clinical symptoms to peak viral load and confirmation of COVID-19 diagnosis.", "accessing_institution": "National Institute of Allergy and Infectious Diseases" }, { "uid": "RP-352145", "title": "PhD student", "task_team": false, "dur_project_id": "DUR-2AE45B9", "workspace_status": "CLOSED", "lead_investigator": "Liang Shan", "research_statement": "Spatial analysis of covid-19.", "accessing_institution": "Florida State University" }, { "uid": "RP-62B42E", "title": "The RADx Long COVID Prediction Challenge - organizers", "task_team": false, "dur_project_id": "DUR-329C75A", "workspace_status": "CLOSED", "lead_investigator": "Timothy Bergquist", "research_statement": "The emergence of post-acute sequelae of SARS-CoV-2 (PASC) is presenting serious and ongoing impact on people?s health and the American health care system. While details on the prevalence, causes, treatment and consequences of PASC are actively being researched, growing evidence suggests that more than half of COVID-19 survivors experience at least one symptom of PASC at six months after recovery of the acute illness. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial COVID-19 disease. Advancements in the software tools using Artificial Intelligence (AI)/Machine Learning (ML) approaches may enable the potential for providing clinical decision support on candidate prognostic factors and assessments of a patient?s risk to developing PASC. \n\nTo that end, we are proposing to conduct a community challenge within the National COVID Cohort Collaborative (N3C) enclave sponsored by the Rapid Acceleration of Diagnostics (RADx) initiative to engage with the machine learning community to develop risk prediction models for identifying COVID patients who are at risk of developing long COVID. We will establish a gold standard true positive dataset against which risk prediction models will be benchmarked. Using N3C data, challenge organizers will identify viable challenge questions focused on predicting long COVID and the associated risks. Participants in this challenge will build models on a training dataset established by the challenge organizers. Those trained models will then be tested on a holdout set to establish initial model accuracy. These trained models will be evaluated against a battery of accuracy and generalizability tests including longitudinal generalizability, cross-site generalizability, hold-out dataset accuracy, and prospective evaluations.", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-2A20DB", "title": "Mitigating sedative-related risk in the management of acute respiratory failure", "task_team": false, "dur_project_id": "DUR-3320ABC", "workspace_status": "CLOSED", "lead_investigator": "Allan Walkey", "research_statement": "The purpose of this project is to determine the balance of risks and benefits for opioid-based sedation strategies for patients with acute respiratory failure requiring mechanical ventilation, both in patients with and without COVID-19. Mechanical ventilation provides respiratory support to more than 1 million patients in the US annually with acute respiratory failure, including those with the most severe manifestations of COVID-19. Guidelines for management of analgesia and sedation during mechanical ventilation recommend an opioid-based ?analgesia-first? approach, a strategy that differs markedly from recommendations to reduce opioid prescribing and associated risks for long-term opioid use across most other clinical settings. However, optimal sedation and analgesia strategies to maintain patient comfort during mechanical ventilation, but minimize short-term (delirium, prolonged mechanical ventilation) and long-term risks (persistent opioid use, readmissions, day alive and out of institution) are unclear. Multiple knowledge gaps remain before opioid-sparing strategies during acute respiratory failure can be prospectively tested, including more precise classification of current sedation-analgesia practice patterns, quantification of comparative risks and benefits of opioid-sparing strategies during mechanical ventilation for short-term and long-term outcomes, and identification of patients who may be most at risk for long-term opioid-related complications resulting from higher dose opioid exposure during mechanical ventilation. Using the NCATS N3C De-Identified Data Set, we therefore seek to (1) characterize clinical practice patterns used for sedation and analgesia among patients with acute respiratory failure who receive mechanical ventilation ? both with and without COVID-19; and (2) compare outcomes of opioid- vs non-opioid sedative-predominant strategies during mechanical ventilation. Findings from this project will directly inform the design of randomized trials testing novel mechanical ventilation sedation and analgesia strategies that maximize both short- and long-term benefit.", "accessing_institution": "University of Massachusetts Medical School" }, { "uid": "RP-A9EB04", "title": "mRNA Vaccines Effect on Venous Thromboembolism in COVID19 Pneumonia", "task_team": false, "dur_project_id": "DUR-33BB9AB", "workspace_status": "CLOSED", "lead_investigator": "Yusra Medik", "research_statement": "Thrombosis, microangiopathy and endothelial injury have been identified in pathogenesis of COVID-19 infection early on during pandemic. The incidence of venous thromboembolism in hospitalized COVID-19 patients reported to be higher and was associated with higher mortality. \nmRNA vaccines have been approved by FDA in December 2020 and since then has been the single most important intervention to take pandemic under control. Depending on the strain up to 15% of fully vaccinated patients have been tested positive. However, this population has significantly lower rates of hospitalization and mortality. Effect of vaccines on venous thromboembolism (DVT and PE) in breakthrough COVID19 patients has not been studied. Here, we will investigate the rate of venous thromboembolism in breakthrough COVID19 patients who received mRNA vaccines. \n", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-BF35F4", "title": "AI ML Analysis of Mental Health Disparities in COVID-19 Patients: Examining the Effect of Race, Ethnicity, and Age on Developing Psychosis Symptoms During Hospitalization", "task_team": false, "dur_project_id": "DUR-35530C0", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "This research aims to investigate mental health disparities among COVID-19 patients, with a specific focus on psychosis symptoms during hospitalization. Utilizing artificial intelligence and machine learning techniques, the study examines the influence of race, ethnicity, and age on the development and severity of psychosis symptoms in this patient cohort. Through analysis of diverse patient data, including demographics and mental health assessments, the study seeks to identify predictive factors associated with psychosis symptoms. The primary goal is to uncover potential disparities in psychosis symptomatology and provide insights for targeted interventions. By understanding the complex interplay between demographic factors and mental health outcomes, this research aims to contribute to improved patient care and resource allocation strategies during the COVID-19 pandemic.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-E0D331", "title": "A hybrid intergroup median centric classifier to predict long covid outcome.", "task_team": false, "dur_project_id": "DUR-3BCBC8D", "workspace_status": "CLOSED", "lead_investigator": "Mahbubur Rahman", "research_statement": "We develop a hybrid intergroup median based classifier to predict long covid outcome for this research project. The classifier can be trained by specific categorical feature along with different categorical features. As a result, the classifier can be used to identify the important categorical features leading to long covid outcome. Hence, we would like to have access to synthetic dataset.\nThe intergroup represents group inside group. The outer group corresponds to specific categorical feature while the inner group corresponds to labels. The centroid of each group is initialized and updated by the respected median of the quantitative features at the inter grouping steps. The centroid is updated and corrected at the inter grouping steps to improve the overall accuracy of the classifier. \n", "accessing_institution": "Data-Automata LLC" }, { "uid": "RP-687464", "title": "The Effect of COVID-19 Stay-At-Home Orders on Hemoglobin A1c Levels in Diabetic Patients", "task_team": false, "dur_project_id": "DUR-3E3080C", "workspace_status": "CLOSED", "lead_investigator": "Annette Hays", "research_statement": "According to the Centers for Disease Control (CDC), approximately 30.3 million people or 9.4% of the United States (US) population had diabetes mellitus in 2015. As uncontrolled diabetes may lead to increases in morbidity, mortality, and costs of care for patients, it is paramount that patients achieve and maintain their glycemic goals. In an effort to promote positive glycemic outcomes, a reliable patient-provider relationship and regular follow-up are cornerstones of quality diabetic care. ?n March 2020, several states underwent emergency stay-at-home orders in an effort to suppress the peak of the Novel Coronavirus, COVID-19. Healthcare facilities were forced to alter their care and reschedule routine care patients for later dates. Among these routine patients were both controlled and uncontrolled diabetics. Using the National COVID Cohort Collaborative (N3C) database, we will retrospectively review patients with a documented COVID-19 infection and diabetes to evaluate clinical outcomes, including A1c control.", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-8D4772", "title": "Developing data intensive policies for disparity in health outcome for COVID-19 patients", "task_team": false, "dur_project_id": "DUR-3EE2355", "workspace_status": "CLOSED", "lead_investigator": "Mohammad Adibuzzaman", "research_statement": "As COVID-19 indiscriminately ravages communities around the country, equitable and fair treatment of patients within healthcare settings have become a focal issue. An ethical response to the pandemic would ensure fairness in healthcare facilities, especially in terms of allocating resources or prioritizing treatment. Since past research on non-pandemic health issues consistently documented discriminations, often inadvertently, against patients from ethnic minority backgrounds or vulnerable populations, the issue of health disparity has been a great concern in research and clinical communities as well as for policy makers.", "accessing_institution": "Purdue University" }, { "uid": "RP-074A99", "title": "Machine Learning for Identifying Severe Outcome Risk Factors in COVID-19", "task_team": false, "dur_project_id": "DUR-404799B", "workspace_status": "CLOSED", "lead_investigator": "ELAINE HILL", "research_statement": "Risk factors of COVID-19 mortality, and other severe outcomes, have thus far been identified based on clinical studies using traditional statistical methods. Understanding of the full suite of the risk factors remains limited and the true death toll of the pandemic is unknown. The objective of this research is to develop a replicable framework for identifying risk factors for severe COVID-19 outcomes using N3C cohort data and advanced data-driven methods. The use of advanced machine-learning methods to achieve this objective is critical, as traditional statistical methods may not be able to handle issues likely present in these data, including severe multicollinearity, a high imbalance between known COVID-19 involved and non-involved cases, and sparsity. Using the limited data set (level 3) from N3C and machine-learning techniques, we will identify predictors of severe COVID-19 illness (e.g., hospitalization, ICU use, ventilator use). In our main models, we will include individual demographics, clinical factors, and underlying medical conditions as well as county and 5-digit zip code-level demographic, economic, and social characteristics. For exploratory analyses, we will include data on healthcare resources and local environmental characteristics.", "accessing_institution": "University of Rochester" }, { "uid": "RP-857126", "title": "Prevalence and outcomes of co-infection and super-infection with SARS-CoV-2 and other pathogens", "task_team": false, "dur_project_id": "DUR-40C95A3", "workspace_status": "CLOSED", "lead_investigator": "Jackson Musuuza", "research_statement": "Patients infected with SARS-CoV-2 (the virus that causes COVID-19) can have other respiratory infections present at the time of SARS-CoV-2 infection diagnosis. In medical terms this is called co-infection. On the other hand, these patients can acquire new infections during the process of treatment for COVID-19. This is referred to as superinfection. Recent research has shown that the presence of either co-infections or super-infections may lead to poor patient outcomes such as an increased risk of death and higher likelihood of the need to help patients breathe (mechanical ventilation), prolonged length of hospital stay (LOS) and need for intensive care unit (ICU) admission. In this study we will use the N3C data to determine the proportion of COVID-19 patients that also have co-infections and that of COVID-19 that subsequently get super-infections. We will also examine factors that might increase the likelihood of having these extra infections among COVID-19 and outcomes of occurrence of co-infections and super-infections among COVID-19 patients.", "accessing_institution": "University of Wisconsin?Madison" }, { "uid": "RP-45BD6C", "title": "An Optimization-based Social Separation Framework to Minimize the Spread of Diseases in Social Networks", "task_team": false, "dur_project_id": "DUR-465E3B7", "workspace_status": "CLOSED", "lead_investigator": "Su Li", "research_statement": "We provide a novel optimization-based framework that aims to identify social separation policies that optimally mitigate the spread of diseases in social networks. The study considers a number of important factors, such as subject-specific risk information and the negative economic impact of imposing separation restrictions. The data type we required is the Synthetic dataset in order to get subjects' risk information, such as age, health conditions. The objective is to generate a bunch of subjects' infection risks to test the performance of our framework.", "accessing_institution": "Texas A&M University" }, { "uid": "RP-8C99CD", "title": "Postpartum Long Term Effects of Women with COVID- 19 ", "task_team": false, "dur_project_id": "DUR-468DEED", "workspace_status": "CLOSED", "lead_investigator": "Christina Bhola", "research_statement": "An estimated 426,000 women have been diagnosed with COVID during pregnancy or during postpartum. Complications for this vulnerable population include maternal mortality, chronic lung diseases, obesity, and hypertension. New evidence suggests that women are at an increased risk of negative health outcomes during the first year postpartum however women are traditionally only followed for the first 6 weeks postpartum. The study aim is to prove that pregnant women with COVID should be followed up to one year postpartum looking at the variables of their demographics, procedures, medications, lab results, and more through the N3C Database. There are additional goals of observing the extension of COVID symptoms or long COVID and its effects on postpartum women. ", "accessing_institution": "University of South Carolina" }, { "uid": "RP-D1CC96", "title": "Risk of Psychiatric Sequelae within Six Months of SARS-CoV-2 Infection in Adults with No Prior Psychiatric History", "task_team": false, "dur_project_id": "DUR-487B4B9", "workspace_status": "CLOSED", "lead_investigator": "ELAINE Hill", "research_statement": "This project seeks to describe the risk of a first psychiatric diagnosis among adult patients in the N3C database within six months of SARS-CoV-2 infection, as well as examine the risk of a first psychiatric diagnosis following infection when stratified by sociodemographic factors that have been shown to increase risk of contracting SARS-CoV-2 and developing COVID-19 (age, sex, race, and ethnicity). We will describe the relative risk of a first psychiatric diagnosis within six months of confirmed SARS-CoV-2 infection, and compare to recently published literature to assess generalizability of our findings to the population. Additionally, we will perform a series of logistic regressions to examine the relative risk of a first psychiatric diagnosis among different categories of age, sex, race, and ethnicity, while adjusting for confounding factors such as body mass index, smoking status, and pre-existing comorbidities (hypertension, diabetes, and chronic lower respiratory, cardiovascular, chronic kidney, and chronic liver diseases). All outcomes of interest will be coded dichotomously; any diagnosis of a mood disorder, anxiety disorder, or psychotic disorder will be considered a positive outcome.", "accessing_institution": "University of Rochester" }, { "uid": "RP-D0CD11", "title": "Impact of the Pandemic on Incidence Rates of Non-Fatal Suicide Attempts ", "task_team": false, "dur_project_id": "DUR-4D31226", "workspace_status": "CLOSED", "lead_investigator": "David Bard", "research_statement": "Deaths by suicide are a leading cause of adolescent and young adult deaths nationally; presentations to the emergency room for suicidal ideation or attempts at self- harm through ingestions or other means are common. This project will expand existing research conducted at OU Health Sciences Center, where it was found that there has been a large increase since the beginning of the COVID-19 pandemic. We will review records for patients aged 4 years or older with diagnoses associated with suicide attempt or self harm and attempt to identify trends in overall rates within the context of pre-existing patterns, with special attention paid in the period of time after March 2020. Age-dependent suicide attempt time-to-event tables will be constructed and compared over pre and post-pandemic eras. ", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-DE9F29", "title": "Performance of comorbidity scores among individuals with COVID-19", "task_team": false, "dur_project_id": "DUR-4E3D763", "workspace_status": "ACTIVE", "lead_investigator": "Hemalkumar Mehta", "research_statement": "Comorbidities are a usual source of confounding. Researchers have historically used Charlson comorbidity scores to control confounding due to comorbidities in COVID-19 studies. In this project, we propose to adapt the Elixhauser comorbidity score and compare its performance against Charlson comorbidity score for clinical outcomes among individuals with COVID-19. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-BD78E2", "title": "Stage Aware Prediction of COVID19 Disease Progression", "task_team": false, "dur_project_id": "DUR-4EC781C", "workspace_status": "CLOSED", "lead_investigator": "Charles Livermore", "research_statement": "Development of a deep learning method to perform prediction of COVID19 disease progression in patients with respect to the stages of clinical treatment. Our intent is to utilize statistically similar data for the purposes of initial data exploration and model development in this domain. ", "accessing_institution": "University of Houston" }, { "uid": "RP-CDE210", "title": "Impact of COVID vaccination status on Clostridioides difficile Infection (CDI) Clinical Outcomes", "task_team": false, "dur_project_id": "DUR-5019628", "workspace_status": "ACTIVE", "lead_investigator": "Bijun Kannadath", "research_statement": "Clostridioides difficile is a major healthcare-associated infection, with an incidence of 116.1 cases per 100000 persons(1). Among that, 56% of Clostridioides difficile infection (CDI) cases used antibiotics in the prior 12 weeks(1), which makes CDI one of the most prevalent problems in hospitals and nursing homes. C. difficile is a gram-positive, spore-forming, anaerobic bacillus(2). Symptoms(3) include diarrhea, abdominal pain, and cramps, with complications like colitis, pseudomembrane formation, fulminant colitis(4), perforation, megacolon, or death. \nThe COVID-19 pandemic has been linked to an increase in CDI(5), attributed to gut dysbiosis and antibiotic overuse(6, 7). At least 81% of the US population has received one COVID-19 vaccine dose, and 70% are fully vaccinated(8). Stoian et al(9) have shown concurrent COVID-19 and CDI increases morbidity and mortality. However, the relationship between COVID-19 vaccination and CDI remains unexplored. This study aims to analyse this relationship using the National COVID Cohort Collaborative (N3C) Data Enclave(10).\n1.Centers for Disease Control and Prevention. 2024. Emerging Infections Program, Healthcare-Associated Infections ? Community Interface Surveillance Report, Clostridioides difficile infection (CDI), 2022 2024 [Available from: \"> https://www.cdc.gov/healthcare-associated-infections/media/pdfs/2022-CDI-Report-508.pdf.\n2.Goudarzi M, Seyedjavadi SS, Goudarzi H, Mehdizadeh Aghdam E, Nazeri S. Clostridium difficile Infection: Epidemiology, Pathogenesis, Risk Factors, and Therapeutic Options. Scientifica (Cairo). 2014;2014:916826.\n3.Vaishnavi C. Clinical spectrum & pathogenesis of Clostridium difficile associated diseases. Indian J Med Res. 2010;131:487-99.\n4.Adams SD, Mercer DW. Fulminant Clostridium difficile colitis. Curr Opin Crit Care. 2007;13(4):450-5.\n5.Bentivegna E, Alessio G, Spuntarelli V, Luciani M, Santino I, Simmaco M, et al. Impact of COVID-19 prevention measures on risk of health care-associated Clostridium difficile infection. Am J Infect Control. 2021;49(5):640-2.\n6.Langford BJ, So M, Raybardhan S, Leung V, Soucy JR, Westwood D, et al. Antibiotic prescribing in patients with COVID-19: rapid review and meta-analysis. Clin Microbiol Infect. 2021;27(4):520-31.\n7.Linares-García L, Cárdenas-Barragán ME, Hernández-Ceballos W, Pérez-Solano CS, Morales-Guzmán AS, Miller DS, et al. Bacterial and Fungal Gut Dysbiosis and Clostridium difficile in COVID-19: A Review. J Clin Gastroenterol. 2022;56(4):285-98.\n8. Available from: https://usafacts.org/visualizations/covid-vaccine-tracker-states/.\n9.Stoian M, Andone A, Boeriu A, Bândil? SR, Dobru D, Laszlo S, et al. COVID-19 and Clostridioides difficile Coinfection Analysis in the Intensive Care Unit. Antibiotics (Basel). 2024;13(4).\n10.Haendel MA et al. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc. 2021;28(3):427-43.", "accessing_institution": "The University of Arizona" }, { "uid": "RP-E18646", "title": "Treatment associated with lower mortality in hospitalized COVID-19 patients", "task_team": false, "dur_project_id": "DUR-51C074E", "workspace_status": "CLOSED", "lead_investigator": "Hong Liu", "research_statement": "The scale of COVID-19 cases is unprecedented. Our objective is to investigate the treatments that are associated with a lower mortality in hospitalized COVID-19 patients. This is a retrospective large cohort study involving all COVID-19 patients who required hospitalization. The exposures are any COVID-19-related treatments. The outcome is all-cause in-hospital mortality. We use Cox regression in analysis. We will estimate survival using the product-limit Kaplan-Meier curve and use the log-rank test to compare survival curves. We will use a complete case analysis. Treatments administered with a frequency less than 1% will not be considered in the univariate analysis. We then first fit a univariate Cox proportional-hazards model to screen for potential therapies that are associated with mortality. We then use a multivariable Cox proportional-hazards model to estimate independent associations between therapies and hospital mortality. The multivariable analysis includes known patient risk factors for mortality (age, sex, hypertension, requirement for invasive mechanical ventilation) and the therapies selected based on the results of univariate analysis. This is an exploratory investigation. The results of this study will facilitate the investigation of the effective treatments of COVID-19. ", "accessing_institution": "University of California, Davis" }, { "uid": "RP-159EFE", "title": "Temporal Dynamics of COVID-19 Infection and Post-Operative Outcomes Following Coronary Artery Bypass Grafting: A Retrospective Analysis", "task_team": false, "dur_project_id": "DUR-52A1DC3", "workspace_status": "ACTIVE", "lead_investigator": "henry hoang", "research_statement": "The COVID-19 pandemic has posed unprecedented challenges to healthcare systems worldwide, impacting various aspects of patient care, including surgical outcomes. Coronary Artery Bypass Grafting (CABG) procedures are common interventions for patients with coronary artery disease, but the interaction between COVID-19 infection and post-operative outcomes remains poorly understood. Understanding the temporal relationship between COVID-19 infection and complications following CABG surgery is crucial for guiding clinical management strategies.", "accessing_institution": "Albert Einstein College of Medicine" }, { "uid": "RP-A54EC3", "title": "Impact of Pre-admission PPI/H2RA Use on Severity of COVID Patients", "task_team": false, "dur_project_id": "DUR-551E540", "workspace_status": "CLOSED", "lead_investigator": "Xiaoyang Ruan", "research_statement": "Despite advances in treatment and vaccination, COVID-19 continues to exert a heavy toll on healthcare utilization. Hence identifying modifiable risk factors for severe infection is of the utmost importance. Studies suggest that gastric acid can impair the infectivity of SARS-CoV-1, so gastric acid suppression with PPIs/H2RA can make enterocytes more susceptible to infection by this virus and facilitate spread to other organs causing severe COVID-19. However, there is conflicting evidence regarding gastric acid suppression and the risk of severe COVID-19. Studies from Korea and Germany have showed that antacid users were at an increased risk of developing severe COVID. However, subsequent studies from the UK and North America failed to show an elevated risk for mortality or mechanical ventilation with antacid use. ", "accessing_institution": "Mayo Clinic" }, { "uid": "RP-6D6676", "title": "A Study of the Association between GERD and COVID-19", "task_team": false, "dur_project_id": "DUR-5775A17", "workspace_status": "CLOSED", "lead_investigator": "Marie Ozanne", "research_statement": "Gastroesophageal reflux disease (GERD) occurs when stomach acid frequently flows back into the esophagus, which can irritate the lining. Since the outbreak of the COVID-19 pandemic, researchers have found a higher prevalence of GERD. For example, a study of prevalence and risk factors of GERD in Saudi Arabia recorded a GERD prevalence of 34.2% during the pandemic versus 24.8% pre-pandemic. Moreover, some common symptoms of GERD are also more common in COVID patients compared to people without COVID, and other symptoms of GERD may be more pronounced in COVID patients. While possible explanations for increased GERD prevalence during the COVID-19 pandemic have been proposed, based on the existing research, it is unknown whether there is a true association between the incidence of COVID-19 and GERD in the US population, or whether these two conditions are simply co-occurring, potentially as a result of lifestyle changes during the pandemic, for example. In this project, we aim to quantify the prevalence of GERD in COVID-19 positive individuals, and compare it to the general population. We also aim to identify demographic and clinical factors associated with GERD in COVID-19 positive individuals, and compare them to the general population.", "accessing_institution": "Mount Holyoke College" }, { "uid": "RP-D6332D", "title": "COVID-19 infection and mortality among individuals with disabilities", "task_team": false, "dur_project_id": "DUR-59CB398", "workspace_status": "CLOSED", "lead_investigator": "Jamie Doyle", "research_statement": "Individuals with disabilities may not be able to practice social distancing and other COVID-19 preventive measures due to their impairment, potential reliance on caregivers for activities of daily living, and the necessity to continue essential therapies. The purpose of this study is to examine geographic variation and correlates of COVID-19 infection and mortality rates of individuals with developmental, intellectual, and physical impairments as compared to those without. Rates will be weighted based on population size of the zip code and stratified by disability. A survival analysis will be performed with mortality as the outcome. Results from this study will help inform public health interventions targeting this vulnerable population. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-209D56", "title": "Marijuana and Invasive Fungal Disease in Immunocompromised Patients", "task_team": false, "dur_project_id": "DUR-5CADBE3", "workspace_status": "CLOSED", "lead_investigator": "Matthew Duprey", "research_statement": "Aspergillus, a common mold found in the environment, has been known to grow on cash crops like\nmarijuana. While it is harmless to the general population, it carries significant risks for individuals with\ncompromised immune systems. For cancer patients with leukemia and those receiving an allogeneic\nhematopoietic stem cell transplant (HSCT), solid organ transplant recipients receiving immunosuppressive\ndrugs to reduce rejection, individuals taking immunomodulatory therapy for autoimmune disorders, individuals\nreceiving high dose steroids, and people living with HIV, contact with fungal spores can lead to invasive\npulmonary disease. Although rare, invasive fungal infections like aspergillosis cost the US medical system $1.2\nbillion. Survival rates range from 25-59%, depending on the underlying disease state and risk factors. Immunocompromised individuals are also more likely to develop COVID-19, a disease state that has been associated with markedly higher rates of invasive fungal infection. Questions remain as to whether the increased rate of invasive fungal infection is due to the disease process of COVID-19 or due to the underlying immunocompromise.\n\nRecently, cultivation of cannabis sativa for recreational and medicinal use has increased, based on\nchanging legislative culture in many states. This has led to increases in marijuana use with a widely varied set\nof safety regulations between states. Aspergillus has been cultured from marijuana and found in commercially\navailable cannabis products. Despite some states having policies to test for fungal contamination, the variation\nin regulation between states leaves end users vulnerable to fungal contamination of marijuana, which could\nlead to invasive pulmonary disease. Specifically, states who decriminalize marijuana without robust fungal\ntesting regulations are putting their most vulnerable citizens at risk of a potentially fatal infection.\nWe propose to study the impact of changing marijuana policies using a natural experiment approach\nwhich can control for year-to-year variation while evaluating changing policy. ", "accessing_institution": "University of Kentucky" }, { "uid": "RP-19677E", "title": "Effect of SARS-CoV-2 vaccination on severity of disease in patients with T2-high immune profiling", "task_team": false, "dur_project_id": "DUR-5E9D76A", "workspace_status": "CLOSED", "lead_investigator": "Benjamin Sines", "research_statement": "Prior to the COVID-19 pandemic, vaccine-associated enhanced respiratory disease (VAERD) was reported in several preclinical studies of SARS-CoV and MERS-CoV vaccines involving different animal model systems. Associated vaccines included whole-inactivated virus particle vaccines (inactivated vaccines) and both replicon-vectored and recombinant subunit Spike protein vaccines. Vaccinated animals exhibited enhanced lung injury and type 2 immunopathology, including pulmonary eosinophil infiltration and upregulation of type 2 cytokines, during coronavirus infection. Robust neutrophilic and lymphocytic pulmonary infiltrates were also described. Most studies were performed in Balb/c mice, but vaccine-enhanced immunopathology was also reported in C57BL/6 mice, ferrets and non-human primates. We propose to study outcomes of SARS-CoV-2 infection in patients with clinical correlates of T2-high immune profile with and without vaccination, using the real world evidence available in the N3C data enclave.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-4255C7", "title": "Investigating the impact of biases on the performance of analytical models for COVID-19 related care.", "task_team": false, "dur_project_id": "DUR-6119C9E", "workspace_status": "CLOSED", "lead_investigator": "Suranga Kasthurirathne", "research_statement": "Artificial?intelligence (AI)?offers?considerable?potential to support efforts to combat the COVID-19 pandemic. However, a majority of datasets used for COVID research were originally collected in support of patient care or to document costs, and not in support of research purposes. The quality of such datasets may be influenced by prejudiced decision making, poor representation of vulnerable populations, and incompleteness or errors in data collection and processing[1, 2]. These issues contribute to bias, which is broadly defined as systematic errors resulting in one population being favored over another[2]. Use of such datasets for analytics leads to the garbage in - garbage out problem[3] resulting in biased analytical models harmful to vulnerable populations such as minorities, seniors, women, persons with special healthcare needs or those impacted by adverse SDoH[4, 5]. These concerns are particularly relevant in context of the COVID epidemic, which has raised serious questions on disparities in care across diverse patient populations. As such, there is an urgent need to Investigate analytical models for biases caused by demographic factors such as race/ethnicity, gender or age, or various SDoH. Our long-term goal is to develop reproducible methods to address systematic analytical biases targeting vulnerable populations. The objective of this proposal is to mitigate analytical biases in AI models that predict COVID related care needs. \n\n1.\tRoebuck K. Data quality: high-impact strategies-what you need to know: definitions, adoptions, impact, benefits, maturity, vendors: Emereo Publishing; 2012. ISBN: 1743048564.\n2.\tFerryman K, Pitcan M. Fairness in precision medicine. Data & Society. 2018.\n3.\tKim Y, Huang J, Emery S. Garbage in, garbage out: data collection, quality assessment and reporting standards for social media data use in health research, infodemiology and digital disease detection. Journal of medical Internet research. 2016;18(2):e41.\n4.\tShankar S, Halpern Y, Breck E, Atwood J, Wilson J, Sculley D. No classification without representation: Assessing geodiversity issues in open data sets for the developing world. arXiv preprint arXiv:171108536. 2017.\n5.\tTommasi T, Patricia N, Caputo B, Tuytelaars T. A deeper look at dataset bias. Domain adaptation in computer vision applications: Springer; 2017. p. 37-55.", "accessing_institution": "Indiana University" }, { "uid": "RP-442493", "title": "MELD score and Maddrey discriminant function for prognosis prediction in covid patients ", "task_team": false, "dur_project_id": "DUR-9158CC0", "workspace_status": "CLOSED", "lead_investigator": "Howard Chung", "research_statement": "This project will focus on the prognosis of COVID-19 in patients with alcoholic hepatitis. Using the NCATS N3C Data Enclave, we will investigate the differences between alcoholic hepatitis patients with high vs. low Maddrey discriminant function/MELD scores. Compare the mortality rate of patients with high/low MELD scores in different settings and the changes of the scores after treatments. ", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-E11471", "title": "Understanding the COVID-19 disease course characteristics in Filipinos", "task_team": false, "dur_project_id": "DUR-632CD63", "workspace_status": "CLOSED", "lead_investigator": "Gerard Dumancas", "research_statement": "As of September 22, 2022, there were approximately 4 million cases in the Philippines with 62,695 COVID-19 deaths. Despite this number of cases in the country, there remains a scarcity of research on the detailed clinical course of this disease among Filipinos due to the lack of an organized aggregated patient data resource in the Philippines. The N3C dataset is a potentially rich source of Filipino patient data on the clinical course and outcome of COVID-19 as there is a large Filipino diaspora in the United States and Filipinos constitute a substantial fraction of the American healthcare workforce who responded to the pandemic and were also severely affected by it. In collaboration with researchers from the Philippines, we plan to establish the clinical course of the disease among Filipinos and compare this with other ethnicity cohorts in the dataset to reveal disease characteristics affected by the underlying genetic substrate. Comparing the disease course from the N3C Filipino cohort with data from patients in the Philippines may also reveal aspects of the disease that are affected by geography and/or environmental factors. \n\nThe overall goal of this research is to establish a clinical course of COVID-19 among Filipinos utilizing various information obtained from the N3C dataset which includes gender, age, ethnicity, drug and device exposure information, records of clinical observations, laboratory results, vital signs, and quantitative findings from pathology reports among others, and other data available.", "accessing_institution": "University of Scranton" }, { "uid": "RP-53E7EA", "title": "Fungi and COVID19", "task_team": false, "dur_project_id": "DUR-68C87E7", "workspace_status": "CLOSED", "lead_investigator": "Iliyan Iliev", "research_statement": "Some patients with COVID19 suffer from complications related to fungal infections. Here we explore whether specific treatments are beneficial in this situation. ", "accessing_institution": "Weill Cornell Medicine" }, { "uid": "RP-33CDE7", "title": "COVID-19- Clinical Differences between Symptomatic Patients & Asymptomatic Carriers", "task_team": false, "dur_project_id": "DUR-6A3E2E1", "workspace_status": "CLOSED", "lead_investigator": "Moriya Cohen", "research_statement": "The purpose of this research is to suggest a new method for explainable AI that will allow from one hand to predict if a person will be asymptomatic and from the other hand to explain the reason for that prediction. This will allow the physician and the decision makers to trust the machine learning models. Our new method will combine prediction algorithms and cluster analysis algorithms.\nDecision makers need to rely on data when creating an action plan. But a ?black box? algorithm cannot provide the relevant information. Decisions need to be based on analysis process based. Our goal is to combine patient?s predictive algorithms with clustering algorithms in order to trace sub groups among asymptomatic and symptomatic patients.\nIf we want to control the pandemic, we need to understand the way it is spreading. The key to that understanding rely on our ability to identify COVID-19 carries, assuming we can?t test everyone at all times. \n", "accessing_institution": "Bar-IIan University" }, { "uid": "RP-252A2B", "title": "Severity of COVID-19 infection among the first infection and re-infection patients", "task_team": false, "dur_project_id": "DUR-6C1D466", "workspace_status": "CLOSED", "lead_investigator": "Aish Ravisankar", "research_statement": "Corona Virus 2019 caused by Severe Acute Respiratory Syndrome Virus 2019 became a global pandemic in 2019 affecting 500 million individuals. The reinfections with SARS-COV-2 are an important aspect of COVID-19. Reinfections, commonly defined as a positive RT-PCR test after 60 days from the first RT-PCR positive testing became more common after mass vaccination while the virus continued to adapt into variants with increased infectivity. The first infection with SARS-COV-2 does not confer lasting immunity. Studies of Electronic Health Record are a crucial part of the US National RECOVER initiative . This research study aims to assess the severity of the COVID-19 infection among the first infection and re-infection among high-risk category individuals. The research study will use Electronic Health Record information from N3C data. The N3C data has health records from over 75 hospitals holding 16 million patient records. The N3Cs analytics platform was used for data access and analysis.\nThe Inclusion and Exclusion Criteria for the study includes the following\n \n1. ICD-10 COVID-10 diagnosis code or a positive SARS-CoV-2 PCR or Antigen test between March 1, 2020, to October 1 2022; The earliest event was the COVID-19 index date.\n2. Re infection events occurring earlier to December 1, 2022.\n3. Age: 60 years and above.\n4. Having at least one recorded health care visit prior to COVID-19 index date.\n5. Having one recorded health related comorbidity.\n\nRe-infection is defined as positive SARS-CoV-2 or antigen test that occurred 60 days after the recorded initial COVID-19 index date. Many literature findings have 90 days for reinfection post index date, while some literature reviews indicate that patients stop shedding SARS-CoV- 2 after 60 days, hence for this study the 60 days was selected as an appropriate threshold.\n \n", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-6B45AE", "title": "Assessment of correlations between risk factors and symptom presentation among defined at-risk groups ", "task_team": false, "dur_project_id": "DUR-6CED270", "workspace_status": "CLOSED", "lead_investigator": "Jamie Henzy", "research_statement": "We propose to use COVID patient data to find any associations between factors such as age, gender, comorbidities and COVID symptoms or symptom length. For example, do immunocompromised patients with COVID more frequently experience nausea? We'll take an exploratory approach using statistical methods and visualization techniques appropriate to multidimensional data sets. Any correlations identified may allow us to find patterns in disease presentation specific to a given population, informing treatment regimens and precautions.", "accessing_institution": "Northeastern University" }, { "uid": "RP-DE5C73", "title": "Benchmarking Generative Adversarial Network (GAN) based synthetic EHR data generation methods for enabling COVID-19 research", "task_team": false, "dur_project_id": "DUR-70D61B9", "workspace_status": "CLOSED", "lead_investigator": "Justin Guinney", "research_statement": "The goal of this project is to benchmark existing Generative Adversarial Network (GAN) based algorithms for generating synthetic feature sets to predict COVID-19 caused hospitalization and to establish the standards for assessing the quality of synthetic data. We will preprocess the N3C de-identified dataset to a feature set including patients? demographics, condition, procedure and drug code and apply GAN models on this feature set to generate synthetic data. We will assess the quality of synthetic data in terms of its statistical resemblance to real data, its utility for making hospitalization predictions, as well as privacy risks. This work complements - and will be coordinated with - the Synthetic Data workstream.", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-15CA52", "title": "Analyzing changes in symptom presentation among individuals with Myalgic Encephalomyelitis or Chronic Fatigue Syndrome (ME or CFS) and Long COVID ", "task_team": false, "dur_project_id": "DUR-722A7E3", "workspace_status": "ACTIVE", "lead_investigator": "Charisse Madlock-Brown", "research_statement": "This project aims to analyze changes in symptom presentation among individuals with Myalgic\nEncephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID. Patients with chronic inflammatory conditions, including fibromyalgia, ME/CFS, Long COVID, and multiple sclerosis all report symptoms changes or cycling over time Yet there is little to no research on this phenomenon. By analyzing how patient-reported quality of life and disease presentation fluctuates, as well as biological data, we can develop improved management strategies and create hypotheses around IACC pathology.", "accessing_institution": "University of Iowa" }, { "uid": "RP-BA93BD", "title": "Precision Phenotyping of Long COVID", "task_team": false, "dur_project_id": "DUR-76AA4BE", "workspace_status": "ACTIVE", "lead_investigator": "Yuan Wang", "research_statement": "This project is aimed at precision phenotyping of long COVID cases using ontology structures. ", "accessing_institution": "University of South Carolina" }, { "uid": "RP-7BC7D1", "title": "Cardiovascular Disease and COVID-19: Predictors of COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-77B92ED", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "The proposed project aims to investigate the predictors of COVID-19 outcomes in patients with cardiovascular disease. Utilizing data from the N3C Data Enclave, we will analyze the impact of various clinical and demographic factors on the severity and mortality of COVID-19 among this high-risk population. The study will focus on identifying key predictors such as viral load, comorbidities, and treatment regimens, including the use of antiviral therapies like remdesivir. Our findings will contribute to the development of targeted interventions and improve clinical outcomes for patients with cardiovascular disease affected by COVID-19.", "accessing_institution": "login.gov" }, { "uid": "RP-01A7C4", "title": "Retrospective identification of medications to treat COVID-19", "task_team": false, "dur_project_id": "DUR-787342D", "workspace_status": "CLOSED", "lead_investigator": "Ben Richardson", "research_statement": "COVID-19 is a disease caused by the novel coronavirus SARS-CoV-2 which has infected over 11 million people in the US and killed over 250,000. Many avenues are currently being pursued to help mitigate the spread and lethality of the virus, including vaccine development, more rapid testing, and repurposing of already-approved pharmacotherapeutics. Unfortunately, the novelty of SARS-CoV-2 is requiring the development of new treatments, which takes time and are not yet available for successfully treating and eliminating the virus. However, repurposing already-approved drugs may be a more rapidly-available solution in the short term while vaccines are not yet widely available. To evaluate the potential therapeutic benefit of existing drug regimens to limit duration and severity of COVID-19 disease, the disease (observation) duration, death events, and multi-day hospital stays in COVID-19-positive patients in the N3C database will be evaluated as they relate to patient exposures to specific drug classes. We will evaluate level 2 de-identified data to identify relationships between lower disease severity/shorter duration and concurrent exposure to specific drugs or drug class may identify novel treatment strategies to lower disease severity, death rates, and reduce spread. Such identification of effective pharmacotherapies may also highlight novel targets for the development of future treatments or vaccines.", "accessing_institution": "Southern Illinois University School of Medicine" }, { "uid": "RP-15CC06", "title": "Effect of Pharmacologic Modulation of Cholinergic Status on Respiratory Infection Outcomes in Elderly", "task_team": false, "dur_project_id": "DUR-7ED1455", "workspace_status": "ACTIVE", "lead_investigator": "Veronika Zarnitsyna", "research_statement": "Using a retrospective study design, we aim to explore the effect of pharmacological modulation of cholinergic status on the outcome of respiratory viral infections in the elderly. We propose using N3C SARS-CoV-2 infection data to assess how cholinergic and anticholinergic drugs affect the likelihood of symptomatic infection and mortality after primary and secondary infection.", "accessing_institution": "Emory University" }, { "uid": "RP-52E5C4", "title": "Estimating individual treatment effect for various COVID-19 treatment", "task_team": false, "dur_project_id": "DUR-856A5CC", "workspace_status": "CLOSED", "lead_investigator": "Yanfei Wang", "research_statement": "As a global pandemic, a lot of work has been devoted to COVID-19 studies. Unfortunately, there is currently no specific treatment for COVID-19. Although we might have access to records of patients, their medications, and outcomes, we do not have complete knowledge of why a doctor decides which medication to a patient. To identify drugs that affect COVID-19 outcomes, we would like to develop an algorithm to estimate individual treatment effects (ITE) using a limited data set (level 3). In particular, we would like to determine whether the use of medicine (Bamlanimab, bebtelovimab, evusheld, lagevrio, paxlovid) at baseline will reduce the incidence and/or severity of COVID-19. What is the relationship between treatment and Long-COVID? However, the major challenge of estimating the individual treatment effect is the existence of confounding, which depends on variables but also affects the outcome. For example, richer patients might better afford certain medications and may receive better health care. ", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-679506", "title": "Application of Machine Learning to Identify LOAD and Investigate COVID-19 as a Potential Risk Factor using EHRs from the N3C", "task_team": false, "dur_project_id": "DUR-8B4E90A", "workspace_status": "ACTIVE", "lead_investigator": "Carly Rose", "research_statement": "Alzheimer?s Disease (AD) is the seventh leading cause of death in the United States, with an estimated 6.7 million Americans living with AD. Late-onset Alzheimer?s Disease (LOAD), defined as when AD symptoms onset at 65 or older, accounts for 1 in 9 people ?65. Additionally, Black Americans are disproportionately affected by LOAD relative to other racial groups, with 18.6% of Black Americans over the age of 65 affected. Even with this racial disparity in the burden of the disease, little research has been conducted focusing on AD phenotyping for early detection among the African American population. Comprehensive phenotyping for all populations will deepen the understanding of AD risk factors. With the emergence of the Coronavirus disease of 2019 (COVID-19) and the fact that it predominantly impacts older and immunocompromised individuals, it has emerged as a new potential risk factor for AD. To enhance the understanding of LOAD phenotyping for early detection and its relationship with COVID-19, the proposed analysis will perform machine learning techniques on electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C). The N3C offers a unique opportunity to access national, de-identified, individual-level EHR data from nearly 100 institutions across the United States for rapid and meaningful research related to COVID-19. First, the proposed study will develop a machine learning algorithm for phenotyping LOAD with three Alzheimer?s Disease-specific datasets, which have been reviewed by a clinical adjudication board (CAB) to identify AD cases with certainty. The proposed machine learning pipeline includes merging the AD datasets, preprocessing, feature engineering, exploratory data analysis, splitting the final data set into 80/20 training/testing sets, running through six determined machine learning models, and model evaluation. Once a suitable algorithm for phenotyping has been developed and validated, it will be applied to N3C?s self-reported Black population, which accounts for over 3 million patients. This method is estimated to extract around 105 thousand LOAD cases among this study population. These cases will then be used to determine if there are significant differences in the development or progression of LOAD in patients after receiving a COVID-19-positive test. This will help to determine if COVID-19 is a potential risk factor for LOAD, as it has been shown to affect brain structure and predominantly impacts older individuals. ", "accessing_institution": "Case Western Reserve University" }, { "uid": "RP-8C9850", "title": "Anti-depressant use and COVID-19 morbidity and mortality ", "task_team": false, "dur_project_id": "DUR-8DDAD9D", "workspace_status": "CLOSED", "lead_investigator": "Samin Kamal", "research_statement": "Emerging evidence suggests a possible association between anti-depressant use, especially selective serotonin reuptake inhibitors (SSRIs), and a protective effect in relation to COVID-19 morbidity and mortality. Previous studies have been limited to studying SSRI anti-depressants and have not considered other major classes of anti-depressants for which there also exist plausible, protective biological mechanisms. Sample size limitations have prevented previous studies from independently analyzing the effect of specific anti-depressant drugs, instead having to rely on grouped analyses. To overcome this limitation the proposed study will provide robust estimates of the association between anti-depressant use, across all five major classes, and COVID-19 morbidity and mortality. Leveraging the large sample size of the N3C cohort will lead to more precise estimates of exposure effects across individual drugs. The categories of anti-depressants that will be investigated include SSRIs, serotonin and norepinephrine reuptake inhibitors (SNRIs), tricyclic anti-depressants (TCAs), monoamine oxidase inhibitors (MAOIs), and an atypical category to characterize all other remaining anti-depressants. Various clinical measurements and outcomes will be used to characterize and compare morbidity in terms of escalating severity across the clinical spectrum. These include but are not limited to mechanical ventilation, cardiac events, surgical interventions, neurological complications, etc. Access to the level two de-identified data set is requested to analyze morbidity and mortality across the five exposure groups defined above. ", "accessing_institution": "University of Rochester" }, { "uid": "RP-B8261D", "title": "Impact of pharmaceutical interventions on COVID-19 mortality and long-COVID in the United States", "task_team": false, "dur_project_id": "DUR-9072437", "workspace_status": "CLOSED", "lead_investigator": "Nicole Swartwood", "research_statement": "COVID-19 patient outcomes are improved through the use of vaccines and anti-viral medications. In this analysis we will use de-identified patient data from N3C to calculate the coverage of COVID-19 vaccination and anti-viral medication use over time for different patient groups, as well as patient mortality rates and long-COVID incidence rates as a function of vaccination status, anti-viral medication use, and other risk factors such as age. ", "accessing_institution": "Harvard University" }, { "uid": "RP-995485", "title": "Predicting Hospitalization of Covid-19 confirmed cases", "task_team": false, "dur_project_id": "DUR-90B3DCA", "workspace_status": "CLOSED", "lead_investigator": "Zhenhui Xu", "research_statement": "This is a course project which requires data of covid-19 cases. I will explore data from N3C data enclave and report both descriptive statistics and predictive modelling.", "accessing_institution": "Duke University" }, { "uid": "RP-9B91DE", "title": "COVID-19 Vaccination and Opioid Use Disorder in Rural Populations", "task_team": false, "dur_project_id": "DUR-962E6BC", "workspace_status": "CLOSED", "lead_investigator": "Meghan Farrington", "research_statement": "People with opioid use disorder (OUD) are more likely to be vulnerable to COVID-19 infection and having severe complications, due to a variety of social and biological factors. We propose to leverage COVID-19 testing and diagnosis information, OUD diagnoses, and rural residence information from the level three National COVID Cohort Collaborative (N3C) database. Our overall objective is to assess the relationship between OUD and COVID-19 infection for the rural population in our service area (Vermont, New Hampshire, Maine, and Northern New York). We will assess the following research questions: 1) Is there a difference in COVID-19 infection rates among rural patients with OUD compared to rural patients without OUD in our service area? , and 2) Among patients in our service area with COVID-19 infection, is COVID-19 more severe for rural patients with OUD compared to rural patients without OUD?", "accessing_institution": "University of Vermont" }, { "uid": "RP-154F98", "title": "The impact of vaccination and antiviral treatment on post-operative complications following total joint arthroplasty in Covid+ patients", "task_team": false, "dur_project_id": "DUR-964C066", "workspace_status": "ACTIVE", "lead_investigator": "Jami Pincavitch", "research_statement": " In this study, we hypothesize that receipt of a full Covid-19 vaccine series and/or Covid-19 treatment with an antiviral agent will mitigate the increased risk of post-operative mortality and complications.", "accessing_institution": "West Virginia University" }, { "uid": "RP-082E9A", "title": "Data standardization and characterization analysis with clinical use cases relating multiple data types and fields", "task_team": false, "dur_project_id": "DUR-9967D77", "workspace_status": "ACTIVE", "lead_investigator": "Craig Mayer", "research_statement": "De-identified data will be used to analyze the amount and density of clinical events present in the dataset. The goal of the research is to understand changes in the rate of occurrence of certain conditions, procedures and other clinical events as it relates to a COVID-19 infection. This is in order to see if there is a discernable increase in certain clinical events that may be related to COVID-19 infections and understand how this compares to similar analysis done on other large scale datasets.", "accessing_institution": "United States National Library of Medicine" }, { "uid": "RP-96DDC8", "title": "cholangiopathy in COVID-19 patients ", "task_team": false, "dur_project_id": "DUR-9A9C6C0", "workspace_status": "CLOSED", "lead_investigator": "Howard Chung", "research_statement": "This project will focus on cholangiopathy in COVID-19 patients. By using the NCATS N3C Data Enclave, we will analyze the incidence of secondary sclerosing cholangitis/ischemic cholangitis in critically ill COVID-19 patients. We will need the liver enzyme levels throughout the hospitalization, and positive MRCP results from all the patients without any previous liver disease. We will compare the trend of liver enzymes, length of stay in the hospital, prognosis between COVID-19 patients with and without cholangiopathy.", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-10ECEB", "title": "Incidence of Postpartum Depression in Adolescents Delivering during the COVID-19 Pandemic and Impact of COVID-19 Diagnosis on Postpartum Depression in Adolescents", "task_team": false, "dur_project_id": "DUR-A505283", "workspace_status": "CLOSED", "lead_investigator": "Lejia Hu", "research_statement": "Over the past several years, the COVID-19 pandemic has showcased that a patient's mental health is as critical in their care as their physical health. This is especially true for child and adolescent patient populations, as the pandemic highlighted their vulnerability to mental health stress due to a lack of developed coping strategies and understanding. Postpartum depression is the most common psychiatric disorder affecting childbirth, and postpartum depression in adolescents remains relatively poorly understood and poorly researched. This study aims to investigate the incidence of postpartum depression in adolescents who gave birth during the COVID-19 pandemic. It also aims to understand if adolescents who tested positive for COVID-19 have an increased incidence of postpartum depression than peers who did not test positive for COVID-19. ", "accessing_institution": "Boston Strategic Partners Inc" }, { "uid": "RP-365847", "title": "Does gastric anatomy change from bariatric surgery affect COVID-19 prognosis?", "task_team": false, "dur_project_id": "DUR-A90BB5A", "workspace_status": "CLOSED", "lead_investigator": "Beishi Zheng", "research_statement": "Patients who had previous bariatric surgery are prone to have multiple nutritional deficiency, including thiamine, vitamin B12, folate, iron, vitamin D and calcium, fat-soluble vitamins (A, E, K), zinc and copper, some of those were proved to be related to immune system. Although with supplementation, a lot of patients still have deficiency with some of those nutrition which can lead them immunocompromised. Bariatric patients usually have previous history of hypertension, diabetes, hyperlipidemia and multiple obese related comorbidities, so they are more fragile to stressful situation, such as COVID-19. Many post- bariatric patients are also on PPI which was found having increasing risk to develop severe COVID. What?s more, those patients are prone to have GI bleeding due to gastrointestinal system anatomy which is one of the common complications of COVID. This study aims to assess if patients with previous bariatric surgery is a risk factor to get severe COVID infection and poor prognosis. ", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-81BDD0", "title": "Major Adverse Cardiovascular Events and All-Cause Mortality in Patients with Inflammatory Bowel Disease", "task_team": false, "dur_project_id": "DUR-AA4923C", "workspace_status": "CLOSED", "lead_investigator": "Alfred Anzalone", "research_statement": "This project seeks to investigate the risk of major cardiovascular events (MACE) and all-cause mortality in patients with inflammatory bowel disease (IBD) who are infected with SARS-CoV-2 compared to patients with SARS-CoV-2 infection only. The utility of this analysis is to determine the impact single, dual and triple immunosuppressive therapy has on outcomes of IBD patients with COVID-19 as limited data exists on this patient population. Preliminary evidence suggest an increased risk of MACE and all-cause mortality among patients with COVID-19. We will also look at incidence of long COVID and post-COVID conditions among those with and without IBD.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-0CB005", "title": "Wearable sensors and EHR data for monitoring long COVID", "task_team": false, "dur_project_id": "DUR-AD72EC9", "workspace_status": "CLOSED", "lead_investigator": "Kalyani Kottilil", "research_statement": "In this work, we adopt existing machine learning techniques for the identification of individuals with long COVID from EHR, and we investigate the relationships with the previously observed long COVID effects on physiological parameters, including a transient bradycardia followed by a prolonged tachycardia, which was observed in a previous study, with different degrees of intensity. We hypothesize that length and intensity of digital biomarkers changes are related to disease severity.", "accessing_institution": "The Scripps Research Institute" }, { "uid": "RP-2964CD", "title": "Thromboembolic risk for people with COVID-19 receiving estrogenic therapies versus non-users: A comparative analysis", "task_team": false, "dur_project_id": "DUR-AD97502", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "This research project aims to conduct a comparative analysis of thromboembolic risk in COVID-19 patients who are receiving estrogenic therapies versus those who are not. By utilizing data from the N3C enclave, we will evaluate the incidence of thromboembolic events during and shortly after COVID-19 infection in both groups. The study will employ statistical and machine learning techniques to identify any elevated risk of thrombotic events associated with the use of estrogen-containing drugs. The outcomes of this research will provide critical insights into the safety and risks of estrogenic therapies in the context of COVID-19, guiding clinical decision-making and patient management strategies.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-BA8F07", "title": "Prediction of Pediatric ICU Outcomes & Complications Associated with COVID-19", "task_team": false, "dur_project_id": "DUR-AE6EDED", "workspace_status": "CLOSED", "lead_investigator": "Thomas Fogarty III", "research_statement": "This project will leverage de-identified data in the N3C to evaluate outcomes and complications associated with COVID-19 in pediatric patients.", "accessing_institution": "Baylor College of Medicine" }, { "uid": "RP-EAF1E6", "title": "COVID-19 severity, prevention, and treatment using N3C data", "task_team": false, "dur_project_id": "DUR-B2CCA42", "workspace_status": "ACTIVE", "lead_investigator": "Margaret Lind", "research_statement": "This project aims to assess the safety and effectiveness of COVID-19 prevention and treatment options using the N3C database, with a particular emphasis on antivirals and vaccinations. The analysis will encompass both the general population and high-risk groups, including pregnant individuals. A key objective is to provide insights into vaccine timelines and explore the associations between vaccination, treatment, and chronic outcomes such as Guillain-Barré Syndrome (GBS) and myocardial infarctions (MIs).", "accessing_institution": "Boston University" }, { "uid": "RP-C60002", "title": "Identifying neuroprotective drugs for reducing long-term cognitive effects of COVID-19", "task_team": false, "dur_project_id": "DUR-C143171", "workspace_status": "ACTIVE", "lead_investigator": "Li Li", "research_statement": "Scientific Goals: To identify existing drugs as potential candidates for repurposing for prophylactic treatment of cognitive symptoms of long COVID. Secondary outcomes include estimation of the prevalence and severity of neurological symptoms of long COVID, as well as their relation to severity of the acute phase of the disease.\n\nObjectives, designs, and plans: The COVID-19 pandemic has had a substantial impact on long-term public health. Recent studies have raised significant concern for long-term adverse neurological and cognitive symptoms following COVID-19 infection, including brain fog, dizziness, headaches, anxiety and sleep disorders. These long-term symptoms may result from symptoms during the acute phase of the disease, and therefore may be reduced by repurposing existing drugs for prophylactic treatment. The degree of neuroprotection for any given drug may depend on details specific to the patient (demographics, comorbidities, etc.) as well as severity of the disease. Therefore, large-scale EHR data, such as that made available through the N3C, is needed to build individualized treatment models. We will apply state-of-art machine learning methods to analyze neurological symptoms associated with COVID to assess long-term clinical outcomes of patients impacted by COVID-19 and identify existing treatments that may offer neuroprotective prophylaxis. \n", "accessing_institution": "Mount Sinai Genomics Inc" }, { "uid": "RP-2E214C", "title": "Data-driven Investigation of Health Disparities in COVID-19 Outcomes: A Focus on Behavioral Health", "task_team": false, "dur_project_id": "DUR-C1AEA06", "workspace_status": "CLOSED", "lead_investigator": "Hollis Karoly", "research_statement": "Our overall project aim is to explore identity and behavioral health predictors of COVID severity and disease course, including the impact of mental and physical health comorbidities on COVID outcomes. We will use machine learning approaches on this large dataset to improve our understanding of health disparities based on race, gender and ethnicity in a range of COVID-related and behavioral health outcomes. We will also incorporate specimen data?particularly specimens related to immune-function (e.g., blood cytokines and other immune markers). This project has translational significance because it will include biological, psychological, and social factors that affect COVID severity and disease course. We request access to level 2 individual-level data (HIPAA safe harbor data). Our research questions require individual rather than aggregate data. More specifically, using machine learning and multilevel analyses we will be able to model nuances in the individual-level data that would not be possible with synthetic data. Analyses of these data will provide critical insights to push the field forward that would not be possible with synthetic data.", "accessing_institution": "Colorado State University" }, { "uid": "RP-89050B", "title": "Development of Quality Measures to Assess Performance of Hospitals Across Care settings and Providers Caring for Medicare Beneficiaries", "task_team": false, "dur_project_id": "DUR-C572512", "workspace_status": "ACTIVE", "lead_investigator": "James Harris", "research_statement": "As the complexity of patient care and provider workflows continues to evolve in the United States due to various quality reporting program requirements, value-based care agreements, and so forth, it is imperative that new electronic clinical quality measures (eCQMs) are developed to help alleviate burden and improve patient care. Specifically, eCQMs focused on clinical process and effectiveness, health equity and outcomes, patient safety, care coordination, population and public health, as well as efficient use of healthcare resources are all \"top of mind\" across healthcare institutions, payer groups, government entities, and others involved in the quality measurement process. Therefore, this project seeks to assess the feasibility of developing innovative, publicly reported performance-based eCQMs through rigorous data element availability analyses as well as multi-phased testing approaches utilizing a publicly available data set, and will initially focus on the Medicare beneficiary population.", "accessing_institution": "Yale University" }, { "uid": "RP-64AC71", "title": "Modeling of COVID-19 Risks for Back-to-Work Programs", "task_team": false, "dur_project_id": "DUR-C57918D", "workspace_status": "CLOSED", "lead_investigator": "Li Li", "research_statement": "Scientific Goals: To develop a modeling framework that informs decisions for sustainable, lower cost, and effective testing strategies during the COVID-19 post-lockdown era through leveraging information from various scales. The N3C limited Data Set (N3CLDS) will be used to estimate the risk of contracting/transmitting COVID-19.\n\nObjectives, designs, and plans: Given the prolonged pandemic, many companies/schools have started to implement programs involving COVID-19 testing, symptom reporting, and contact tracing to safely bring people back to work/school. However, the programs mainly rely on passive reporting and universal testing schedules without considering the heterogeneity in a workforce. Models that can actively guide the logistics of testing is essential to control the disease transmission effectively. We performed a pilot study on risk factors of COVID-19 infection from EMR data at Mount Sinai Health System. However, the study population was limited to NYC area. To obtain more accurate and generalizable estimates of an individual?s risk of contracting and transmitting COVID-19, data from N3CLDS are essential given the comprehensive information on individual-level risk factors with the wide geographical coverage. We propose a Bayesian hierarchical model to incorporate data from individual and community levels. In turn, this estimated risk can serve as an indicator for individuals who might need more frequent testing. Additionally, the model can be expanded to include a network-based transmission model to integrate contact network structures where necessary. \n", "accessing_institution": "Mount Sinai Genomics Inc" }, { "uid": "RP-E7E050", "title": "Examining the Efficacy of Pharmacological Treatments for Alleviating Brain Fog in Long COVID Patients: A National COVID Cohort Collaborative (N3C) Database Analysis", "task_team": false, "dur_project_id": "DUR-C94291E", "workspace_status": "ACTIVE", "lead_investigator": "Patrick Dib", "research_statement": "Long COVID, a persistent condition following SARS-CoV-2 infection, frequently manifests cognitive impairment termed \"brain fog.\" The aim of this study is to examine and compare the efficacy of diverse pharmacological treatments in mitigating cognitive impairments in long covid patients by utilizing data from the National COVID Cohort Collaborative (N3C) database. This study will employ a retrospective analysis of longitudinal data from the N3C database with emphasis on long covid patients presenting with ?brain fog? symptoms. This will be accomplished by utilizing advanced statistical methodologies to analyze the association between various pharmacological treatments and the improvement of cognitive function while considering demographic variables and comorbidities as covariates.", "accessing_institution": "The Ohio State University" }, { "uid": "RP-CE71E0", "title": "Incidence and outcomes of COVID-19 infection in patients with chronic liver disease", "task_team": false, "dur_project_id": "DUR-C9CE539", "workspace_status": "CLOSED", "lead_investigator": "Armaghan-e-Rehman Mansoor", "research_statement": "In our study, we wish to compare outcomes of COVID-19 infection in patients with and without chronic liver disease. Subgroup analyses will be conducted to compare these outcomes in patients with viral hepatitides B, C, alcoholic liver disease and non-alcoholic fatty liver disease (NASH), with and without cirrhosis. The effect of different antiviral treatments for Hepatitis B or C in patients with viral hepatitis on clinical course of COVID-19 will also be explored. We also aim to look at predictors of severe disease in this cohort. ", "accessing_institution": "West Virginia University" }, { "uid": "RP-CC3D82", "title": "Factors affecting risk of poor outcomes for COVID-19 for patients with diabetes", "task_team": false, "dur_project_id": "DUR-CE3CCDA", "workspace_status": "CLOSED", "lead_investigator": "Ashok Krishnamurthy", "research_statement": "Does the level of glycemic control before COVID-19 affect the risk for poor outcomes? If the answer is yes, we should be working to get people better control now, perhaps. Or is the risk related to age, obesity, non-white race, Hispanic ethnicity, heart disease, kidney disease, hypertension, poverty etc., all of which are\nincreased in patients with diabetes.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-C33543", "title": "Causal comparative effectiveness analysis of COVID-19 treatment strategies", "task_team": false, "dur_project_id": "DUR-D23B539", "workspace_status": "CLOSED", "lead_investigator": "Liangyuan Hu", "research_statement": "The COVID-19 pandemic is an evolving crisis threatening global health and economies. Public health experts believe that this pandemic has no true precedent in modern times. To this date, optimal treatment strategies, including treatment classes (e.g., anticoagulant drugs, anti-inflammatory drugs, corticosteroids, etc.) and treatment timing are still unclear. Several national and international randomized controlled trials (RCT) are being conducted to compare the effect of one or two treatment strategies. It is infeasible, however, especially in a time of crisis, to conduct RCT to investigate all possible treatment strategies. In addition, RCT inclusion and exclusion criteria often limit generalizability to frail populations, precisely the demographic most at risk of severe morbidity and mortality. \n\nWe aim to leverage the continuously growing COVID-19 data collected across the nation to validate and compliment the randomized trials. We will conduct in-depth investigations of the comparative effectiveness of multiple treatment options and the treatment interactions therein (as many patients take more than one type of medications at the same time) in real-world settings. Furthermore, research is needed to understand heterogeneity in effectiveness of COVID-19 treatments in vulnerable subgroups of patients. The most vulnerable patients include frail elderly, disadvantaged racial groups, and those who have immune suppression, like patients with cancer, HIV, organ transplant, or users of immunosuppressing medications. This project has two aims. In Aim 1, we will develop i) a continuous-time joint marginal structural model and ii) a Bayesian machine learning based methods to evaluate the causal comparative effectiveness of multiple treatment strategies. We will stratify our analyses for pre-ICU and post-ICU patients. In Aim 2, we will develop a data-driven method to estimate the treatment effect heterogeneity, particularly in vulnerable subpopulations. \n\n", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-58D94E", "title": "Automated exploratory subgroup analysis to identify vulnerable subgroups and examine heterogeneous treatment effects of interventions in COVID-19", "task_team": false, "dur_project_id": "DUR-D753E59", "workspace_status": "CLOSED", "lead_investigator": "AMOL RAJMANE", "research_statement": "The overarching goal of this research is to evaluate variations of care associated with Covid-19 outcomes and related interventions. Our specific objectives are threefold. First, we aim to identify subgroups (possibly spanning an arbitrary number of features including patient history and co-morbidities) that have much higher rates of adverse events (critical disease, mortality), as compared to the average rate across all sampled Covid-19 patients. Second, we aim to examine the heterogeneous treatment effects of different Covid-19 treatment strategies to identify the subgroups of Covid-19 patients whose outcomes are most impacted by the treatments. Third, we aim at demonstrating how the subgroup insights can be used to improve the prediction and granularity of dynamical Covid-19 disease models.\n\nSubgroup analyses are common techniques that can be used to identify non-random variations of care among Covid-19 patients. However, to conduct subgroup analyses, researchers often have to specify their objectives and reasons for subgroup analysis a priori, define the subgrouping variables upfront, and correct for the multiple comparisons problem. Unfortunately, conventional approaches for conducting subgroup analyses require domain expertise and, more importantly, limit researchers to analyzing only a few variables, beyond which it becomes computationally infeasible. Furthermore, these approaches lack a ?data-driven knowledge discovery? aspect as investigators must suggest a priori which subgrouping variables they would like to investigate. Our methodological goal is to address the limitations of conventional subgroup analysis approaches by extending subset scanning techniques from the anomalous pattern detection literature to support insight discovery through an automated exploratory subgroup analysis approach.\n", "accessing_institution": "IBM" }, { "uid": "RP-16CAA3", "title": "The Use of Extracorporeal Membrane Oxygenation in Patients with COVID-19 in the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-D9A1067", "workspace_status": "CLOSED", "lead_investigator": "Adeel Abbasi", "research_statement": "The SARS-CoV-2 pandemic has been marked by severe acute respiratory distress syndrome (ARDS) in approximately 20% of patients.1 A number will require end-organ support with extracorporeal membrane oxygenation (ECMO).2,3 To-date over 15,000 patients with COVID-19 treated with ECMO have been reported to the Extracorporeal Life Support Organization. Hemorrhage and thrombosis are the most significant causes of morbidity and mortality during ECMO.4 Half of patients will suffer major hemorrhage and approximately 12% will have major thrombotic complications.5 Stroke and intracerebral hemorrhage are the most morbid of these complications and are the leading cause of death during ECMO.6 Patients with severe COVID-19 can develop a hypercoagulable state with an increased risk of thrombotic complications.7-10 Thus, patients with severe COVID-19 associated ARDS treated with ECMO may inherently have a higher risk of complications because of the hypercoagulable COVID-19 phenotype.11,12\n\nWe aim to describe the use of ECMO in patients hospitalized with severe COVID-19 associated ARDS within the multi-center National COVID Cohort Collaborative (N3C), and compare the rate of hemorrhagic and thrombotic complications in patients with and without COVID-19 treated with ECMO. ", "accessing_institution": "Brown University" }, { "uid": "RP-45BA39", "title": "Receipt of palliative care at end-of-life among COVID-19 patients with intellectual disabilities", "task_team": false, "dur_project_id": "DUR-DAECC2D", "workspace_status": "CLOSED", "lead_investigator": "Martin Viola", "research_statement": "This project will use synthetic data to investigate differences in receipt of palliative care among inpatients with and without intellectual disability being treated for SARS-CoV-2 infection who died during their terminal hospital stay. ", "accessing_institution": "Weill Cornell Medicine" }, { "uid": "RP-9E54D4", "title": "Risk factors, care disparities and outcomes for gastrointestinal hemorrhage in patients with COVID-19 infection ", "task_team": false, "dur_project_id": "DUR-DE10059", "workspace_status": "CLOSED", "lead_investigator": "Armaghan-e-Rehman Mansoor", "research_statement": "Our project aims to determine the prevalence of gastrointestinal hemorrhage in patients with COVID-19 infection. We also aim to compare treatment pathways and outcomes in patients with or without COVID-19 infection who present with gastrointestinal hemorrhage. Our secondary aim is to further explore the risk factors for gastrointestinal hemorrhage in patients with COVID-19 infection. Lastly, the treatment of gastrointestinal hemorrhage involves potentially aerosol-generating procedures such as endoscopic evaluation. Therefore, we wish to explore the concern that these procedures may be used less frequently in patients with COVID-19 infection.", "accessing_institution": "West Virginia University" }, { "uid": "RP-7EEEB5", "title": "The effect of Paxlovid on the acute and late outcome of Covid-19 infection regarding the vaccination status ", "task_team": false, "dur_project_id": "DUR-094C97A", "workspace_status": "CLOSED", "lead_investigator": "Mahnaz Derakhshan", "research_statement": "This project aims to define the effect of vaccination status on the outcome of antiviral therapy during acute Covid-19 infection using the N3C deidentified Data Set. The efficacy of antiviral treatment in reducing hospitalisation or death from COVID-19 infection in unvaccinated patients has been demonstrated in the EPIC-HR study. In this project, we want to know whether the effect of this medicine is different in vaccinated and unvaccinated populations. The result will provide vital information for defining health protocols and guidelines. ", "accessing_institution": "Conovita Technologies Inc" }, { "uid": "RP-0E6026", "title": "Post-Acute Sequelae of COVID-19 (PASC) amongst patients who were at high and low risk of developing severe acute COVID-19 at time of infection ", "task_team": false, "dur_project_id": "DUR-DE15EBA", "workspace_status": "CLOSED", "lead_investigator": "Vishal Patel", "research_statement": "Post-Acute Sequelae of COVID-19 (PASC), also referred to as long COVID or post-COVID syndrome, is a rapidly evolving field of research, with a wide range of symptoms now being associated with the post-acute phase of COVID-19. Recent evidence also indicates that these persisting symptoms have an impact on patients? health related quality of life. \n\nA recent meta-analysis concluded that approximately 80% of individuals who had had a diagnosis of confirmed COVID-19 continued to have at least one symptom that persisted beyond two weeks post-acute infection, the most commonly reported of which were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%) and dyspnoea (24%). There is a need to understand to what level these patient-reported symptoms manifest into increased healthcare encounters. \n\nElderly patients (typically over 55 years of age) and those with high-risk comorbidities (e.g. diabetes mellitus, cardiovascular disease, immunosuppressive disorders etc.) who become infected with SARS-CoV-2 are most at risk of developing severe acute COVID-19. However, little research has been undertaken on the risk and characteristics of PASC amongst these high-risk patients in comparison to patients at low risk of developing severe acute COVID-19. \n\nIn this study we propose to characterize the prevalence of PASC, overall and between patients at high and low risk of developing severe COVID-19. As an exploratory analysis, we will assess what associations may exist between characteristics of acute COVID-19 (e.g. viral load, SARS CoV-2 variant, maximal respiratory support required, inpatient hospitalization, ICU admission, treatments received, patient vaccination status etc.) and the prevalence of PASC, overall and by specific symptoms/diagnoses. \n\nThis study will require access to the N3C de-identified dataset.", "accessing_institution": "GLAXOSMITHKLINE LLC" }, { "uid": "RP-DD71E5", "title": "Impact of pre-existing comorbidity and antioxidant or anti-inflammatory use on COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-E8081BE", "workspace_status": "CLOSED", "lead_investigator": "Alice Ceacareanu", "research_statement": "We hypothesize that COVID-19 symptoms severity, disease course, and treatment outcomes are strongly influenced by the combined effect of pre-existing comorbidities and the pharmacodynamics of antioxidant and anti-inflammatory treatments received by a patient. This interaction may further impact clinical changes and events occurring post-recovery. We aim to characterize the clinical complexity of the high- and intermediate-risk patients by focusing on the pathophysiology of their pre-existing comorbidities and on the mechanism of action of the pharmacotherapy received. We will use machine learning methods and domain knowledge driven longitudinal causal inference methods to reveal clinically meaningful insights to patient characteristics and treatment modalities. Model robustness will be evaluated with both re-sampling methods and known negative control relationships. ", "accessing_institution": "Hatwick College" }, { "uid": "RP-18AB54", "title": "CPAP usage association with morbidity and mortality among Covid patients", "task_team": false, "dur_project_id": "DUR-ED9AD44", "workspace_status": "ACTIVE", "lead_investigator": "Amit Saha", "research_statement": "Sleep apnea affects more than 3 in 10 men and nearly 1 in 5 women. The association between COPD as an obstructive lung disorder and OSA as a sleep breathing disorder means a person suffering from both has a compromised respiratory system that cannot even rely on sleeping at night for recovery or relief. Data demonstrates benefits of cpap among patients with Covid when applied early. We would like to find the optimal time window that leads to better patient outcomes of morbidity and mortality.", "accessing_institution": "Wake Forest University Health Sciences" }, { "uid": "RP-6CA09C", "title": "Studying the effect and the risk of adverse outcomes of COVID-19 infection in pediatric and adult patients using statistical and machine learning methods", "task_team": false, "dur_project_id": "DUR-00843A8", "workspace_status": "CLOSED", "lead_investigator": "Corneliu Antonescu", "research_statement": "In this research project we try to understand and predict which patients with COVID will need to be admitted to the hospital or will develop complications and need to be admitted to the Intensive Care Unit. To achieve the goal pf predicting which patients will need to be admitted to the hospital and the Intensive Care Unit we will test several machine learning algorithms, select the one that has the highest performance and describe it. Our machine learning algorithm should be able to help decide, based on factors like age, gender, preexisting medical conditions, measurements like blood pressure and heart rate and lab results, whether the patient will need to be admitted to the hospital or the intensive care or will not need to be admitted.", "accessing_institution": "University of Arizona" }, { "uid": "RP-796E27", "title": "Synthetic data for LACaTS", "task_team": false, "dur_project_id": "DUR-03DAEEB", "workspace_status": "ACTIVE", "lead_investigator": "San Chu", "research_statement": "This is a workspace to prepare the overview data for the investigators of Louisiana Clinical and Translation Science (LA CaTS) Center. Only synthetic data are involved.", "accessing_institution": "Pennington Biomedical Research Center" }, { "uid": "RP-D95B1A", "title": "Association between RAAS inhibitor class and COVID-19 outcomes. ", "task_team": false, "dur_project_id": "DUR-04E9286", "workspace_status": "CLOSED", "lead_investigator": "William Hillegass", "research_statement": "Previous studies and meta-analyses demonstrate an adjusted association between RAAS inhibitor background therapy and lower risk of mortality among patients hospitalized for COVID-19 disease. Using a propensity score matched analysis, we will examine the consistency of this association across angiotensin converting enzyme inhibitors, angiotensin receptor blockers, and mineralocorticoid receptor antagonists. Cohorts will be stratified by several baseline comorbidities and other medications.", "accessing_institution": "University of Mississippi Medical Center" }, { "uid": "RP-3D516B", "title": "[N3C Operational] Implementation of Syntegra Synthetic Data Generator?", "task_team": false, "dur_project_id": "DUR-07B3207", "workspace_status": "CLOSED", "lead_investigator": "Ofer Mendelevitch", "research_statement": "This project will leverage novel approaches to developing synthetic data to create data sets that can be shared with negligible risk of re-identifying subjects. We will implement Syntegra's synthetic data engine; generate COVID-19 synthetic patient records and evaluate statistical fidelity and privacy aspects.", "accessing_institution": "Syntegra" }, { "uid": "RP-FF7905", "title": "Exploring Differential Mortality and Healthcare Utilization in Lung Cancer Patients Hospitalized with COVID-19: The Role of Demographics", "task_team": false, "dur_project_id": "DUR-08D59B0", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "Lung cancer patients represent a particularly vulnerable population amidst the COVID-19 pandemic due to their compromised respiratory function and immunocompromised status. Understanding the interplay between lung cancer, COVID-19, and demographic factors is crucial for optimizing clinical management and reducing health disparities. This study aims to explore the differential mortality rates and healthcare utilization patterns among lung cancer patients hospitalized with COVID-19, focusing on the role of demographics.\n\nUsing data from the N3C enclave, health records of lung cancer patients admitted with COVID-19 will be analyzed using demographic variables including age, gender, race/ethnicity and socioeconomic status. Statistical analyses, including multivariable regression modeling and survival analysis, will be conducted to assess the impact of demographic factors on mortality rates and healthcare utilization among lung cancer patients with COVID-19. Subgroup analyses will explore potential disparities in outcomes across different demographic categories.\nFindings from this study will contribute to a nuanced understanding of the intersectionality between lung cancer, COVID-19, and demographic factors. By identifying disparities in mortality rates and healthcare utilization, this research aims to inform targeted interventions and healthcare policies to improve outcomes and reduce health inequities among lung cancer patients affected by COVID-19. Ultimately, this study seeks to enhance the quality of care and support provided to this vulnerable patient population during the ongoing pandemic.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-BBB544", "title": "Use of Evusheld and descriptive analysis of utilization", "task_team": false, "dur_project_id": "DUR-0D6E3E5", "workspace_status": "ACTIVE", "lead_investigator": "Dan Housman", "research_statement": "Retrospective study to examine the current pattern of use within the populations who are indicated for EVUSHELD use. EVUSHELD is an antibody treatment for immunocompromised and high risk patients that provides pre-exposure prophylactic protection against COVID-19. EVUSHELD has been approved for emergency use but not yet with a full marketing authorization from the FDA. The research will describe baseline characteristics of patients, COVID-19 exposure, and all-cause outcomes for the population of eligible patients expected to receive benefit from EVUSHELD as defined under the terms of authorization. The study will examine both patients who have and have not received EVUSHELD. Areas of analysis include the volume of use within the indicated populations, how EVUSHELD is being used including prescribing patterns, and to evaluate the feasibility to study advanced questions such as efficacy, variation relative to COVID strain, and use in post-exposure populations.", "accessing_institution": "Graticule" }, { "uid": "RP-27C5CE", "title": "Compare coronary artery bypass grafting and percutaneous coronary intervention using quantitative benefit-risk assessment in Covid patients", "task_team": false, "dur_project_id": "DUR-0ECEAD1", "workspace_status": "ACTIVE", "lead_investigator": "Lejia Hu", "research_statement": "In the ongoing debate over treatments for coronary artery disease (CAD), percutaneous coronary intervention (PCI) has been tested against coronary artery bypass grafting (CABG), the gold standard, with conflicting results. This study aims to compare the two operations by incorporating patients? perspectives to inform the assessment of benefit-risk balance in covid patients using real-world data.", "accessing_institution": "Boston Strategic Partners Inc" }, { "uid": "RP-2C64DA", "title": "AI-Assisted Assessment, Tracking, and Reporting of COVID-19 Severity on Chest CT", "task_team": false, "dur_project_id": "DUR-10D3CD9", "workspace_status": "CLOSED", "lead_investigator": "Asser Elkassem;Abou;Abou Elkassem", "research_statement": "COVID-19 is a severe infectious respiratory disease. In this pandemic, some patients with COVID-19 are at high risk of respiratory collapse and death, and there is a need to identify patients at high risk and to monitor therapy. Chest CT images contain information that can be used to monitor disease severity. However, current text-based reporting of chest CT findings in COVID-19 patients by radiologists are subjective, highly variable between radiologists, inefficient to generate and interpret, prone to interpretation errors, frequently omit key findings, and do not provide quantitative data in a standardized format. In patients with COVID-19, there are research methods to assess the percentage of lung involvement of COVID-19 on chest CT images that predict disease severity. However, this method requires a complex, manual workflow to complete and is subject to errors, omissions, and high inter-observer variability. Collectively, the lack of consistent data hinders disease profiling by epidemiologists and public health officials. This project proposes to research and develop an artificial intelligence (AI)-assisted COVID-19 CT imaging workflow for radiologists to rapidly and objectively quantify the percentage of lung involvement, track common and uncommon COVID-19 lung findings, and automatically generate reports with a graph, key images, and structured text. To perform algorithm training, we are requesting the Level 2, de-identified N3C data set. We hypothesize that the proposed AI-assisted workflow will reduce interpretation errors and omissions and improve accuracy, standardization, inter-observer agreement, efficiency, and reporting in evaluation of COVID-19 disease severity and response to treatment.", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-AC04EA", "title": "Gastroenterological Manifestations of COVID-19 and Clinical Outcomes ", "task_team": false, "dur_project_id": "DUR-1151BDD", "workspace_status": "CLOSED", "lead_investigator": "William Hillegass", "research_statement": "The SARS-CoV-2 enters the human gastrointestinal tract via the ACE-2 receptors which are predominantly expressed within the gastrointestinal tract, from gastric to colonic tissue. The pathophysiology of the SARS-CoV-2 infection on the gastrointestinal tract is commonly manifested as anorexia, nausea, vomiting, and diarrhea in COVID-19 patients. These clinical manifestations usually (have been reported to) occur in combination with respiratory symptoms in 50% of the affected patients and independently without respiratory symptoms in 10% of patients. Patients also present with abnormalities of pancreatic and hepatic enzymes. Whether COVID presentation including gastrointestinal symptoms portends worse outcomes remains poorly undefined. The project will examine whether COVID clinical presentation with gastrointestinal symptoms is associated with ICU length of stay, total length of hospitalization, and inpatient survival. The relationship between gastrointestinal symptoms at clinical presentation and other known or candidate risk factors for severity of illness and poor outcomes such as obesity, diabetes mellitus, and hypertension will also be examined. ", "accessing_institution": "University of Mississippi Medical Center" }, { "uid": "RP-B53CED", "title": "Predicting patient severity and developing comprehensive package of interventions", "task_team": false, "dur_project_id": "DUR-11C2C28", "workspace_status": "CLOSED", "lead_investigator": "Jinha Lee", "research_statement": "The 2019 novel coronavirus (COVID-19) has been accompanied by severe economic, social, and healthcare disruptions. In contrast to other viruses such as influenza, SARS and MERS, COVID-19 pandemic has caused crisis in a number of ways, including uncertainties in treatments and interventions as well as the effectiveness of responses from the point of view of public health system. The focus of this project is to apply machine-learning based algorithm to: 1) identify clinical characteristics of patients associated with severity levels 2) predict trajectory of patient severity(asymptomatic, mildly symptomatic and severe symptomatic) based on clinical characteristics; and 3) provide clinical course and comprehensive package of interventions for each trajectory of patient severity. Machine-learning based prediction will be used for front-liner?s effective clinical decision making as well as preparing resources and reducing burden in care delivery from health system. ", "accessing_institution": "Bowling Green State University" }, { "uid": "RP-A2B309", "title": "Pediatric Community Challenge - Organizers", "task_team": false, "dur_project_id": "DUR-124E82B", "workspace_status": "CLOSED", "lead_investigator": "Timothy Bergquist", "research_statement": "Children with COVID-19 are at risk for severe clinical outcomes including hospitalization, acute COVID, and Multisystem Inflammatory Syndrome in Children (MIS-C). Predictive methods are needed to identify children who are at risk for severe COVID symptoms and to identify sub-phenotypes of children who are at risk for poor health outcomes. To that end, we are proposing to conduct a community challenge within the National COVID Cohort Collaborative (N3C) enclave to engage with the machine learning community to develop risk prediction models for identifying children who are at risk for severe COVID symptoms. We will establish a gold standard true positive dataset against which risk prediction models will be benchmarked.\n\nUsing N3C data, challenge organizers will identify viable challenge questions focused on prediction of complications for pediatric COVID patients. Participants in this challenge will build models on a training dataset established by the challenge organizers. Those trained models will then be tested on a holdout set to establish initial model accuracy. Models will then be put through a ?final training?. These trained models will be evaluated against a battery of accuracy and generalizability tests including longitudinal generalizability, cross-site generalizability, hold-out dataset accuracy, and prospective evaluations.\n", "accessing_institution": "Sage Bionetworks" }, { "uid": "RP-064700", "title": "Examining the mediating effects of cannabis on COVID-19 outcomes in adults with cancer", "task_team": false, "dur_project_id": "DUR-128D512", "workspace_status": "ACTIVE", "lead_investigator": "Manan Nayak", "research_statement": "One in four cancer patients turns to cannabis for physical and mental health symptoms and cancer control. Oncologic cannabis medicating (CM) increased substantially during COVID-19. While in the general population, CM was linked to improved COVID-19 outcomes (possibly due to immune modulation), CM worsened COVID-19 outcomes in oncology. Oncologists acknowledge both discussing cannabis clinically and lacking knowledge to do so. Understanding cannabis? unique oncologic risk profile is thus of critical importance. This project aims to 1) characterize the prevalence nationally of CM and COVID-19 infections in oncology; 2) identify associations between CM and demographic, clinical, and social determinants of health characteristics; 3) develop good algorithmic practices that predict/classify effects of CM on COVID-19 severity in oncology. Findings from this study will help to inform patient-centered oncologic cannabis-related care?particularly in designing educational interventions for providers and patients. ", "accessing_institution": "Harvard University" }, { "uid": "RP-8CBA09", "title": "COVID-19 testing frequency", "task_team": false, "dur_project_id": "DUR-16AD66C", "workspace_status": "CLOSED", "lead_investigator": "Sumi Singh", "research_statement": "This is an exploratory project to identify markers in Synthetic Data that can be used for predicting COVID-19 testing frequency. ", "accessing_institution": "Citizen Scientist" }, { "uid": "RP-9B5B97", "title": "COVID-AKI", "task_team": false, "dur_project_id": "DUR-1BBE618", "workspace_status": "CLOSED", "lead_investigator": "Jianqiu Zhang", "research_statement": "To identify health outcomes of patients with AKI in the N3C cohort, and to identify clinical risk factors, drug usage, treatment interventions, and patient characteristics (demographic, social, behavioral, and familial factors) that may influence the development and progression of AKI. The research also seeks to ascertain potential predictive and preventative factors for disease progression along with.", "accessing_institution": "University of Minnesota" }, { "uid": "RP-0EB221", "title": "The effect of symptomatic Covid-19 Infection on Thrombotic events in Women of Reproductive Age: a retrospective cohort review", "task_team": false, "dur_project_id": "DUR-1DA20DA", "workspace_status": "CLOSED", "lead_investigator": "Brian Hendricks", "research_statement": "Diagnosis of Severe Acute Respiratory Syndrome coronavirus 2, SARS-Cov-2, has spread into a world-wide pandemic at an unprecedented rate. Little is known concerning adverse events of a covid-19 diagnosis. With increased clotting factors, thrombotic events may be more likely. The interaction of SARS-Cov-2 and prescription birth control are unknown. However, hormonal birth control can cause thrombosis in high risk patients. (5) With the introduction of Covid-19 into this population, can we accurately determine an increase in thrombotic events? \n\nThrombotic events leading to pulmonary embolism have been reported in 31% of patients in an ICU with a Covid-19 diagnosis. (1) Patients entering the ICU with a Covid-19 diagnosis usually have comorbid conditions. The health of the population in an ICU differs from that of a non-hospitalized population. However, if thrombotic events are occurring at a higher rate in this population, pregnancy outcomes may be effected. Covid-19 infection increases systemic inflammation with high levels of C-Reactive protein and ferritin. (2) Inflammation in the body can lead to a clotting event; thus, the thrombotic events in women of reproductive age can jeopardize the viability of pregnancy. \n\nLimited data is available of Covid-19 as a causal effect of miscarriages. However, multiple letters to the editors have outlined suspicion of covid-19 infection contributing to miscarriage. Hachem et. al. found a late term miscarriage in a women who presented with vaginal bleeding and otherwise healthy pregnancy. The woman had a RT-PCR positive Covid-19 Result, and increased WBC levels and c- reactive protein. (3) Similarly, a first trimester miscarriage was reported in a woman who was diagnosed with Covid-19 at 10 weeks after substantial exposure. (4) \n\nKlok, F. A., Kruip, M. J. H. A., Van der Meer, N. J. M., Arbous, M. S., Gommers, D. A. M. P. J., Kant, K. M., ... & Endeman, H. (2020). Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thrombosis research, 191, 145-147.\nBoggess, K. A., Lieff, S., Murtha, A. P., Moss, K., Jared, H., Beck, J., & Offenbacher, S. (2005). Maternal serum C-reactive protein concentration early in pregnancy and subsequent pregnancy loss. American journal of perinatology, 22(06), 299-304.\nHachem, R., Markou, G. A., Veluppillai, C., & Poncelet, C. (2020). Late miscarriage as a presenting manifestation of COVID-19. European Journal of Obstetrics and Gynecology and Reproductive Biology, 252, 614.\nWong, T. C., Lee, Z. Y., Sia, T. L., Chang, A. K., & Chua, H. H. (2020). Miscarriage risk in COVID-19 infection. SN Comprehensive Clinical Medicine, 2(9), 1449-1452.\nGray, B., Floyd, S., & James, A. H. (2018). Contraceptive management for women who are at high risk of thrombosis. Clinical obstetrics and gynecology, 61(2), 243-249\n\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-426624", "title": "[N3C Operational] Education Resources Development for Data Science", "task_team": false, "dur_project_id": "DUR-1E57DDC", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The [N3C Operational] Education Resources Development for Data Science is a collaborative workspace for multi-disciplinary teams to create, and test educational material for teaching data science. Access to the DUR workspace is limited to educators and will not be accessible to students or used for teaching. Educational resources being developed include a wide variety of formats including traditional material such as slides, text, as well as project-based learning assignments that require students have access to other DURs ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-12CA7E", "title": "Neurological Consequences of COVID-19 Associations with Dementia, Alzheimer?s, and Long-Term Outcomes", "task_team": false, "dur_project_id": "DUR-21611A1", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "Neurological disorders, particularly dementia and Alzheimer?s disease (AD), have been identified as significant risk factors for severe COVID-19 outcomes. Emerging evidence suggests that COVID-19 infection may accelerate cognitive decline, increase dementia incidence, and contribute to long-term functional impairment in patients with pre-existing neurological conditions. Understanding these associations is critical for improving post-COVID-19 care and developing targeted interventions for at-risk populations.\nThis study aims to investigate the long-term neurological effects of COVID-19 on individuals with pre-existing neurological disorders, including Alzheimer?s disease, vascular dementia, and other cognitive impairments. \nUtilizing the de-identified electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C), this research will provide valuable insights into how COVID-19 impacts long-term neurological health. The findings will inform clinical guidelines and policy recommendations to enhance post-COVID care for vulnerable populations.\n", "accessing_institution": "login.gov" }, { "uid": "RP-932FC0", "title": "Optimizing Extubation Strategies in ICU Patients", "task_team": false, "dur_project_id": "DUR-2229DD0", "workspace_status": "ACTIVE", "lead_investigator": "Christopher Capone", "research_statement": "Background:\nPatients in critical care settings, such as the ICU, require continuous monitoring and timely clinical treatment decisions to stabilize vitals and mitigate risks of adverse outcomes. The COVID-19 pandemic underscored the severe strain on healthcare systems, with ICU resources stretched beyond capacity, compounded by a global shortage of intensivists. In this dynamic, high-stakes environment, clinicians must make sequential decisions based on limited patient information, often under significant uncertainty. The increasing frequency and severity of pandemics, driven by climate change and urbanization, further highlight the need for scalable, data-driven approaches to optimize clinical decision-making.\n\nObjective:\nThis study aims to develop a reinforcement learning (RL)-based framework to optimize extubation strategies for mechanically ventilated ICU patients, with specific focus on predicting \"time-to-extubation\" readiness and personalizing sedation regimens during ventilator weaning. These models will leverage state-action-reward mechanisms to improve clinical outcomes and resource allocation while reducing clinician cognitive load.\n\nMethods:\nWe will utilize public and clinical datasets, including MIMIC-IV, Mount Sinai MSDW, and N3C, to train and validate our RL models. Patients with Acute Hypoxemic Respiratory Failure (AHRF) meeting specific inclusion criteria will form the primary cohort. Two RL approaches will be explored: Conserved Q Learning (CQL) for its conservative offline learning capabilities, and Decision Transformers (DTs) for leveraging trajectory-based learning. A tiered reward system will prioritize both short-term patient stabilization and long-term outcomes, such as successful extubation and post-ICU survival.\n\nTo evaluate the effectiveness and generalizability of these models, we will:\n\nTrain on MIMIC-IV for initial policy learning.\nTest on the Mount Sinai MSDW cohort to assess clinical applicability.\nValidate using the N3C cohort to ensure generalization to COVID-19 patients and external datasets.\nResults:\nOur RL models will be evaluated on key clinical metrics, including ventilator-free days, extubation success (?24 hours extubated and alive), and reduced reintubation rates. Cross-validation and offline policy evaluation methods, such as weighted importance sampling and fitted Q-evaluation, will ensure robust model assessment.\n\nConclusion:\nOptimizing extubation strategies using RL has significant clinical implications. It enhances patient outcomes by promoting faster, sustained extubation while minimizing risks associated with prolonged ventilation. Moreover, it supports healthcare preparedness by offering scalable, personalized solutions that adapt to diverse patient populations and dynamic clinical environments. These insights will not only improve resource use and decision-making but also strengthen ICU systems for future public health crises.", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-B4A7DD", "title": "Predicting organ dysfunction in PASC patients", "task_team": false, "dur_project_id": "DUR-224E931", "workspace_status": "CLOSED", "lead_investigator": "Christopher Ferguson", "research_statement": "The COVID-19 pandemic has had a devastating impact on our country and the world. Recent focus has fallen on the prevalence of Post-acute sequelae of SARS-COV-2 infection (PASC) and our extremely limited understanding of those who are most at risk. Patients with PASC present quite differently with no clear ability to discern which patients might suffer from cognitive, renal, or cardiac problems as a result of their initial infection and subsequent long-term sequalae. This project aims to apply machine learning methodologies to patient data collected at the time of acute infection, including demographics, comorbidities, lab measurements, and clinical observations, to predict patients at risk of long-term sequalae and the organ system(s) impacted. If successful, this project could help improve our understanding of how individual PASC patients are at risk and tailor clinical treatment or interventions accordingly. This would also be directly relevant to other infectious diseases, as similar long-term organ dysfunctions are seen following an episode of sepsis.", "accessing_institution": "Office of the Assistant Secretary for Planning and Evaluation" }, { "uid": "RP-19D8CB", "title": "Fairness Models with Applications to Predictions of COVID-19 Outcomes and Vaccine Efficacy among Under-Represented Groups", "task_team": false, "dur_project_id": "DUR-22B21C9", "workspace_status": "CLOSED", "lead_investigator": "Chuan Hong", "research_statement": "In this project, we will develop fairness models for predicting COVID-10 outcomes and vaccine efficacy by incorporating fairness criteria. Specifically, we will first evaluate the disparities of COVID-outcomes and vaccine efficacy. We will then propose a series of fairness models. We will demonstrate the utility of the proposed methods in obtaining fair predictions for under-represented groups. ", "accessing_institution": "Duke University" }, { "uid": "RP-8DBC65", "title": "COVID-19 in individuals with Down's Syndrome", "task_team": false, "dur_project_id": "DUR-244E243", "workspace_status": "CLOSED", "lead_investigator": "Tell Bennett", "research_statement": "This project will investigate the clinical characteristics of COVID-19 in individuals with Down syndrome, who are at high risk of developing severe complications from SARS-CoV-2 infection because of interferon dysregulation. The research activities in this proposal could enable tailored strategies for better prevention, diagnosis, and treatment of COVID-19 in this vulnerable population.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-AEE7F4", "title": "SARS-CoV-2 Reinfection Severity - Nathaniel Hendrix", "task_team": false, "dur_project_id": "DUR-57030A3", "workspace_status": "CLOSED", "lead_investigator": "Nathaniel Hendrix", "research_statement": "We will conduct a retrospective, regression-based analysis with the primary objective of determining whether the risk of severe adverse outcomes of COVID-19 differs with prior SARS-CoV-2 infection. Our secondary objective will be to assess this change in risk in subgroups that were not well-represented in Bowe, et al., whose sample is composed mostly of older white men. We will do this by conducting stratified analyses by race and ethnicity (black, Hispanic white, non-Hispanic white, or other), age, and gender.\n\nWe will estimate all effects using a generalized additive Cox proportional hazards model. This model has the advantage of using non-parametric estimates of changing effects for selected covariates over time or across the range of continuous values. For example, the generalized additive model?s (GAM?s) use of splines to model smoothed effects can capture non-linear changes in the severity of outcomes associated with different strains of virus, with the availability of vaccines and pharmaceuticals, or with age.", "accessing_institution": "Stanford University" }, { "uid": "RP-9DC35A", "title": "COVID-19, treatment patterns, and cardiovascular outcomes among pregnant and non-pregnant women of reproductive age", "task_team": false, "dur_project_id": "DUR-2488E8C", "workspace_status": "ACTIVE", "lead_investigator": "Rebecca Schorr", "research_statement": "Aim 1: Elucidate COVID-19 treatment patterns across clinical, demographic, and geographic characteristics, including pregnancy status. H1: Women who are pregnant will be less likely to receive treatment for COVID-19 compared to women who are not pregnant; H2: Demographic and geographic characteristics such as higher neighborhood deprivation index or lower access to care and education will be associated with lower prevalence of treatment for COVID-19 among women of reproductive age. \nAim 2: Determine associations between COVID-19 diagnosis and treatment with thrombotic events among women of reproductive age. H1: Women who experience COVID-19 will have an increased risk of thrombotic events; H2: Use of hormonal contraception will exacerbate the risk of thrombotic events associated with COVID-19; H3: Treatment will attenuate the risk of thrombotic events associated with COVID-19. \nAim 3: Assess the risk of Adverse pregnancy outcomes (APOs) after a COVID-19 diagnosis and treatment. H1: Pregnant women with COVID-19 will be at higher risk of hypertensive disorders of pregnancy and preterm birth when compared to pregnant women without SARS-CoV-2 during pregnancy; H2: COVID-19 treatment will attenuate the risk of hypertensive disorders of pregnancy and preterm birth associated with diagnosis.\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-64742C", "title": "Postoperative complications following surgery for Proximal Humerus Fracture in COVID positive patients", "task_team": false, "dur_project_id": "DUR-262507C", "workspace_status": "ACTIVE", "lead_investigator": "Chimdindu Obinero", "research_statement": "This study aims to investigate the association between COVID-19 diagnosis and the development of postoperative complications following surgery for proximal humerus fracture, utilizing the N3C data enclave. We will access deidentified patient-level data including diagnostic, procedural, and demographic information, to identify individuals who have undergone surgery for proximal humerus fracture. The primary predictor variable will be a history of COVID-19 diagnosis. The study will assess the odds of developing complications such as pneumonia, pulmonary embolism, deep vein thrombosis (DVT), sepsis, acute myocardial infarction (MI), and hospital readmission. Additionally, we will examine demographic variations (age, sex, race) and the prevalence of comorbidities (e.g., diabetes, COPD, CAD, CHF, etc.) across the defined groups. ", "accessing_institution": "Henry Ford Medical Center" }, { "uid": "RP-5677B5", "title": "Characterization of long-COVID: definition, stratification, and multi-modal analysis", "task_team": false, "dur_project_id": "DUR-26D69E0", "workspace_status": "ACTIVE", "lead_investigator": "Emily Pfaff", "research_statement": "\"Long COVID\" patients have exhibited significant and heterogeneous symptoms such as fatigue, brain-fog, sleep disturbances, anxiety, and depression, etc. Therefore, there is an urgent need to use the data held within the N3C to perform stratification so as to provide a robust set of phenotypes for diagnosis and subsequent treatment management. A Long-COVID phenotype will support prognostic characterization of different substrata, potentially more precise care management, and greatly inform prospective interventional studies. We will describe the incidence, timing, and severity of sequelae of SARS-CoV-2 infection in adults and children and ascertain incidence rates across different demographic groups and differences in outcomes based upon early disease course characterization. The characterization will include public external datasets related to potential environmental and social determinants of health available in N3C. This is a N3C Consortium project.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-0AEEEB", "title": "The Associations of Neighborhood and Environment with COVID Infection Prevalence in Patients with Metastases to the Spine ", "task_team": false, "dur_project_id": "DUR-27C9AB0", "workspace_status": "CLOSED", "lead_investigator": "Comron Saifi", "research_statement": "We will explore the relationship between neighborhood environment and prevalence of COVID in patient with spinal metastases. We aim to understand the environmental associations for this patient population developing COVID. ", "accessing_institution": "Houston Methodist Research Institute" }, { "uid": "RP-6454D1", "title": "JHCEIRS: Detection of CoVID-19 among ILI in EDs in Taiwan", "task_team": false, "dur_project_id": "DUR-2A70509", "workspace_status": "CLOSED", "lead_investigator": "Yi-Chin Lin", "research_statement": "The objective of JHCEIRS multi-center (the US, Taiwan and Macha sites) study is to improve the detection and treatment of influenza and other respiratory virus including SARS-CoV-2. With border control and policy announcement of protective measures including wearing mask, handwashing and social distancing, the CoVID-19 activity has been low in Taiwan since the first wave of CoVID-19 outbreak in China. Therefore, with the experience of respiratory virus enrollment surveillance and expertise of machine learning in our team, we proposed to utilize data on N3C data enclave to make effort on the development and analysis of the medical data including demographic, comorbidities, and symptoms to evaluate new and innovative molecular based assays and machine-learning based prediction model to better characterize factors associated with severe disease and advance earlier recognition and treatment of patients with SARS- CoV-2.", "accessing_institution": "Keelung Chang Gung Memorial Hospital" }, { "uid": "RP-07038B", "title": "Development and validation of a machine-learning COVID-19 mortality and complications prediction models: A nationwide cohort study", "task_team": false, "dur_project_id": "DUR-2D8CA94", "workspace_status": "ACTIVE", "lead_investigator": "Wonsuk Oh", "research_statement": "COVID-19 is an ongoing global public health emergency caused by the SARS-CoV-2 coronavirus. 33.3 million patients have been diagnosed with the COVID-19 in the United States by March 2021. Among 2.1 million patients died due to the COVID-19 infection and its complications. Recent studies have proposed predictive models including mortality, severe complications such as pneumonia and acute kidney injury. However, many of them are either constructed using data collected from specific regional service areas and or not fully validated on geographically distinct regions. In this study, we are going to build predictive models for severe complications and mortality. We plan to adapt the hold-out validation approach to demonstrate that models using national representing data outperform from local data.", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-EBDE22", "title": "The Use of NCATS N3C Data Enclave to Answer High-priority Clinical Problems", "task_team": false, "dur_project_id": "DUR-32D3D51", "workspace_status": "CLOSED", "lead_investigator": "Kenrick Cato", "research_statement": "The aim of this project is to utilize the rich and de-identified COVID-19 datasets offered through NCATS's N3C initiative to answer clinical-relevant questions and to generate new knowledge that advances health outcomes and informs policy. One example is to see how well Early Warning Scores (EWS) work for COVID patients and to bring a nursing domain focus to the data within the N3C data repository (e.g., conduct a gap analysis to see what type of information are missing in the data enclave and what should be included in the future).", "accessing_institution": "Columbia University" }, { "uid": "RP-1B63B6", "title": "Access to Nutrition and the Prevalence of COVID Among Patients with Metastatic Cancer to the Spine ", "task_team": false, "dur_project_id": "DUR-5818A27", "workspace_status": "CLOSED", "lead_investigator": "Comron Saifi", "research_statement": "The project examines the relationship between access to nutrition and development COVID 19 among patients with spinal metastases. ", "accessing_institution": "Houston Methodist Research Institute" }, { "uid": "RP-2A70AB", "title": "Exploring the utility of N3C data for observational studies for regulatory decisions", "task_team": false, "dur_project_id": "DUR-5C0231A", "workspace_status": "CLOSED", "lead_investigator": "Efe Eworuke", "research_statement": "To understand the risk factors for receiving the different monoclonal antibodies and evaluate the impact of receiving monoclonal antibodies on COVID-19 hospitalization.", "accessing_institution": "Food and Drug Administration" }, { "uid": "RP-EDB567", "title": "COVID-19 Survival analysis", "task_team": false, "dur_project_id": "DUR-5D257BC", "workspace_status": "CLOSED", "lead_investigator": "Weihao Wang", "research_statement": "The aim of this study was to conduct a survival analysis to establish the variability in survivorship of patients with COVID-19 in US.", "accessing_institution": "Stony Brook University" }, { "uid": "RP-7ECDA2", "title": "Multilevel Determinants of Racial/Ethnic Disparities in Maternal Morbidity and Mortality in the Context of the COVID-19 Pandemic ", "task_team": false, "dur_project_id": "DUR-3406B72", "workspace_status": "CLOSED", "lead_investigator": "Jihong Liu", "research_statement": "Annually in the U.S., nearly 60,000 women experience severe maternal morbidity and mortality (SMMM) with substantial health disparities by race/ethnicity, even prior to the COVID-19 pandemic. The unprecedented COVID-19 pandemic has hit communities of color the hardest. Non-Hispanic Black and Hispanic women who are pregnant appear to have disproportionate SARS-CoV-2 infection and death rates. Questions regarding the impact of the COVID-19 pandemic on racial disparities in SMMM and the dynamics and interactions of multilevel determinants such as broader social contexts of SMMM remain unanswered. The overarching goal of this study is to investigate racial/ethnic disparities in maternal morbidity and mortality (MMM), the contributing roles and mediating pathways of social contexts (e.g., residential segregation, racial discrimination in poverty, education, unemployment, and home ownership), and their long-standing health consequences postpartum. We will achieve our goal by studying the distributions of COVID-19 cases and multilevel determinants of maternal health in the United States using electronic health records data from the ongoing National COVID Cohort Collaborative (N3C). Nationwide social context databases will be added to N3c databases. \nWe will achieve two specific aims: 1) to examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in SMMM; 2) to examine and explore how the key features of social contexts have contributed to the widening of racial/ethnic disparities in MMM during the pandemic and identify distinct mediating pathways through maternity care and mental health. ", "accessing_institution": "University of South Carolina" }, { "uid": "RP-EA86D2", "title": "The Association between Social Determinants of Health, COVID-19, and Incidence of De Novo Depression Following Traumatic Brain injury ", "task_team": false, "dur_project_id": "DUR-35859AB", "workspace_status": "CLOSED", "lead_investigator": "Lejia Hu", "research_statement": "A traumatic brain injury (TBI) occurs when the brain experiences dysfunction as a result of an external force, such as a bump or blow to the head or body, or an object that penetrates the skull and enters the brain. TBI is a significant cause of death and disability in the United States. Out of the approximate 1.5 million Americans who sustain a TBI each year, 3.33% die and 6% develop a long-term disability. Studies have found that social determinants of health (SDoH) such as race and ethnicity, socioeconomic status, and access to care are most frequently associated with the occurrence of TBIs due to varying levels of risk exposure. This study aims to investigate the social determinants of health that are associated with the incidence of major depressive disorder (MDD) among TBI patients. With regards to COVID, current literature looks at the impacts of the pandemic era with regards to post-TBI treatment. Therefore, this study is coming from a causal perspective and aims to understand if a COVID-19 diagnosis increases the incidence of MDD as well. ", "accessing_institution": "Boston Strategic Partners Inc" }, { "uid": "RP-DC2CDA", "title": "Mortality Rates of COVID+ Hospitalized Patients with a History of Hypertension on Different Hypertensive Medications", "task_team": false, "dur_project_id": "DUR-361C611", "workspace_status": "CLOSED", "lead_investigator": "Alessandro Ghigi", "research_statement": "This study aims to understand, among COVID+ hospitalized patients with a history of hypertension, what is the mortality rate of the following five different patient cohorts: (1) on any ACE inhibitor, no ARB; (2) on any ARB, no ACE inhibitor; (3) on both ACE inhibitor and ARB; (4) on any other hypertensive medication, no Ace inhibitor, no ARB; and (5) not on any hypertensive medication?", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-5D484B", "title": "Ontology-based Vaccine Classification and Vaccine Effect Analysis using N3C Data", "task_team": false, "dur_project_id": "DUR-36ED2AE", "workspace_status": "ACTIVE", "lead_investigator": "Yongqun He", "research_statement": "Vaccination has played a significant role against COVID-19. There are many COVID-19 vaccines developed and used against COVID-19. Different terminologies have also been used to classify these various vaccines. The disintegration of vaccine knowledge representation has posed a challenge to our systematic N3C data analysis. The N3C system uses the OMOP for data standardization. However, currently the OMOP vaccine terminologies are complex. To address this issue, we have formed the collaborative OHDSI Vaccine Vocabulary Working Group (Vaccine Vocab WG) with an aim to standardize and apply the vaccine knowledge representation using the Vaccine Ontology (VO). Significant results were achieved and presented in OHDSI Symposium 2023. For this N3C DUR application, we plan to test and apply our VO-based OMOP vaccine terminology harmonization to study N3C data for specific use cases. More specifically, this application has two specific aims: Aim 1: Evaluate how our VO-based harmonization of different OMOP vaccine terminologies may impact the integration of the N3C vaccine data and support vaccine data analysis. Aim 2: Using ontological approach to analyze specific vaccine effects on COVID-19 patients. We will compare different types of vaccine positive protective effects and adverse events following administering of covid-19 vaccines or other types of vaccines. We will also develop ontology-based machine learning methods to predict vaccine effects including vaccine-induced protection and adverse events. We expect that our ontology-based classification and data analysis will significantly improve vaccine-related N3C data processing and analysis pipeline. ", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-D92D38", "title": "Effects of Covid or long Covid on blood pressure variability", "task_team": false, "dur_project_id": "DUR-370D65A", "workspace_status": "ACTIVE", "lead_investigator": "Marc Basson", "research_statement": "Day to day variability in blood pressure has been associated with long term poor health outcomes including decreased survival and increased risk of heart attack, stroke, and hospitalization. One potential cause of blood pressure variability is poor control of the blood pressure by the nervous system. We intend to determine whether having had Covid-19 or \"long haul Covid\" is associated with increased risk for blood pressure variability that might contribute to long term worse health outcomes.", "accessing_institution": "University of North Dakota" }, { "uid": "RP-D183B1", "title": "HIV and COVID-19: Effect of Shelter-In-Place Orders on Virologic Suppression", "task_team": false, "dur_project_id": "DUR-3A95DC3", "workspace_status": "CLOSED", "lead_investigator": "Christopher Schriever", "research_statement": "The objective of this study is to retrospectively investigate the effects of COVID-19 shelter-in-place orders on clinical outcomes, including virologic suppression, in patients living with HIV. To do this, a retrospective comparison before and after the shelter-in-place orders will be conducted. ", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-0608C3", "title": "Predicting COVID-19 severity: Identifying key Comorbidities", "task_team": false, "dur_project_id": "DUR-5E88C81", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "This research project aims to identify key comorbidities that predict the severity of COVID-19 at the time of infection. Utilizing the extensive patient-level data available within the N3C enclave, we will conduct a comprehensive analysis to determine which pre-existing conditions significantly influence the clinical outcomes of COVID-19 patients. By employing advanced statistical models and machine learning techniques, we will evaluate the impact of various comorbidities on the severity of COVID-19, measured through hospitalization rates, ICU admissions, and mortality. The findings from this study will provide critical insights into patient risk stratification and inform clinical decision-making processes to improve patient management and resource allocation during the ongoing pandemic.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-44DD30", "title": "Antimicrobial drug prescribing in hospitalized COVID-19 patients", "task_team": false, "dur_project_id": "DUR-3C064D7", "workspace_status": "CLOSED", "lead_investigator": "Allison Kolbe", "research_statement": "According to the 2019 Antibiotic Threats Report by the Centers for Disease Control and Prevention, antimicrobial resistance (AMR) contributes to 2.8 million infections and 35,000 deaths in the U.S. each year. Both appropriate and inappropriate antimicrobial drug use can contribute to the development of AMR. Studies have shown a high rate of antimicrobial drug usage in hospitalized COVID-19 patients, despite reported low rates of secondary infections. Relatively little is known about how antimicrobial drug prescribing has evolved over the course of the COVID-19 pandemic, and what impact this may have had on AMR. This study seeks to address these research gaps. Specifically, this study aims to: (1) quantify rates of antimicrobial use in hospitalized COVID-19 patients over time, including the types of antimicrobial drugs used; (2) evaluate differences in antimicrobial prescribing by patient characteristics, such as age, gender, race, or ethnicity; (3) evaluate differences in antimicrobial prescribing by facility characteristics, such as location or provider type; and (4) quantify rates of AMR infections in hospitalized COVID-19 patients. ", "accessing_institution": "Office of the Assistant Secretary for Planning and Evaluation" }, { "uid": "RP-EE8EAC", "title": "Evaluation of determinants of health, treatment, co-morbidity, symptoms, and recovery of patients hospitalized with Covid-19", "task_team": false, "dur_project_id": "DUR-3DD1EE6", "workspace_status": "ACTIVE", "lead_investigator": "Gesulla Cavanaugh", "research_statement": "The purpose of the study is to assess the characteristics of hospitalized patients with Covid-19 as the primary reason for admission. Variables of interest are co-morbidities, geographical location, race and ethnicity, BMI, length of stay, genetics, wellness, medical history, symptom severity, treatments used by the hospital, and patient risk and survival rate. With employed machine learning techniques, we aim to uncover factors associated with Covid-19 recovery in hospitalized patients. We aim to examine the factors associated with survival rate in the hope to propose better treatment; as well as gain a generalized understanding of patients who recover or die from a severe, moderate, or mild Covid-19 illness.", "accessing_institution": "Nova Southeastern University" }, { "uid": "RP-AC6933", "title": "Respiratory Conditions and COVID-19: Risk Factors for Long COVID and Respiratory Complications", "task_team": false, "dur_project_id": "DUR-44080E9", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "This research project seeks to assess the incidence of long COVID-related pulmonary fibrosis, chronic cough, dyspnea, and other respiratory complications in patients with pre-existing asthma or chronic obstructive pulmonary disease (COPD). Utilizing data from the N3C enclave, we will analyze a cohort of individuals diagnosed with COVID-19 and examine their long-term respiratory outcomes. Our primary objective is to determine whether patients with asthma or COPD are at increased risk for developing long COVID-associated respiratory conditions compared to those without these pre-existing conditions. We will employ statistical analyses to compare the incidence and severity of pulmonary fibrosis, chronic cough, dyspnea, and other outcomes across these patient populations. The findings from this study will inform clinicians and public health officials on the long-term impact of COVID-19 on respiratory health, aiding in the development of targeted post-COVID management strategies for at-risk populations.\n", "accessing_institution": "login.gov" }, { "uid": "RP-D8FCCE", "title": "Virtual pooling: A privacy-preserving method for learning complex signals across siloed biomedical datasets", "task_team": false, "dur_project_id": "DUR-44EB7EB", "workspace_status": "ACTIVE", "lead_investigator": "Trinabh Gupta", "research_statement": "Current research on COVID-19 relies primarily on the N3C dataset. While this unique data resource contains harmonized clinical health data from >230 different sites and over 20 million patients across the United States, it captures a relatively small fraction (<20%) of the overall patients in the US. Thus, current research findings on COVID-19 have potential for bias. Furthermore, N3C data does not include data from nations outside the purview of the NIH and NCATS. The COVID-19 research community would benefit from new methods that can scale data analysis to national and global levels. \n\nTo this end, we are developing a new method for data analysis called virtual pooling. This is a method that keeps the data distributed or federated (to preserve privacy, security, and control over the data) while enabling the discovery of insights that are functions of the _total_ dataset. Virtual pooling is inspired by the practice of federated analytics (aggregate data meta-analysis) but it improves over this existing solution in terms of the analysis methods and the quality of analysis results.\n\nThe aim of this project is to validate virtual pooling on a prior study conducted on N3C (Andersen et al. 2022). We pick this particular study as it is in a clinical domain that our team understands well. If virtual pooling proves comparable to actual pooling, then the next step would be to deploy this method to scale up on future COVID-19 studies to include more centers from within and outside the US, test and improve the generalizability of previously published COVID-19 findings, and answer previously unanswered questions that require access to data at national and global scale.", "accessing_institution": "University of California, San Francisco" }, { "uid": "RP-92581D", "title": "Risk estimator for COVID 19 re-infection", "task_team": false, "dur_project_id": "DUR-4839960", "workspace_status": "CLOSED", "lead_investigator": "Bharathi Myneni", "research_statement": "Purpose: Even though, COVID reinfection occurs less frequently, reinfected people show higher levels of virus in the nose and the throat. Hence, higher potential for disease transmission.\nI want to develop a COVID 19 Re-infection risk estimator which can help to identify key risk factors and patients who are at higher risk for developing re-infection.\nHypothesis:\nIf certain risk factors correlate to a higher chance of COVID-19 reinfection, then can a risk estimator be developed to assess the risk of re-infection using machine learning (ML) method? \nProcedure:\nObtain data use agreement (DUA) executed by National Center for Advancing Translational Sciences (NCATS) to gain access to N3C (National COVID Cohort Collaborative) data.\n N3C is the largest National COVID electronic health record repository which includes a cohort of COVID 19 and control cases. In the present study, only synthetic data is utilized.\n Six medical and eleven non-medical risk factors are used in the present study. The study cohort includes both COVID and control groups to analyze the data. Statistical methods are used to develop a risk estimator to predict the risk of COVID 19 re-infection.\n", "accessing_institution": "Citizen Scientist" }, { "uid": "RP-18BB05", "title": "COVID-19 & Cancer: Defining the Immunological Paradigm ", "task_team": false, "dur_project_id": "DUR-4E1E29C", "workspace_status": "CLOSED", "lead_investigator": "Yan Leyfman", "research_statement": "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel betacoronavirus that causes the respiratory illness coronavirus disease 2019 (COVID-19). COVID-19 ranges in severity from an asymptomatic viral infection to life-threatening cases of pneumonia, acute respiratory distress syndrome (ARDS), multi-organ damage and sepsis. Cancer patients are at an increased risk of severe SARS-CoV-2 infection due to their immunocompromised status. We propose a mechanism by which SARS-CoV-2 infection causes multiple organ damage through IL-6-mediated inflammation and hypoxia-induced cellular metabolic alterations leading to cell death. Hypoxia is also induced by malignancy due to alterations in metabolism, resulting in greater IL-6 secretion. \n\nWe aim to determine (1) outcomes of COVID-19 infection in hospitalized patients, including mortality, need for ICU-level care, efficacy of attempted therapies, time to recovery, and co-infections (2) compared cytokine levels amongst patients based on COVID-19 status and cancer status. \n\n", "accessing_institution": "Pennsylvania State University" }, { "uid": "RP-A916BC", "title": "[N3C Operational] Synthetic data generation and validation using CEHR-GPT for COVID Research", "task_team": false, "dur_project_id": "DUR-4EB0CAB", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The purpose of the [N3C Operational] Synthetic data generation and validation using CEHR-GPT for COVID Research is to develop and validate an open-source tool that can generate synthetic data for COVID-19 research. Synthetic data generation, because it contains no PII, holds a great potential to democratize access to health care data for the research community. However, until both privacy process and the scientific utility are validated the use of the data will be questioned. In the past N3C had a synthetic data initiative but the cost of the commercial vendors was unsustainable. The only way to validate the generalizability of the scientific utility is to have a representative sample and N3C is the only national repository of COVID-19 patients with linked data including CMS, and Mortality where this can be done. \n\nSynthetic Electronic Health Records (EHR) are crucial for healthcare research and machine learning, especially for those lacking access to actual healthcare data. Traditional methods like rule-based systems and generative adversarial networks (GANs) create synthetic EHR data in tabular forms but often miss the temporal aspects of patient histories, affecting data quality. The use of Generative Pre-trained Transformers (GPT) is gaining traction in EHR data applications, including disease progression, population estimation, and creating synthetic data. Our work focuses on using GPT for synthetic data generation. We train a GPT model with a unique patient representation from CEHR-BERT. This approach allows for generating patient sequences easily convertible to the OMOP data format, addressing previous limitations in synthetic EHR data generation.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-C542AB", "title": "Disparities in COVID-19", "task_team": false, "dur_project_id": "DUR-4EBAAB4", "workspace_status": "CLOSED", "lead_investigator": "Tamas Gal", "research_statement": "Race and ethnicity based disparities in COVID-19 testing, treatment and mortality are documented based on institutional datasets. This project aims to look at disparities at a larger scale in COVID-19. We are requesting Level 2 de-identified data to explore if there are significant disparities in COVID-19 testing, treatment and mortality.", "accessing_institution": "Virginia Commonwealth University" }, { "uid": "RP-3E1CEE", "title": "Formal Evaluation of Machine Learning and Statistical Approaches to Predict Outcomes among Pediatric and Adults patients with COVID19 Disease", "task_team": false, "dur_project_id": "DUR-534C016", "workspace_status": "CLOSED", "lead_investigator": "Christos Argyropoulos", "research_statement": "Adults and children with COVID-19 are at risk for severe clinical outcomes including hospitalization, acute COVID, including Multisystem Inflammatory Syndrome in Children (MIS-C) and Post Acute Covid Sequelae (PASC). Methods are needed to answer to particularly important clinical questions: predicting the need for hospitalization for patients who test positive in an outpatient setting, evaluate methods for predicting the need for respiratory and cardiovascular interventions in hospitalized patients, including children with MIS-C and finally predict the development of the PASC syndrome among the survivors of the acute disease. To that end, we are proposing to access the National COVID Cohort Collaborative (N3C) enclave community to leverage de-identified electronic health record data to develop, train and compare computational models from the Machine Learning and Statistical Learning fields that can predict severe COVID-19 complications, equipping healthcare providers with the information and tools they need to identify patients at risk. ", "accessing_institution": "University of New Mexico" }, { "uid": "RP-E7FAAD", "title": " Assessing and Modelling the Impact of COVID-19 Pandemic on HIV Diagnosis and Prevalence using Machine Learning", "task_team": false, "dur_project_id": "DUR-53C4B69", "workspace_status": "ACTIVE", "lead_investigator": "Marie Lluberes", "research_statement": "Human Immunodeficiency Virus (HIV) compromises the immune system by attacking CD4 cells, and its transmission is associated with exposure to bodily fluids from an infected individual. Modes of transmission include unprotected sexual contact, blood transfusions, exposure to contaminated needles and equipment, organ transplants, and from mother to child. Certain populations, such as healthcare workers and individuals who inject drugs, face heightened risks. The global health repercussions of the COVID-19 pandemic have influenced several aspects in healthcare access and delivery, and disease diagnosis and management. The pandemic has had a profound impact on healthcare for vulnerable populations, exacerbating existing inequalities and creating new challenges. This study aims to assess how the pandemic has influenced HIV diagnosis rates and prevalence. Utilizing data from the N3C electronic health records (EHR) from 2019 to 2023, we analyze trends in HIV diagnoses, testing frequencies, and patient outcomes across different phases of the pandemic. Our findings will provide insights into the pandemic?s impact on HIV healthcare and highlight areas for targeted intervention.", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-1177B7", "title": "Assessment of Sepsis Related EHR Data for Secondary Analysis of Sepsis Occurrences in COVID-19 Hospitalized Patients", "task_team": false, "dur_project_id": "DUR-54980C1", "workspace_status": "CLOSED", "lead_investigator": "Patricia Buendia", "research_statement": "This project will assess the information on the diagnosis and treatment of sepsis patients for COVID-19 patients who were hospitalized during the pandemic, April 2020 to July 2022, and beyond, to support our CLAIRE hospital study findings with additional sepsis results. Some CLAIRE results can be found in a preprint format, DOI:10.21203/rs.3.rs-2292121/v1. For this purpose, data sets like the ones in NCATS N3C are crucial. We require access to deidentified data and while we do not need the dates of service we need the days since hospital admission until sepsis diagnosis and days until recovery or death. Moreover, we are developing python code to assess the data quality of clinical data that we will use on the reported sepsis cases among the COVID-19 hospitalized cases. The process involves parsing the data and metadata and mapping them to clinical data vocabularies like those in UMLS where appropriate. ", "accessing_institution": "Lifetime Omics, Inc." }, { "uid": "RP-EA5C52", "title": "Impact of COVID-19 on cancer survivorship outcomes and health care resource utilization", "task_team": false, "dur_project_id": "DUR-55B2516", "workspace_status": "CLOSED", "lead_investigator": "Yu Ke", "research_statement": "Traditionally, essential components of cancer survivorship care ? ongoing screening, surveillance, physical and psychological symptom support, lifestyle promotion ? are delivered in specialist outpatient settings. During the global COVID-19 pandemic, the frequency and types of health care visits dropped tremendously to reduce the risk of COVID-19 transmission (Kutikov et al., 2020). Besides impacting care delivery, available evidence also showed that COVID-19 is associated with poorer psychosocial well-being and deteriorating emotional functioning among cancer survivors (Jammu et al., 2020 and Bargon et al., 2021). However, conclusive evidence of the impact of COVID-19 on downstream clinical outcomes and health care utilization patterns remains unknown. Furthermore, with reports of long COVID-19 symptoms, the additional burden imposed by COVID-19 infection on top of survivorship issues has yet to be characterized. Therefore, this study aims to elucidate the impact of COVID-19 on cancer survivors? clinical outcomes and health care utilization patterns. \n\nWe hypothesize that COVID-19 will cause an impact on the follow-up care modalities among cancer survivors, where the engagement of services may shift from being predominantly tertiary to greater primary involvement. We also hypothesize that cancer survivors infected with COVID-19 would have poorer clinical outcomes and greater health care resource utilization throughout the survivorship phase than non-infected survivors. Thus, we are requesting for the data from the N3C platform to conduct a retrospective cohort study following up with cancer survivors for up to one year.", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-162491", "title": "Does the use of UDCA (ursodeoxycholic acid) at baseline reduce the incidence and/or severity of COVID-19? - Aayush Visaria", "task_team": false, "dur_project_id": "DUR-6362DB1", "workspace_status": "ACTIVE", "lead_investigator": "Aayush Visaria", "research_statement": "Ursodeoxycholic acid (UDCA) is a bile acid traditionally used for liver and gallbladder pathologies. Recently, pre-clinical and small, retrospective studies have demonstrated that UDCA exhibits anti-inflammatory, antioxidant effects that may have benefit in relieving acute inflammatory states and help prevent liver injury. Whether these properties confer benefits on incidence or outcomes of COVID-19 infection is unknown. Our primary aim is to determine whether UDCA decreases risk of inpatient hospitalization among adults with COVID-19. Secondary aims include: 1) to determine which demographic and comorbid subgroups of patients currently indicated to take UDCA benefit most from decreased hospitalizations risks, 2) to determine whether UDCA use at baseline decreases risk of severe COVID-19 requiring invasive ventilation, mechanical ventilatory support, or ICU admission among COVID-19 positive patients in the ED or inpatient setting. To address these questions, we will emulate a hypothetical target trial using a causal inference framework, similar in design to a study by Gupta et al. that used retrospective cohort data to examine the effect of tocilizumab on mortality among critically ill COVID-19 patients. Our hypothetical target trial is adults with laboratory-confirmed COVID-19 in the outpatient setting who are randomized to either UDCA or no UDCA within 2 days of COVID-19 diagnosis and followed up to COVID-19 inpatient hospitalization (further dividing into COVID-19 in-hospital severity as described below), death, or end of study period. Our observational study design will utilize a time-stratified, retrospective cohort study comparing risk of COVID-19 severity in COVID-19 patients using UDCA to patients not using UDCA, adjusting for confounders (discussed below) using inverse probability weighting and stratifying by likely indication for UDCA use, COVID-19 variant, and vaccination status. This will effectively address the PHASTR question of determining whether UDCA decreases COVID-19 severity. Unfortunately, given the ambiguity with which control. Non-COVID patients are included in the database, it is difficult to ascertain incidence of COVID-19 using N3C data.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-730547", "title": "Impact of Perioperative COVID Diagnosis and Vaccination Status on Postoperative Outcomes Following Elective Cervical Spinal Surgery", "task_team": false, "dur_project_id": "DUR-64B09BE", "workspace_status": "CLOSED", "lead_investigator": "Aditya Joshi", "research_statement": "Preoperative management for spinal surgery includes a myriad of examinations and referrals. However, the guidelines surrounding a preoperative COVID-19 diagnosis are not clearly defined. While COVID-19 has been implicated in pulmonary complications, current research has also associated COVID-19 with numerous other multi-organ complications. Previous studies have demonstrated the negative impact COVID-19 has on postoperative outcomes for both urgent and elective orthopedic surgeries. Chan et al. 2023 demonstrated a preoperative diagnosis of COVID-19 within 2 weeks of lumbar surgery was associated with increased odds of 90 day complications. Hegde et al. 2023 demonstrated that patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) who received a postoperative diagnosis of COVID-19 had increased odds of 30 day postoperative complications. This study aims to analyze the role of COVID-19 diagnosis and vaccination status on postoperative outcomes following cervical spine surgeries. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-D1790B", "title": "COVID-19 Outcomes and Vaccination Rate in Patients with Chronic Lymphocytic Leukemia ", "task_team": false, "dur_project_id": "DUR-66A6B29", "workspace_status": "CLOSED", "lead_investigator": "Douglas Kou", "research_statement": "Given advanced age, comorbidities, and immune dysfunction, chronic lymphocytic leukemia (CLL) patients may be at a particularly high risk of being infected by COVID-19 and resulting in poor outcomes related to COVID-19. The objectives of this study are to (1) examine the clinical outcomes of COVID-19 in patients with CLL tested positive for COVID-10, (2) assess the COVID-19 vaccination rate in CLL patients, and (3) conduct a pattern of use analysis to examine changes (if any) in regimens prescribed for CLL patients pre- and post-COVID-19 pandemic. For this analysis, we would use the National COVID Cohort Collaborative (N3C) de-identified data set. The all-cause mortality and hospitalization rate after the COVID-19 diagnosis in CLL patients tested positive for COVID-19 would be calculated. The patient characteristics including social-demographics (e.g., age, sex) and comorbidities (e.g., Charlson comorbidity index) would be described for the CLL patients, and the patient characteristics would be compared between CLL patients with and without the mortality and/or hospitalization. Further, the COVID-19 vaccination rate in all the CLL patients identified in the N3C data would be evaluated. The study results would help the research community better understand the impact of COVID-19 in the US CLL patient population. ", "accessing_institution": "BeiGene USA Inc" }, { "uid": "RP-878DEA", "title": "Transferring vaccine hesitancy predictability from seasonal influenza to Covid-19 using the N3C Database", "task_team": false, "dur_project_id": "DUR-68686D9", "workspace_status": "CLOSED", "lead_investigator": "Emile Clastres", "research_statement": "While the influenza vaccine is known as the most effective way to reduce violence and mortality with influenza and Covid-19, especially in clinical risk groups, its coverage remains suboptimal. In particular, a large fraction of the population is vaccine-hesitant, which means that their intake varies from season to season.\nStudies suggest that social determinants, intermediary effects and healthcare-related factors are good predictors of influenza hesitancy. Yet, these factors are either influenza-specific (e.g. policy related), infrastructure-specific, or not readily available. In this study, we seek to identify new factors for vaccine hesitancy based on substance use and mental health,\nthat would be available in standardized health data in OMOP format and potentially transferrable to Covid-19 vaccine hesitancy. \nDespite the large body of literature on the determinants of vaccine hesitancy, there is a need to identify vaccine hesitancy at a population level to optimally allocate resources related to reduce vaccine hesitancy. To our knowledge, there are limited studies that present predictive models of\nvaccine uptake using routinely collected data. However, none one of these studies focused on vaccine hesitancy in adult patients, and none of them had the intent of transferring their predictions to non-influenza vaccination.\n", "accessing_institution": "Stanford University" }, { "uid": "RP-151967", "title": "Examination of the Moderating Effect of Race on the Relationship between Vitamin D Status and COVID-19 Test Positivity using Propensity Score Methods", "task_team": false, "dur_project_id": "DUR-6E4A86C", "workspace_status": "CLOSED", "lead_investigator": "Kevin McKee", "research_statement": "With a well-established role in inflammation and immune function, vitamin D status has emerged as a potential factor for preventing transmission of COVID-19. The purpose of this study is to evaluate the moderating effect of vitamin D status, as measured by 25-hydroxyvitamin D [25(OH)D], on the relationship between race and the risk of COVID-19 positivity, and to compare propensity score (PS) model results to those obtained from classical bivariate and multivariable models. ", "accessing_institution": "Virginia Tech" }, { "uid": "RP-5D2B44", "title": "Methods and Challenges in Observational Health Data Analysis, Spring 2023", "task_team": false, "dur_project_id": "DUR-7EB3E29", "workspace_status": "CLOSED", "lead_investigator": "Shawn O'Neil", "research_statement": "This project is associated with the PMED 6410 course at CU Anschutz. Description: Observational Electronic Health Record (EHR) data are an increasingly accessible resource for medical research. Such records are often structured in a Common Data Model (CDM), from which question-relevant information must be extracted, aggregated, and analyzed. This course introduces students to real-world EHR analysis with the National COVID Cohort Collaborative (N3C; https://covid.cd2h.org), a secure cloud-hosted database with billions of records about millions of patients from dozens of sites across the US. We?ll discuss medical vocabularies and the OMOP CDM, practice querying and analyzing tabular data with SQL and R, see examples of common statistical techniques for observational data, and explore select topics in machine learning and data integration. Students will additionally have an opportunity to investigate COVID-19-related questions of their own interest as a course project.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-B6C299", "title": "Community and Individual Level Factors Associated with SARS-CoV-2 Infections and Outcomes in Patients Living in Rural Areas", "task_team": false, "dur_project_id": "DUR-73A2C40", "workspace_status": "CLOSED", "lead_investigator": "Sharita Thomas", "research_statement": "Rural communities are being adversely affected by the COVID-19 pandemic with social and economic factors lending to the susceptibility. Further, growing data on racial and ethnic groups in rural areas has generated concern about economic, political, social, and health system failings resulting in disproportionate rates of morbidity and mortality. Concern also deepens for rural areas impacted by past or impending rural hospital closures. Rural hospitals are a primary source of health care and of jobs in many rural communities. Rural hospital closures can severely reduce access to health care by rural communities and have also impacted communities with larger proportions of racial and ethnic minorities. This study will provide rural stakeholders and policymakers with a better understanding of the past and current health and economic condition of rural communities as a result of COVID-19. Additionally, the perspective of rural residents will inform rural stakeholders and policymakers with insight on the lived experience. The study aims to describe and analyze the factors, including rural hospital and health facility closures, associated with COVID-19 morbidity, susceptibility, mortality, and vaccination among rural residents.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-A64F90", "title": "Analyzing health disparities among people with cardiovascular disease and SARS-CoV-2: The impact of race, ethnicity, age, and vaccination status on hospitalization rates, length of stay and mortality", "task_team": false, "dur_project_id": "DUR-7459DB8", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "The COVID-19 pandemic has underscored existing health disparities, with certain demographic groups experiencing disproportionate impacts. Among individuals with cardiovascular disease (CVD), infection with SARS-CoV-2 presents unique challenges and risks. This research project aims to analyze health disparities among CVD patients infected by SARS-CoV-2, focusing on the influence of race, ethnicity, age, and vaccination status on hospitalization rates, length of stay (LOS), and mortality outcomes.\nUsing data from the N3C enclave, this study will conduct an analysis of COVID-19 outcomes among CVD patients. Demographic information, including race, ethnicity, age, and vaccination status, will be used to assess disparities in hospitalization rates, Length of Stay and mortality.\nThe analysis will employ statistical techniques, including multivariable regression models, to identify independent associations between demographic factors, COVID-19 infection, and clinical outcomes among CVD patients. Subgroup analyses will be conducted to examine intersectional effects and elucidate disparities across different demographic categories.\nKey outcomes of interest include hospitalization rates, LOS, and mortality rates among CVD patients infected by SARS-CoV-2. Additionally, the impact of vaccination status on COVID-19 outcomes will be evaluated to assess the effectiveness of vaccination in mitigating disparities and reducing disease severity among CVD patients.\nFindings from this research will contribute to a deeper understanding of health disparities among CVD patients affected by COVID-19 and inform targeted interventions to address disparities in healthcare delivery and outcomes. By identifying factors associated with increased hospitalization rates, prolonged LOS, and higher mortality among CVD patients with COVID-19, this study aims to support evidence-based strategies for improving care quality and reducing disparities in vulnerable populations.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-1EBB23", "title": "COVID-19 Vaccine Influence on Thrombosis ", "task_team": false, "dur_project_id": "DUR-76E02ED", "workspace_status": "CLOSED", "lead_investigator": "michael yurchak", "research_statement": "The purpose of the project is to further investigate increased incidence of thrombotic events post COVID-19 vaccine administration. We aim to identify potential risk factors including but not limited to age, sex, co-morbidities, etc. We also plan to do a cross study comparing rates of clotting between COVID-19 patients and those individuals who received the COVID-19 vaccine. ", "accessing_institution": "West Virginia School of Osteopathic Medicine" }, { "uid": "RP-6A3956", "title": "The Association of Air Quality and Olfactory Dysfunction in COVID-19 Patients in the United States", "task_team": false, "dur_project_id": "DUR-789369E", "workspace_status": "CLOSED", "lead_investigator": "Pranav Mirpuri", "research_statement": "The COVID-19 syndrome caused by the coronavirus SARS-CoV-2 is a multiorgan disease which can manifest with respiratory, neurological, gastrointestinal, and systemic symptoms. A significant number of symptomatic COVID-19 patients have presented with olfactory dysfunction including anosmia using the limited dataset. Studying the epidemiology of COVID-19 will allow for a more accurate characterization of its etiology and pathogenesis. Furthermore, if persons are more likely to experience more serious illness as a result of their area air quality, there may need to be significant alterations to the roll-out of COVID-19 to ensure equity of care and to minimize disease burden in the United States. ", "accessing_institution": "Rosalind Franklin University of Medicine and Science" }, { "uid": "RP-7E7E0E", "title": "Burden of COVID-19 Pandemic in US Patients with Chronic Obstructive Pulmonary Disease: a National Study Using the N3C Data Enclave", "task_team": false, "dur_project_id": "DUR-78B7DE2", "workspace_status": "CLOSED", "lead_investigator": "Paul Reyfman", "research_statement": "Observational studies have suggested that chronic obstructive pulmonary disease (COPD) is a risk factor for worse outcomes from COVID-19 pneumonia, including more severe disease, greater risk of respiratory failure, and greater risk of mortality. We would like to use the National COVID Cohort Collaborative (N3C) dataset (limited dataset) to assess the burden of COVID-19 in patients with COPD compared with patients without COPD using clinical data from large a multi-site observational cohort in the United States.", "accessing_institution": "Northwestern University" }, { "uid": "RP-53878C", "title": "Mechanical Ventilation Characterization of COVID-19 Patients during the Pandemic", "task_team": false, "dur_project_id": "DUR-7BCBD94", "workspace_status": "ACTIVE", "lead_investigator": "Hieu Nguyen", "research_statement": "A retrospective analysis will be performed utilizing deidentified data from the National COVID Cohort Collaborative. Inclusion criteria will be positive COVID result, age >4 weeks, length of stay >1 day, were admitted either inpatient or in the emergency department, and had at least one day of mechanical ventilation. Exclusion criteria include patients on chemotherapy and patients with an immune deficiency. The goal is to use multiple linear regression models and chi-squared tests to associate increased mechanical ventilation use to improved patient outcomes with a p-value of 0.05. Sequential analysis would be performed to analyze any combination of therapies having synergistic effects. ", "accessing_institution": "Georgia State University" }, { "uid": "RP-A77B02", "title": "Evaluation of the Relationship\tBetween Chronic Medication Use and COVID-19 Disease\t\t", "task_team": false, "dur_project_id": "DUR-7D09D5E", "workspace_status": "CLOSED", "lead_investigator": "Alexandra Sierko", "research_statement": "Background: COVID-19, caused by the SARS-CoV-2 virus, is a devastating disease that has impacted the entire world population. Although little is known regarding the viral pathogenesis, there are numerous theories related to the viral mechanisms of infectivity. Recent research has identified a potential role for targets and disease processes that are directly affected by common medications. These components include the renin-angiotensin-aldosterone system (RAAS), bradykinin signaling, vitamin D homeostasis, vasodilation, and the inflammatory response. The purpose of this project is to determine if chronic use of medications known to impact potential COVID-19 targets influences disease course and/or severity. \n\nMethods: We will conduct a retrospective review of the National COVID Cohort Collaborative (N3C) database to examine the relationship between chronic treatment with medications known to impact potential COVID-19 targets and disease course. Subject medications include RAAS inhibitors, such as angiotensin- converting enzyme (ACE) inhibitors and angiotensin II receptor type I blockers (ARBs), bradykinin receptor antagonists, vitamin D supplements, vasodilators and vasoconstrictors (e.g. nitrates and ergots), anti-inflammatory drugs including non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids, and interferons . Using the World Health Organization (WHO) Clinical Progression Scale and the American College of Emergency Physicians (ACEP) Classification COVID Severity Scale, correlation analyses will be used to identify relationships between chronic medication therapies and COVID disease course. ", "accessing_institution": "Auburn University" }, { "uid": "RP-B911D9", "title": "Comparing the impacts of respiratory virus infections (SARS-CoV-2, RSV, influenza) on new-onset type-1 and -2 diabetes risk", "task_team": false, "dur_project_id": "DUR-83A615B", "workspace_status": "ACTIVE", "lead_investigator": "Elaine Yu", "research_statement": "This retrospective, multi-site cohort study will be among study participants of the National Institutes of Health (NIH) National COVID Cohort Collaborative (N3C). The three-fold study objectives will be to: 1) compare the risk of incident type 1 or 2 diabetes diagnosis after SARS-CoV-2 infection, relative to influenza and RSV infections; 2) evaluate the impact of SARS-CoV-2 reinfections on the risk of incident type 1 or 2 diabetes diagnosis, relative to other respiratory viral reinfections; and 3) assess the influence of type 1 or 2 diabetes status on the risk of SARS-CoV-2 reinfections. We will use predictive models (80% training, 20% test) with K-fold cross-validation. ", "accessing_institution": "Vitalant Research Institute" }, { "uid": "RP-5F49F3", "title": "Introduction to Analyzing Real-World Data Using the National COVID Cohort Collaborative, Spring 2024", "task_team": false, "dur_project_id": "DUR-84D88B6", "workspace_status": "CLOSED", "lead_investigator": "Shawn O'Neil", "research_statement": "This project is associated with the Education and Training Domain Team. Description: Observational Electronic Health Record (EHR) data are an increasingly accessible resource for medical research. Such records are often structured in a Common Data Model (CDM), from which question-relevant information must be extracted, aggregated, and analyzed. This course introduces students to real-world EHR analysis with the National COVID Cohort Collaborative (N3C; https://covid.cd2h.org), a secure cloud-hosted database with billions of records about millions of patients from dozens of sites across the US. We?ll discuss medical vocabularies and the OMOP CDM, practice querying and analyzing tabular data with SQL and R, and see examples of common statistical techniques for observational data. Students will additionally have an opportunity to investigate COVID-19-related questions of their own interest as a course project.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-1D3246", "title": "Immunity Modeling for Infection Outcomes of Interactions between Acute Viral Respiratory Infections", "task_team": false, "dur_project_id": "DUR-8544401", "workspace_status": "ACTIVE", "lead_investigator": "Rachel Baccile", "research_statement": "The COVID-19 pandemic caught the global community unprepared, leading to widespread lockdowns and other non-pharmaceutical interventions to stop the spread of the virus before effective vaccines were developed. Due to extremely limited immunity in the human population, the virus continued to circulate, catalyzed by emerging variants that were more transmissible and/or immune-evading and despite rapid vaccine development and rollout. Human behavior and social contact patterns were significantly altered for more than two years, interfering with patterns of seasonal respiratory virus circulation. It is hypothesized that the COVID-19 will eventually become a seasonal infection with increases in transmission primarily driven by factors such as temperature, humidity, and human behavior rather than population-level immunity.1\n\nImmunity is complex and varies according to the timing and context of inciting exposures. SARS-CoV-2 infection and vaccination elicits complex immune responses that may affect susceptibility to other acute viral respiratory infections (ARIs). The converse may be true as well: exposure to influenza, respiratory syncytial virus (RSV), and other ARIs may alter immunity to SARS-CoV-2. The concept of viral interference has been described previously and involves complex immunological responses and host-pathogen interaction dynamics, which may elicit positive or negative feedback loops between different viral infections.2 For example, recent work suggests that prior mild COVID-19 disease enhances influenza vaccine response.3\n\nDuring the winter of 2022-2023, the medical community observed different patterns of influenza and RSV epidemics compared to the years preceding the COVID-19 pandemic. Elevated infection rates observed in young children may be explained by the lack of exposure and hence immune priming during the critical early stages of life. While not as dramatic, higher infection rates and severity were also observed in other age groups, suggesting that lack of exposure to respiratory viruses resulting from COVID-19 non-pharmaceutical interventions had broader implications beyond COVID-19.\n\nOur primary objective is to identify and describe patterns of immunization (SARS-CoV-2, influenza) and repeat acute viral respiratory infections during 2018-2023. Our secondary objective is to identify individual immune priming events (infections, vaccinations) and their combinations that predict subsequent influenza, COVID-19, and ARI outcomes, including infections and disease severity.\n", "accessing_institution": "University of Chicago" }, { "uid": "RP-410864", "title": "COVID-19 in Adult Congenital Heart Patients", "task_team": false, "dur_project_id": "DUR-893B569", "workspace_status": "CLOSED", "lead_investigator": "Alexander Karius", "research_statement": "Adult congenital heart patients have unique cardiac anatomy, which can affect physiologic parameters. COVID-19 infection may differentially impact this patient population, necessitating unique treatment considerations. We seek to quantify and present how the COVID-19 pandemic has impacted patients with adult congenital heart disease. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-F3805C", "title": "Causal Estimate of Impact of Antidepressants and Antipsychotics on Treating Delirium Patients in the ICU due to COVID-19", "task_team": false, "dur_project_id": "DUR-89E3CE6", "workspace_status": "CLOSED", "lead_investigator": "Riddhiman Adib", "research_statement": "Delirium occurs in about 80% of cases in the Intensive Care Unit (ICU) and is associated with an extended hospital stay, increased mortality, and other related issues. The COVID-19 pandemic is another probable causal factor of ICU admission, and longer ICU stays. Delirium has no biomarker-based diagnosis and is frequently treated with antipsychotic drugs (APD). There has yet to be any randomized controlled trial to compare the efficacies of APD in the treatment of Delirium for COVID-19 versus non-COVID-19 patients. We plan to use the Causal inference framework to look for the underlying causal model, leveraging the availability of extensive observational data on COVID-19 patients. We focus on building a structural causal model for delirium patients triggered by COVID-19 in the ICU using large observational data sets through connecting various covariates correlated with Delirium. In our previous work, we have curated the Delirium population subgroup MIMIC-Delirium from large observational publicly available ICU data (MIMIC-III) and explored relevant causal model & effects.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-2A413A", "title": "Geographic Patterns and Temporal Trends of COVID-19 Outcomes: a National Study Using the N3C Data Enclave", "task_team": false, "dur_project_id": "DUR-8C944B2", "workspace_status": "CLOSED", "lead_investigator": "Joseph Bailey", "research_statement": "As the COVID-19 pandemic expanded through the United States, different geographic areas have experienced markedly different rates of infection and enacted different policies about masking and social isolation. Furthermore, patients in different geographic areas have disparities in access to health care. This project aims to compare the burden of COVID-19 across the spectrum of geographic environments in the United States using clinical data from large a multi-site observational cohort.", "accessing_institution": "Northwestern University" }, { "uid": "RP-CEAA8C", "title": "Data Utility Verification using EHR from CURE ID", "task_team": false, "dur_project_id": "DUR-908DDE9", "workspace_status": "ACTIVE", "lead_investigator": "Ruth Kurtycz", "research_statement": "To demonstrate the utility of the EHR data collected in the CURE ID Drug Repurposing project funded by the FDA, we are executing a series of trial emulation analysis with COVID-19 as a use case, using causal inference modeling to replicate the trial methodology on observational data. To date, we have explored use of dexamethasone and remdesivir within the extracted EHR data in CURE ID. Access to NC3 limited data is requested to verify the results of these emulation analyses on a similar data source. Demonstrating the utility of observational data within treatment of COVID-19 will allow for use of these data and methods in evaluating treatment options of rare and emerging diseases in the future.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-BA6A9C", "title": "Development of a COVID-19 clinical staging model and evaluation of risk factors for COVID-19 acquisition and disease progression using computer modeling", "task_team": false, "dur_project_id": "DUR-A078846", "workspace_status": "CLOSED", "lead_investigator": "Yufu Zhang", "research_statement": "We have developed multiple models and analyses of our patients with COVID-19 at UI Health to help determine who is more likely to pass away, require elevated levels of care or get intubated. We have also compared the effectiveness of multiple published computer models on our data set. Many of these external models have performed poorly on our data set. We would like to see how our internally developed models and these external models perform on a larger data set as found in the N3C Data Enclave. ", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-F87C00", "title": "Tobacco Use and COVID-19 Investigating COVID-19 Severity and Pulmonary Outcomes", "task_team": false, "dur_project_id": "DUR-95D4478", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "Tobacco use is a well-established risk factor for respiratory diseases and has been implicated in worse outcomes for COVID-19 patients. Smoking is known to impair immune function, reduce lung capacity, and increase the likelihood of severe respiratory infections. Given COVID-19?s impact on the respiratory system, smokers may be at a higher risk of severe complications, including acute respiratory distress syndrome (ARDS), prolonged ventilator dependency, and long-term pulmonary dysfunction.\nThis study aims to assess the effects of smoking on COVID-19 severity and recovery by evaluating the relationships between smoking status, ventilator dependency, and pulmonary recovery trajectories. \nUsing de-identified electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C), this study will provide data-driven insights into how tobacco use affects COVID-19 prognosis. The findings will contribute to public health strategies, clinical guidelines, and smoking cessation programs tailored to reduce COVID-19 complications in at-risk populations.", "accessing_institution": "login.gov" }, { "uid": "RP-23C1C2", "title": "Temporal analysis of peri-operative complications following COVID19 infection in patients undergoing autologous breast reconstruction. ", "task_team": false, "dur_project_id": "DUR-98E14CE", "workspace_status": "ACTIVE", "lead_investigator": "Jini Jeon", "research_statement": "Autologous breast reconstruction is a crucial procedure for many individuals seeking to restore their breast following mastectomy, with outcomes often influenced by various factors, including patient specific characteristics and past medical history. Amid the COVID-19 pandemic, attention has turned to understanding potential complications in surgical procedures, particularly for patients with a history of COVID-19 infection. The systemic effects of COVID-19, such as cytokine dysregulation, hypercoagulability, and compromised immune response, may pose challenges to the process of tissue healing and integration after autologous breast reconstruction. Limited evidence suggests that a history of COVID-19 infection may increase the risk of complications in autologous breast reconstruction. Potential complications such as flap necrosis, infection, delayed healing, and suboptimal cosmetic outcomes may be exacerbated in individuals with a history of COVID-19. The inflammatory response triggered by the virus could disrupt vascular supply to the graft, impede tissue integration, and compromise overall surgical success. Moreover, the immune-suppressive effects of COVID-19 infection might further compromise the body's ability to mount an effective immune response during the healing process, potentially leading to an increased risk of postoperative infections and other complications. Currently, there is very limited evidence regarding the specific relationship between history of COVID-19 infection and complications in autologous breast reconstruction. Further investigation is necessary to establish a definitive causal relationship and to understand the interplay of various factors such as temporal influences, patient comorbidities, and disease management. By utilizing data sourced from the National Covid Cohort Collaborative (N3C), which documents various clinical data points throughout the COVID-19 pandemic, this study aims to uncover the potential relationship between history of the viral infection and surgical outcomes. ", "accessing_institution": "Albert Einstein College of Medicine" }, { "uid": "RP-03D4C1", "title": "Deep Learning based models to predict COVID-19 patients outcomes", "task_team": false, "dur_project_id": "DUR-998711D", "workspace_status": "ACTIVE", "lead_investigator": "Laila Gindy Bekhet", "research_statement": "There is an increased need for tools to help identify patients at high risk of clinical deterioration in an early phase. With the extensive use of electronic records and the availability of historical patient information, predictive models that can help identify patients at risk based on their history at an early stage can be a valuable adjunct to clinician judgment. Deep learning based models can help better predict patients' health outcomes using patients' clinical history information. We plan to train models that can predict different health outcomes on admission including mortality risk, intubation, and prolonged length of stay", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-6709FE", "title": "Investigating the Association Between SARS-CoV-2 Infection and Incidence of New Primary Cancers", "task_team": false, "dur_project_id": "DUR-9AC9C68", "workspace_status": "ACTIVE", "lead_investigator": "Divya Jariwala", "research_statement": "Objective: To explore the association between COVID-19 infection and the subsequent diagnosis of new primary cancers in individuals without prior cancer history\n\nProject Overview\nThe project seeks to investigate whether there is a statistically significant relationship between previous SARS-CoV-2 infection and the subsequent diagnosis of new primary cancers. By analyzing real-world data (RWD) from the N3C data enclave, the study will focus on patients with a documented history of COVID-19 infection, excluding individuals with a prior cancer diagnosis or known cancer risk factors such as smoking, alcohol abuse, or family history. Utilizing electronic health records (EHR) and retrospective longitudinal data, the research will assess cancer incidence rates in the COVID-19 cohort and aim to understand whether SARS-CoV-2 infection may contribute to biological disruptions that could increase the risk of developing new primary cancers\n", "accessing_institution": "Healthark Sensing Labs Pvt Ltd" }, { "uid": "RP-84A1BE", "title": "A Study of the Association between Glomerulonephritis and COVID-19", "task_team": false, "dur_project_id": "DUR-9B7A627", "workspace_status": "CLOSED", "lead_investigator": "Marie Ozanne", "research_statement": "Glomerulonephritis is an acute kidney injury (AKI) which causes the glomeruli, which are tiny filters in the kidneys, to swell. There are a variety of potential causes of glomerulonephritis, including infectious diseases, which can directly or indirectly lead to this disease (Mayo Clinic). Multiple case reports have described some form of glomerulonephritis in COVID-19 patients. Moreover, some studies on AKI have shown a strong link between patients hospitalized for COVID and AKI. Research studies focused on specific subtypes of glomerulonephritis in the context of COVID-19 infection are few, and mostly regional. Based on the existing research, it is unknown whether there is a true association between COVID-19 and glomerulonephritis in the broader U.S. population, or whether the two disease states are independent, but have occurred simultaneously. In this project, we aim to quantify the prevalence of glomerulonephritis in COVID-19 positive patients, and compare it to the general population. We also aim to identify demographic and clinical factors associated with glomerulonephritis in COVID-19 positive patients, and compare them to the general population.", "accessing_institution": "Mount Holyoke College" }, { "uid": "RP-287159", "title": "Describing the Natural History of COVID-19-associated Invasive Fungal Infections using the National COVID Cohort Collaborative (N3C) Database ", "task_team": false, "dur_project_id": "DUR-9CE7E65", "workspace_status": "CLOSED", "lead_investigator": "Benjamin Papadopoulos", "research_statement": "Since the start of the COVID-19 pandemic, over 220 million people have been infected, and over 45 million killed by the disease. Patients with severe COVID-19 infections are exposed to many invasive fungal infection (IFI) risk factors due to tissue necrosis, epithelial damage, and antibiotic and immunosuppressant treatments. In the largest dataset to date, 9.8% of patients ventilated for severe COVID-19 had pulmonary aspergillosis, an IFI. Despite similar severity scores on admission, aspergillosis was associated with greater peak severity scores, treatment periods, and length of stay. IFIs are associated with significant attributable mortality in other conditions, including influenza, suggesting that some patients may have died because of these secondary infections, not COVID-19. If expediently diagnosed and treated, mortality in patients with IFIs can be lowered. However, estimates of IFI incidence vary widely, and little is known if patients are dying from or with their COVID-19 associated IFI (CAIFI). To date, reports have been limited to single center experiences with limited generalizability and scientific rigor. There is an urgent need to understand the epidemiology and natural history so that clinicians can be aware and appropriately treat CAIFIs. The National COVID Cohort Collaborative (N3C) database represents the largest collection of COVID-19 patient data with a wide geographic diversity. We propose to use the limited data set (LDS) which maintains the protected health information for our study. By using data from N3C, we will provide the first inclusive, multicenter incidence of CAIFI in the United States, describe the incidence, risk-factors, and associated mortality. ", "accessing_institution": "Washington University in St. Louis" }, { "uid": "RP-E6674A", "title": "Collorative Filtering for COVID Drug Recommendations", "task_team": false, "dur_project_id": "DUR-A2557C1", "workspace_status": "CLOSED", "lead_investigator": "Nicholas Sorrells", "research_statement": "\nThis project aims to use collaborative filtering to analyze the effects of various drugs on COVID patients? suffering from clinical depression over time and whether these effects are different for patients without COVID. These will then be compared to estimations using logistic regression, content-based filtering, and machine learning. Unlike aforementioned methods, the collaborative filtering aspect of this project will not involve covariates, but rather look for recommendations solely on patient outcomes. Through the process of serendipity, these can identify preferences and similarities that may be missed by analysis of covariates. As we will not have data on patients? approval of the effectiveness of medications, we will estimate these outcomes by the change in depression severity over time, and form a similarity matrix identifying similar users based on similar drug regimens. If similar users share positive outcomes of 3 different medications, but some users with positive results also takes a 4th medication, that can be seen as a recommendation. This will be used on test data to evaluate the accuracy of predictions. This can be compared to the results of logistic regression and other methods ", "accessing_institution": "Louisiana State University Health Sciences Center - New Orleans" }, { "uid": "RP-4F4BE1", "title": "Medical Device Demand Modeling", "task_team": false, "dur_project_id": "DUR-A54B8E9", "workspace_status": "ACTIVE", "lead_investigator": "Donald Richardson", "research_statement": "The COVID-19 pandemic placed health systems under unprecedented strain from numerous perspectives (human, financial, time, equipment/resources). Supply chains and demand for medical devices (broadly understood) were severely disrupted. Health system planning for future shocks via scenario modeling could be improved though a better understanding of the complex relationships between the dynamics of population burden (i.e. time-varying incidence and severity), medically indicated/provision of procedures/care, and medical device usage. We propose to leverage information available within the N3C Data Enclave to characterize these relationships between COVID and non-COVID patients, severity, timing of admission/care-seeking and the distribution of CPT codes. We will examine this over the course of the pandemic, across US regions, and between high and low(er) burden time periods. Where possible, we will seek to create distributions mapping COVID patient severity and other characteristics to the probability of the use of specific procedures and medical device types. Such mappings can inform future modeling of demand for medical device under a range of hypothetical (or observed) health system strains.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-B67DDA", "title": "Real-World COVID-19 Treatment Tracking and Performance ", "task_team": false, "dur_project_id": "DUR-A6A7C9D", "workspace_status": "ACTIVE", "lead_investigator": "Therese Tripler", "research_statement": "At the onset of the COVID-19 pandemic in early 2020 the Open Data Portal (ODP) https://opendata.ncats.nih.gov/covid19/ was developed as a tool to quickly share COVID-19 repurposing data and experiments for the then current FDA approved drugs. The ODP has grown into a multi-faceted data source including animal models, standardized assays, in vivo and in vitro therapeutic data, and therapeutic efficacy against circulating SARS-CoV-2 variants. In addition, ODP will release a curated database that tracks publications of real-world evidence of COVID-19 treatments. ODP is an invaluable tool for rapid digestion and understanding of the efficacy of treatments against evolving variants. Linking curated data from ODP with N3C?s electronic health records will provide an opportunity to compare curated datasets, such as in vitro treatment effectiveness with a dataset on treatment effectiveness from a clinical setting sourced through N3C?s electronic health records database. This provides the opportunity to explore questions such as: How does a treatment perform in a clinical versus laboratory setting? Are the trends in treatment effectiveness similar in vitro compared with a clinical setting? This may offer insights for researchers from academia, industry, and government to make informed decisions regarding drug development, precision healthcare, and research funding. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-B8F38C", "title": "Impact of Preoperative Dysphagia on Postoperative Outcomes", "task_team": false, "dur_project_id": "DUR-A74B647", "workspace_status": "ACTIVE", "lead_investigator": "Seth Cohen", "research_statement": "Dysphagia is a common condition in middle-aged and older populations, has risks of malnutrition, and can adversely impact patient recovery after surgery. Yet, preoperative dysphagia is not typically assessed. Current evidence shows that there is a relationship between covid and the development of swallowing problems (dysphagia) as well as between covid and malnutrition. Furthermore, the care of dysphagia and malnutrition may be different in people who have covid compared to people who do not have covid. This study will examine the relationship between covid, dysphagia, and malnutrition on postoperative outcomes in patients. This study will also examine dysphagia and nutrition-related care patterns in middle-aged and older patients with preoperative dysphagia and/or malnutrition among patients with covid compared to patients without covid. ", "accessing_institution": "Duke University" }, { "uid": "RP-20D91A", "title": "Association between GLP1 receptor agonist (GLP1-RA) and sodium glucose co-transporter 2 inhibitor (SGLT2i) use and COVID-19 outcomes: A national retrospective cohort study", "task_team": false, "dur_project_id": "DUR-AA83B51", "workspace_status": "CLOSED", "lead_investigator": "Trine Julie Abrahamsen", "research_statement": "Emerging evidence from the COVID-19 pandemic suggest that patients with type 2 diabetes comprise a significant portion of the affected population and are at higher risk for severe outcomes including hospitalization and death yet it remains largely unknown how pre-morbid medication may impact outcomes of COVID-19 in patients with type 2 diabetes. Several medications have biologically plausible mechanisms with relevance for patients with diabetes among others including ACE inhibitors, metformin, and DPP4-inhibitors. Recent large cardiovascular outcome trials and subsequent metanalyses have demonstrated that some glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose-linked cotransporter 2 inhibitors (SGLT2i) are associated with a reduction of cardiovascular events and all-cause mortality among the same high-risk populations who show higher susceptibility to severe COVID-19 and increased mortality. Yet, no studies have examined the class effect of these newer anti-hyperglycemic of mortality and other outcomes in the setting of COVID-19 infection. These data are critical because therapeutics represent a highly actionable intervention point to improve outcomes from both the inpatient and outpatient setting for a large population of patients with inherently high risk for COVID-19 associated mortality. To address this gap and inform evolving care guidelines for patients with medication-managed type 2 diabetes during the COVID-19 pandemic, this study aims to characterize the association of use of GLP1-RA and SLGT2i with COVID-19 outcomes using real world data from the National COVID Cohort Collaborative (N3C). We will consider the well-studied and commonly used class of dipeptidyl peptidase-4 inhibitors (DPP4i) as the active comparator drug to avoid confounding by indication.", "accessing_institution": "Novo Nordisk" }, { "uid": "RP-4B5DE6", "title": "Impact of Dermatology Consults on COVID-19 Inpatient Outcomes", "task_team": false, "dur_project_id": "DUR-AC70EAB", "workspace_status": "CLOSED", "lead_investigator": "Julia Mhlaba", "research_statement": "The novel coronavirus disease of 2019 (COVID-19) is associated with several dermatologic manifestations. Case series have reported a wide range of skin findings including pernio-like lesions, erythematous macular or papular rashes, morbilliform or varicelliform eruptions, urticaria, vesicles, petechiae, purpura, livedo reticularis-like lesions, and acro-ischemic lesions (Freeman et al. 2020). In many cases, the presentation of these cutaneous signs appeared concurrently with or prior to the onset of other COVID-19 symptoms. Thus, dermatologist involvement may expedite COVID-19 diagnosis and improve management. \nWe are requesting Level 2 de-identified data to compare mortality rates, length of hospitalization, and treatment modalities for patients with COVID-19 who had a dermatology provider involved in their care versus those who did not. ", "accessing_institution": "Northwestern University" }, { "uid": "RP-4968F7", "title": "Examination of high flow nasal oxygen use in COVID-19", "task_team": false, "dur_project_id": "DUR-B995542", "workspace_status": "CLOSED", "lead_investigator": "Frances Mabrey", "research_statement": "In patients with COVID-19 and severe acute hypoxemic respiratory failure (AHRF), high flow nasal oxygen (HFNO) is increasingly being used as a non-invasive form of respiratory support, often to circumvent or delay the need for invasive mechanical ventilation (IMV). However, one third of patients eventually required IMV. The trade off in harms between HFNO and IMV are unknown, and equipoise exists on the optimal criteria and timing at which a patient on HFNO should be supported with IMV. There remains much to be understood about how to effectively predict HFNO failure in a manner that will improves outcomes/ minimize harms. To address this knowledge gap, we will use an N3C cohort of COVID-19 patients with severe AHRF to examine the variability in the use of existing thresholds to guide transitions to IMV and to improve prediction of HFNO failure.", "accessing_institution": "University of Washington" }, { "uid": "RP-77BBB6", "title": "Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data", "task_team": false, "dur_project_id": "DUR-BA87BBC", "workspace_status": "CLOSED", "lead_investigator": "Nicole Weiskopf", "research_statement": "During the COVID-19 pandemic, there is an immediate need for high-quality data for studies that support patient care, predict outcomes, identify and evaluate treatments, allocate resources, and make operations and policy decisions. While prospective research produces higher-quality evidence, retrospective studies that reuse clinical data can be executed in a shorter time frame and for less cost, both of which are crucial for research in a pandemic. Unfortunately, it has been shown that the usefulness and validity of available COVID-19 data are constrained by various forms of selection bias and information bias, which may lead to non-valid findings in research and analytics and disparities in resulting healthcare practices. The objective of the proposed work is to study the selection and information biases present in clinically derived COVID-19 datasets We will extract data indicating COVID-19, as well as a set of social determinants of health that are commonly associated with healthcare utilization and access. To test for the presence of selection bias, we will construct and compare categorical probability distributions for each social determinant across COVID-19 cases in each data source. Differences in these distributions will indicate selection bias in one or more of the data sources. Next we will determine information bias by extending and adapting tests for missingness and other forms of information bias in the COVID-19 datasets to determine if the quantity and quality of these data vary with respect to clinical factors and those related to social determinants of health. This proposal therefore addresses a significant gap in knowledge: understanding not just the disparities in who is impacted by COVID-19, but who is represented by the data we have available for learning more about the disease. The identification and estimation the influence of social determinants of health on selection bias and information bias in COVID-19 data can guide the use of statistical and analytic approaches that can improve the external and internal validity of research and analytics that rely on these data, including estimates of disease prevalence, understanding the natural course of COVID-19, and identifying patients who are at risk for severe disease.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-6C7450", "title": "[N3C Operational] N3C Data Processing in NCATS HPC Systems", "task_team": false, "dur_project_id": "DUR-BB40587", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "The National Center of Advancing Translational Sciences (NCATS) is pursuing a world-class scientific computing Infrastructure to meet present and future needs in high-end data analytics and extreme-scale computing to accelerate the development of new procedures in the prevention and treatment of diseases, the discovery of more effective drugs, and to drive innovation in therapeutic methods. In 2020, the COVID-19 global pandemic has presented multiple computational challenges requiring the need of powerful, yet flexible scientific computing systems. Systems that can help in the sequencing of the various virus variants, accelerate vaccine development, find new therapeutic procedures, estimate the effectiveness of lockdowns, simulate post-pandemic scenarios, and in general, enhance our preparedness for future pandemics. A new scientific computing infrastructure serving NCATS will provide advanced computing platforms utilizing state-of-the-art techniques in traditional intensive computing, ML, and DL to meet challenges for data processing and analytics, i.e., for the N3C Data Enclave to answer critical research question to address COVID-19, paving the way to proactively react to a future pandemic. The NCATS scientific computing infrastructure (both on-premises and cloud-based platforms) will integrate operations with the N3C Data Enclave in Palantir.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-B95AA4", "title": "Real-World Effectiveness of Sotrovimab in Early Treatment of Non-Hospitalized Patients with COVID-19 During the Omicron Variant Period in the United States", "task_team": false, "dur_project_id": "DUR-BDE6A4D", "workspace_status": "CLOSED", "lead_investigator": "Priyanka Bobbili", "research_statement": "There is a gap in evidence demonstrating the effectiveness of Sotrovimab for the treatment of COVID-19 in the real-world. This retrospective cohort study aims to evaluate the real-world effectiveness of early treatment with Sotrovimab in reducing hospitalization and mortality among patients with mild/moderate COVID-19, during the Omicron variant period in the United States. Outcomes for patients with COVID-19 who received early treatment with Sotrovimab will be compared to those for untreated patients, with statistical adjustments made for confounding variables. ", "accessing_institution": "Analysis Group Inc" }, { "uid": "RP-0115A5", "title": "Adjustment for Linkage Uncertainty in the Secondary Analysis of Linked COVID and CMS Claims Data", "task_team": false, "dur_project_id": "DUR-C1AEC30", "workspace_status": "ACTIVE", "lead_investigator": "Martin Slawski", "research_statement": "Linkage of a primary data source such as COVID clinical records and auxiliary data sources such as claims data bears much potential for capturing aspects that cannot be inferred from a single data source, for example, patient-specific healthcare costs. However, record linkage often relies on fuzzy matching techniques that may lead to incorrect and missing links. Accounting for the resulting uncertainty in downstream statistical analysis is a critical step in maximizing the utility of linked data sets. Supported by NSF funding, the investigator has developed methodology and associated software for this task. Putting these developments to the test on large-scale applications will facilitate the refinement of these tools and provides opportunities for leveraging the full potential of linked COVID clinical records. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-6DC499", "title": "Prediction of Symptoms Associated with Long COVID", "task_team": false, "dur_project_id": "DUR-C4581CD", "workspace_status": "CLOSED", "lead_investigator": "T. M. Murali", "research_statement": "Some patients infected with SARS-CoV-2 manifest a variety of symptoms after the acute phase of the infection. These conditions may persist for a time period longer than expected. New conditions may also develop long after the initial COVID-19 infection has ended. This manifestation of symptoms is termed ?Long COVID? and has been observed in patients with varying severity of the acute phase of COVID-19 infection. It is considerably challenging to predict whether a patient who tests positive for COVID-19 will develop long COVID in the future. This project seeks to develop a machine learning model to predict whether a patient who tests positive for COVID-19 will develop specific new, persistent symptoms associated with long COVID, such as dyspnea (difficulty in breathing). This model will use lab test results, symptoms experienced, and drugs administered to the patients during the COVID-19 infection along with their demographics. The project will create a reusable framework developed using Level 2 (de-identified data) from the N3C cohorts that will be applicable to other symptoms as well. Another important goal of the project will be to validate the model across sites and over time.", "accessing_institution": "Virginia Tech" }, { "uid": "RP-3E31EE", "title": "Vitamin D deficiency in diabetic patients with COVID-19 ", "task_team": false, "dur_project_id": "DUR-859381A", "workspace_status": "CLOSED", "lead_investigator": "Nikhila Gandrakota", "research_statement": "Vitamin D deficiency is associated with increased risk of infections and it has been associated with increasing morbidity and mortality in COVID -19 infections. Diabetes is also one of the most common co-morbidities linked to the severe outcomes of COVID-19. Evidence has shown a pathophysiologic link between diabetes and COVID-19 which is more evident in Vitamin D deficient patients. This study plans to explore relationship between vitamin D deficiency and if its supplementation would affect COVID -19 outcomes in patients with diabetes.", "accessing_institution": "Emory University" }, { "uid": "RP-0E7622", "title": "Neurological Complications Post-Covid-19 in Patients with Diabetes", "task_team": false, "dur_project_id": "DUR-CB09CC3", "workspace_status": "ACTIVE", "lead_investigator": "Sonya Dunlap", "research_statement": "Diabetes is associated with a wide range of neurological complications, of which, the most common is symmetric diabetic sensorimotor polyneuropathy (DSPN). This occurs in approximately 55% in people with type 1 diabetes mellitus (T1DM) and over 45% in those with type 2 diabetes (T2DM). Importantly, DSPN is a risk factor for mortality in diabetes. Therefore, prevention and treatment of DSPN are important not only in reducing the patient burden from this common complication but also in reducing overall morbidity and mortality in diabetes. Patients with diabetes mellitus (DM) are more likely to have severe complications with COVID-19 (SARS-CoV-2). In addition, recovering COVID-19 patients develop several neurological complications including peripheral neuropathy. The current study will determine if patients with concomitant COVID-19 infection and diabetes are at higher risk of neurological complications compared to those with diabetes alone.", "accessing_institution": "Charleston Area Medical Center" }, { "uid": "RP-6E96D2", "title": "Machine Learning Models to Predict Neurological Sequelae in Post-COVID-19 Patients: Development of Cognitive Impairment, and Mood Disorders by Age, Race and Gender", "task_team": false, "dur_project_id": "DUR-CB74337", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "This research project seeks to develop machine learning models to predict the onset of neurological sequelae, including cognitive impairment and mood disorders, in post-COVID-19 patients. Utilizing complex network analysis, we aim to identify patterns and clusters of neuro-cognitive symptoms associated with long COVID. By examining the impact of demographic factors such as age, race, and gender, our models will uncover critical insights into the risk profiles and progression of these conditions. The analysis will leverage patient data from the N3C enclave to build robust predictive models, ultimately aiding in early diagnosis and targeted intervention strategies for affected individuals.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-3F61B2", "title": "Impact of endocrine system failures on Long COVID diagnoses: Analyzing the effects of diabetes, hypo/hyperthyroidism, and adrenal insufficiency", "task_team": false, "dur_project_id": "DUR-CE1C252", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "This study investigates how endocrine disorders like diabetes, hypo/hyperthyroidism, and adrenal insufficiency affect COVID-19 outcomes, specifically focusing on hospital admission rates and length of stay (LOS). Previous research on this topic suggests that SARS-CoV-2 infection may lead to long-term effects on the endocrine system, such as reduced thyroid function and lowered secretion of prolactin and male sex hormones. We aim to identify key predictive factors that influence these outcomes. By analyzing patient data, we will explore the interactions between these conditions and COVID-19. The results will offer insights for healthcare providers to develop targeted treatments and improve resource management, ultimately enhancing care for COVID-19 patients with endocrine disorders\n\n", "accessing_institution": "Axle Informatics" }, { "uid": "RP-112499", "title": "Renal Disease", "task_team": false, "dur_project_id": "DUR-D400420", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "Problem statement: Our previous work in COVID-19 positive inpatients (preprint) identified a large population of people with undiagnosed AKI that suffered from significantly increased risk of mortality. Underdiagnosis of mild disease is a known issue among the nephrology community, which led to the development of KDIGO criteria that define objective rules for diagnosing and staging kidney injury. Still, the lack of an existing diagnostic or billing code in patients with laboratory evidence of disease may be influenced by several factors, including demographics, social determinants of health, and the existence of issues perceived to be more important or severe. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-0C9802", "title": "COVID-19, Social Determinants of Health, and Orthopaedic Surgery", "task_team": false, "dur_project_id": "DUR-D5106C4", "workspace_status": "CLOSED", "lead_investigator": "David Patch", "research_statement": "The purpose of this study is to analyze the demographic, clinical characteristics, social factors, treatments and outcomes of interest among individuals in the National COVID Cohort Collaborative.\n\nOther objectives include developing concept sets, protocol prototypes, code and documentation to use best practices developed in cross-cutting domain teams (ML, ADM&S) to investigate the described research questions. ", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-020D1B", "title": "Analysis of Time Windows as Determining Factors for COVID-19 Patient Outcomes", "task_team": false, "dur_project_id": "DUR-D52146E", "workspace_status": "CLOSED", "lead_investigator": "Arti Patel", "research_statement": "Protocol that determines if a patient should be tested for COVID-19 may depend on presence of certain symptoms and vital signs. If presence of certain symptoms in combination with the length of time before getting tested has an impact on patient outcomes, we would like to find the optimal time window that leads to better patient outcomes.", "accessing_institution": "University of Virginia" }, { "uid": "RP-987966", "title": "Longitudinal data analysis of Long COVID care for disease management and well-being", "task_team": false, "dur_project_id": "DUR-D6DBD50", "workspace_status": "CLOSED", "lead_investigator": "Jiayan Zhou", "research_statement": "Background: Ongoing symptomatic COVID-19 is manifestations of COVID-19 from 4 weeks up to 12 weeks, and post-COVID-19 syndrome (Long COVID) continuation for more than 12 weeks. Due to the lack of evidence of therapies for long COVID prevention and management, patients used diverse self-prescribed medications such as vitamin C, traditional medicines, and chloroquine. Off-label use, unsafe doses, and harmful drug-drug interaction become potential risks of self-prescription for long COVID. Therefore, therapies that potentially manage long COVID are in desperate need of studies to obtain the evidence, which can guide long COVID management globally.\nMethods: We will extract the longitudinal data from the follow-up visit records. Chronic diseases will be determined by the International Classification of Diseases, edition 11 (ICD-11). The statistical analysis includes the missing data imputation, association of each variable with the long COVID time-to-event using the Cox proportional hazard model, long COVID binary outcome using the logistic regression model, and long COVID continuous variables using linear regression model, with age, gender, BMI, comorbidities, routine drug intake as covariates.\nConclusion: The findings of this study can provide long COVID patients with optimal evidence-based approaches to relieve the suffering from symptoms, can help healthcare providers make clinical decisions about long COVID treatment, can help researchers understand the symptoms and signs of long COVID, and can promote the policymakers to construct better self-management guidelines based on evidence.", "accessing_institution": "Stanford University" }, { "uid": "RP-228995", "title": "Investigate associations between vaccination status with long COVID-symptoms and HRQoL in both outpatients and inpatients. ", "task_team": false, "dur_project_id": "DUR-E4782B2", "workspace_status": "CLOSED", "lead_investigator": "Jia Li", "research_statement": "The Coronavirus Disease (COVID-19) pandemic has affected the global economy and widely reshaped human life. COVID-19 disease has produced a wide range of symptoms and severity, including cough, shortness of breath, fatigue, headache, loss of taste or smell, diarrhea, etc. The depth and breadth of the physiological and psychological consequences have contributed to reduced quality of life in these individuals. Various studies have shown that a considerable proportion of COVID-19 patients continued to experience functional limitations and symptoms even 6 months after their first diagnosis. In addition, a few studies have begun investigating the effects of vaccination on long-term COVID. However, the association between their post-vaccination symptoms and vaccination remains a matter of controversy. Therefore, we intend to explore the relationship between post-COVID symptoms and vaccination by using the de-identified data from N3C.", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-2B90D5", "title": "ssociations of BMI Dynamics and Nutritional Biomarkers with New-Onset Neuropsychiatric Symptoms in Acute COVID-19 Patients: Implications for Inpatient Nutrition Management ", "task_team": false, "dur_project_id": "DUR-D701750", "workspace_status": "ACTIVE", "lead_investigator": "Celine Tran", "research_statement": "COVID-19 infections have been linked to various clinical conditions, including new-onset neuropsychiatric symptoms such as insomnia, mood and anxiety disorders, delirium, and encephalopathy. Considering these observations, the connection between nutritional status and susceptibility to neuropsychiatric symptoms during acute COVID-19 infection remains unclear. Given the common occurrence of poor nutritional statuses such as malnutrition, obesity, high glucose, and nutritional deficiencies among individuals with acute COVID-19 cases, which may render them more vulnerable to adverse outcomes, it is important to understand the potential influence of poor nutritional status on the development of neuropsychiatric symptoms in COVID-19. The present study will utilize de-identified data from N3C to explore how nutritional status and BMI dynamics relate to the onset of neuropsychiatric symptoms in hospitalized individuals with acute COVID-19. Nutritional status will be assessed using BMI, various vitamin and mineral biomarkers, and metabolic markers such as glucose and lipid profile. The BMI and nutritional profiles of patients with and without new-onset neuropsychiatric symptoms will be compared to assess the association between BMI fluctuations and the development of these symptoms, as well as the role of nutritional biomarkers in this relationship. Through this investigation, we aim to provide insights that can guide nutrition management strategies for COVID-19 and similar infectious diseases. By optimizing nutritional support during COVID-19 hospitalization and reducing the risk of developing neuropsychiatric symptoms, this study seeks to improve patient outcomes and contribute to enhanced care practices in managing COVID-19 patients. ", "accessing_institution": "University of Southern California" }, { "uid": "RP-59C3AB", "title": "Taste Changes (Dysgeusia) and COVID-19: a National COVID Cohort Collaborative (N3C) Study", "task_team": false, "dur_project_id": "DUR-D918B84", "workspace_status": "CLOSED", "lead_investigator": "Bing Chen", "research_statement": "In this application, we outlined innovative proposals to investigate the epidemiology of dysgeusia in patients with COVID-19. We will use the NCATS N3C Data Enclave to provide real-world evidence of the incidence of dysgeusia in patients with SARS-CoV-2 infection and determine the predictive value of such a symptom in the morality and severity of COVID-19. We need the variables to indicate whether patients had symptoms of dysgeusia, along with other symptoms as covariates. We also need patients? age, gender, comorbidities, and other epidemiological information to control confounding.", "accessing_institution": "New York University Langone Medical Center" }, { "uid": "RP-3C706E", "title": "[N3C Operational] COVID-19 Data Science Methodological Research Data User Request", "task_team": false, "dur_project_id": "DUR-E0D7786", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The [N3C Operational] COVID-19 Data Science Methodological Research project focuses on the is development of new and novel methodological approaches to study COVID-19 as well as teaching and development of best practices in data harmonization curation, machine learning, team science and collaborative analytics environments associated with on COVID-19. ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-DC4472", "title": "Examining Racial and Ethnic Differences in Sleep Outcomes During the COVID-19 Pandemic", "task_team": false, "dur_project_id": "DUR-E1B86D8", "workspace_status": "CLOSED", "lead_investigator": "Xiaoyue Liu", "research_statement": "Sleep plays an essential role in health and well-being. Since the beginning of the COVID-19 pandemic, there have been increased reports of sleep disturbances among individuals in the U.S. A recent study documented that 2 in 3 Americans experienced abnormal sleep during the pandemic. Emerging evidence has suggested a potential relationship between COVID-19 and adverse sleep outcomes. Racial and ethnic minority groups are disproportionately affected by COVID-19 and its related health complications. Currently, few studies have examined sleep outcomes in racial and ethnic minorities who are exposed to COVID-19. In addition, it is not clear how social factors affect sleep outcomes in racial and ethnic groups during the pandemic. Therefore, our study will focus on COVID-19 survivors and seek to investigate: 1) racial and ethnic differences in sleep outcomes, and 2) the relationship between social factors and sleep outcomes among racial and ethnic minority groups. The findings of this study will allow healthcare providers to identify minority populations who are vulnerable to sleep disturbances during the COVID-19 pandemic and develop tailored sleep interventions to reduce sleep disparities in those high-risk groups. ", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-E81AC0", "title": "Assessing the Differential Effects of ACE Inhibitors, ARBs, and SGLT2 Inhibitors in COVID-19 and Non-COVID Patients with a Single Kidney", "task_team": false, "dur_project_id": "DUR-E1CB158", "workspace_status": "ACTIVE", "lead_investigator": "Meng-Hao Li", "research_statement": "This study will investigate the impacts of selected medications on the recurrence or progression of kidney disease, including end-stage kidney disease (ESKD) requiring dialysis, chronic kidney disease (CKD) Stages IV and V, Focal Segmental Glomerular Sclerosis (FSGS), and patient mortality, in individuals with a single kidney due to nephrectomy following live kidney donation or cancer. Specifically, we will compare outcomes between patients who have contracted COVID-19 and those who have not, to determine whether the presence of SARS-CoV-2 modifies the effects of key renoprotective medications.", "accessing_institution": "George Mason University" }, { "uid": "RP-ADABA9", "title": "Acute Kidney Injury in COVID-19", "task_team": false, "dur_project_id": "DUR-E411C6E", "workspace_status": "CLOSED", "lead_investigator": "Moustafa Younis", "research_statement": "Hypercoagulability with microvascular involvement has been described in patients with severe COVID-19. According to a study performed on 44 ICU patients, thromboelastogram found a complete lack of clot lysis in 57% of the patients and that was associated with a high rate of kidney failure, need for hemodialysis and thromboembolic events. These results suggest that the development of microemboli and microthrombi in the context of hypercoagulability could be strong contributors to AKI. This highlights the potential role of anticoagulation in the prevention or treatment of AKI in patients with severe COVID-19. Early identification of kidney involvement in patients with COVID-19 and the use of preventive and/or therapeutic measures are vital to limit consequent AKI or progression to more severe stages thus reducing morbidity and mortality.\nAim: In our study, we aim to retrospectively investigate 1) the impact of anticoagulation on the rates of AKI , 2) the impact of anticoagulation on the rates of progression to RRT, 3) identify predictors of AKI in patients with COVID-19, 4) describe COVID patients at different d-dimer level", "accessing_institution": "University of Kansas Medical Center" }, { "uid": "RP-2B90E1", "title": "Long-Term COVID-19 Effects Identification from synthetic EHR data", "task_team": false, "dur_project_id": "DUR-E441325", "workspace_status": "ACTIVE", "lead_investigator": "Wenqi Shi", "research_statement": "The challenge of effectively addressing long-term COVID-19 symptoms hinges on the prompt and precise identification of affected patients. At present, there is no consensus on objective diagnostic test results, and the current method of manually reviewing patient-reported symptoms is labor-intensive and demands extensive clinical expertise. Moreover, risk assessment and personalized treatment are hindered by a knowledge gap and insufficient patient data. To tackle this urgent issue, an AI-enabled clinical decision support system is essential for the automatic identification of long COVID patients. By bridging the knowledge gap in clinical practice, the proposed system aims to deliver timely, personalized treatment, benefiting long-COVID patients, their families, and clinicians.", "accessing_institution": "Georgia Institute of Technology" }, { "uid": "RP-928C87", "title": "Headache Associated with COVID19 infection ", "task_team": false, "dur_project_id": "DUR-E488423", "workspace_status": "CLOSED", "lead_investigator": "Teshamae Monteith", "research_statement": "\nHeadache is associated with a large global burden of disease and is the second cause of disability worldwide. Moreover, headache is common symptom of COVID19 infection, which may be a systemic symptom, manifestation of nervous system involvement, or triggering of pre-existing primary headache such as migraine. Due to the public health impact of headache disorders, the goal of the study is to assess the incidence, prevalence, risks and persistence of the clinical spectrum of headache due to COVID19 infection. \n", "accessing_institution": "University of Miami" }, { "uid": "RP-D5AE34", "title": "RADx Long COVID Prediction Challenge", "task_team": false, "dur_project_id": "DUR-E6CBB51", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "The emergence of post-acute sequelae of SARS-CoV-2 (PASC) is presenting serious and ongoing impact on people?s health and the American health care system. While details on the prevalence, causes, treatment and consequences of PASC are actively being researched, growing evidence suggests that more than half of COVID-19 survivors experience at least one symptom of PASC at six months after recovery of the acute illness. Symptoms of fatigue, cognitive impairment, shortness of breath, and cardiac damage, among others, have been observed in patients who had only mild initial COVID-19 disease. Advancements in the software tools using Artificial Intelligence (AI)/Machine Learning (ML) approaches may enable the potential for providing clinical decision support on candidate prognostic factors and assessments of a patient?s risk to developing PASC. \n\nTo that end, we are conducting a community challenge within the National COVID Cohort Collaborative (N3C) enclave sponsored by the Rapid Acceleration of Diagnostics (RADx) initiative to engage with the machine learning community to develop risk prediction models for identifying COVID patients who are at risk of developing long COVID. We will establish a gold standard true positive dataset against which risk prediction models will be benchmarked. Using N3C data, challenge organizers will identify viable challenge questions focused on predicting long COVID and the associated risks. Participants in this challenge will build models on a training dataset established by the challenge organizers. Those trained models will then be tested on a holdout set to establish initial model accuracy. These trained models will be evaluated against a battery of accuracy and generalizability tests including longitudinal generalizability, cross-site generalizability, hold-out dataset accuracy, and prospective evaluations.", "accessing_institution": "University of Virginia" }, { "uid": "RP-5AA836", "title": "Covid exposures and outcomes in rare genetic neurodevelopmental disorders", "task_team": false, "dur_project_id": "DUR-E7BA187", "workspace_status": "CLOSED", "lead_investigator": "Timothy Benke", "research_statement": "This project aims to determine if there is worsening of outcomes as a result of SARS-CoV-2 infection amongst people affected by Neurodevelopmental disorders as compared to the general patient population using the N3C De-Identified Data Set. Neurodevelopmental disorders are often caused by rare genetic variation. These disorders often have systemic manifestations beyond the nervous system, including GI, immune and respiratory. Well-known examples are Rett Syndrome (MECP2) and Dravet syndrome (SCN1A). Some syndromes associated with severe epilepsy have multiple genetic causes, such as Lennox-Gastaut Syndrome. Each of these disorders are rare. The pathophysiology of each of these syndromes may or may not be uniquely influenced by SARS-CoV-2 infection to result in short or long-term outcomes that are different from the general population. Understanding the impact of SARS-CoV-2 infection is important to provide a deeper understanding of these rare diseases. Given the unique opportunity of the N3C database, we can now probe information related to SARS-CoV-2 that will also give us new insights into these disorders. This information will be critical to inform future clinical decision making regarding SARS-CoV-2 but also other aspects these disorders.\nIdentifying Neurodevelopmental disorders populations within the N3C enclave and analyzing their SARS-CoV-2 outcomes as compared to the general population would provide important insights into the presentation, effectiveness of treatments, and significant outcomes of SARS-CoV-2 in this group. Analyses may provide additional insights into the key features that could predict negative outcomes from COVID, from highly specific phenotypic features to general organ systems. Furthermore, determining the relative risk of long-COVID in Neurodevelopmental disorders compared to the general population could help elucidate underlying mechanisms of organ-specific long-COVID syndromes.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-807E96", "title": "Role of comorbidities, sex hormones, inflammatory biomarkers, and socio-demographic factors in sex differences of COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-E8A16D2", "workspace_status": "CLOSED", "lead_investigator": "Yilin Yoshida", "research_statement": "Coronavirus disease 2019 (COVID-19) is characterized by a male predominance in severity and mortality worldwide, but reasons underlying the sex-bias are unclear. Sex differences in comorbidities, sex hormones, and inflammatory biomarkers may all be factors involved in the sex differences of COVID-19 outcomes. However, few studies examined the role of these factors in COVID-19 outcomes stratified by sex. In our recent study in a COVID cohort of adults in Louisiana, we observed sex differences in comorbidities and biomarkers as predictors of COVID-19 severe outcomes. Our study underscores the need for a larger study with sex-stratification and robust analysis to determine if our findings are region-specific or hold generalizability to other populations. We propose to use the rich and population-based National COVID Cohort Collaborative (N3C) data to examine to what extent comorbidities, female sex hormones, and inflammatory biomarkers represent sex-specific determinants of COVID-19 outcomes.", "accessing_institution": "Tulane University" }, { "uid": "RP-A08E56", "title": "Does molnupiravir show a reduction of severe outcomes of COVID-19 in the N3C Data Enclave - Benjamin Sines", "task_team": false, "dur_project_id": "DUR-EA90222", "workspace_status": "ACTIVE", "lead_investigator": "Benjamin Sines", "research_statement": "Rationale: Conflicting outcomes from two randomized trials conducted at different epochs during the COVID-19 pandemic and in unvaccinated vs vaccinated populations have caste into question the real-world efficacy of molnupiravir at preventing hospitalization and mortality after COVID-19.\n\nObjective: To estimate the effective of molnupiravir for COVID-19 at preventing all-cause hospitalization and all-cause mortality by day 30 versus mirmatrelvir/ritonavir (Paxlovid) or supportive care.\nSecondary outcomes will include 6 month incidence of post-acute sequelae of COVID-19 (PASC), also referred to as long-COVID.\n\nMethods: Among 30,000 adults in a multi-institutional data enclave in the United States with COVID-19, we will emulate a target trial comparing treatment strategies initiating molnupiravir, mirmatrelvir/ritonavir, or supportive care alone within 5 days of SARS-CoV-2 test positivity. We will control for confounding using inverse probability of treatment weighting and stratification of analysis by vaccination status, viral epoch, and ?high? versus ?low? risk for severe outcomes as defined by concurrent comorbidities outlined by the CDC.\n\nThis project proposes and emulated target trial to assess the effectiveness of molnupiravir for prevention of hospitalization and mortality at 30 days when used for COVID-19 and initiated within 5 days of positive PCR as compared to paxlovid and supportive care.\n\nDesign:\nRetrospective cohort study approached through the emulated target trial framework.\n\nPrimary outcomes: \n1.\tAll-cause mortality at 30-days after SARS-CoV-2 positive test\n2.\tAll-cause hospitalization at 30-days after SARS-CoV-2 positive test\nSecondary outcomes:\n1.\tIncidence of post-acute sequelae of COVID-19 (PASC) as defined by ICD-10 code U09.9.\n\nEligibility\t\nTarget Trial: Adult (>=18) patients with relevant comorbidities or aged >=50 with SARS-CoV-2 test positivity within the last 5 days\nExclusion: in-patient status, pregnant or breastfeeding, allergic to molnupiravir or mirmatrelvir/ritonavir, eGFR<30 mL/minute, Child-Pugh class C liver impairment\t\nEmulated trial: Adult (>=18) patients with relevant comorbidities or aged >=50 with SARS-CoV-2 test positivity within the 5 days prior to initiation of molnupiravir or mirmatrelvir/ritonavir and IPTW matched individuals receiving supportive care alone\nExclusion: in-patient status at eligibility, pregnant or breastfeeding, allergic to molnupiravir or mirmatrelvir/ritonavir, eGFR<30 mL/minute, Child-Pugh class C liver impairment\n\nTreatment Strategies\nTarget Trial: Molnupiravir 800 mg twice daily for 5 days\nMirmatrelvir/ritonavir 300/100 mg twice daily for 5 days\nPlacebo twice daily for 5 days\nEmulated Trial: Molnupiravir 800 mg twice daily for 5 days\nMirmatrelvir/ritonavir 300/100 mg twice daily for 5 days\nSupportive Care as dictated by treating provider\n\nAssignment Procedures\nTarget Trial: Random 1:1:1 assignment\nEmulated Trial: IPTW contrast of molnupiravir, mirmatrelvir/ritonavir, or supportive care exposed individuals 1:1:1\n\nFollow-up Period\nTarget Trial: 30 days from randomization\nEmulated Trial: 30 days from SARS-CoV-2 test positivity\n\nCausal Contrasts:\nTarget Trial: Intentional to treat, per protocol effect\nEmulated Trial: Intention to treat (as indicated by EMR-based prescription for therapeutic)\n\nAnalysis Plan:\nTarget Trial: ITT estimate via comparison of 30-day non-elective hospitalization or mortality among individuals assigned to each treatment strategy\nEmulated Trial: Logistic regression for primary and secondary outcomes with IPTW ", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-3B98A5", "title": "Building computational models to predict outcomes of COVID-19 among children", "task_team": false, "dur_project_id": "DUR-EC4A549", "workspace_status": "CLOSED", "lead_investigator": "Zhe He", "research_statement": "Early covid research had shown that the pediatric population (0-18) had milder acute covid infection symptoms in comparison to other populations. However, emerging research has shown that pediatric outcomes may vary based on other demographics and treatment plans. Exploration of stratified pediatric age cohorts might provide a vital approach to the understanding of variation in covid infection symptoms and outcomes within different stages of development. Despite low risk in the short term, long-term Covid infection effects are more of a concern. Thus, conditions such as Long Covid and Multisystem Inflammatory Syndrome among other conditions may also be influenced by these factors. The goal of this research is to explore the relationship to predict severity and patient outcome based on vital signs and other factors. In addition, the development of deep learning models to predict the length of stay, ventilation requirements, and mortality among other factors using N3C datasets focusing on pediatric populations. ", "accessing_institution": "Florida State University" }, { "uid": "RP-22D00E", "title": "Accounting for between-population variation in COVID-19 risk prediction and surveillance", "task_team": false, "dur_project_id": "DUR-ECA904D", "workspace_status": "CLOSED", "lead_investigator": "Bryan Wilder", "research_statement": "A well-known challenge for machine learning in healthcare is that patients in different environments (e.g., living in different regions, or associated with different providers) induce different distributions over observable features and outcomes. We will study two questions related to this heterogeneity, with the goal of producing more effective characterizations of patients at risk of severe outcomes due to SARS-COV-2 infection as well as more effective population-level tracking of COVID-19 outbreaks and disease burden. First, we will study how between-facility and regional variation impact the performance of risk prediction algorithms and how models can be made more robust to these shifts in distribution. Second, we will study how multiple sites could pool surveillance-related data (e.g., counts of patients with particular diagnoses) together in a manner that preserves patient privacy and allows more rapid detection of new outbreaks or changes in the distribution of disease burden.", "accessing_institution": "Carnegie Mellon University" }, { "uid": "RP-D770D4", "title": "Stroke and COVID Population: An N3C and UVA OMOP analysis", "task_team": false, "dur_project_id": "DUR-8693674", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "This project will use the limited dataset N3C data to characterize COVID-19 patients who suffer stroke. The focus will be on ischemic stroke but it will also explore bleeding strokes (hemorrhagic stroke). We will explore if there are particular patterns that suggest that the COVID-19 stroke population is different from the general stroke population. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-A5CB78", "title": "[N3C Operational] Public Health", "task_team": false, "dur_project_id": "DUR-ECD17D9", "workspace_status": "ACTIVE", "lead_investigator": "Ken Gersing", "research_statement": "The [N3C Operational] Public Health data user request allows a small set of NCATS staff and community members, like analysts, common data model subject matter experts, medical specialist, informaticians, otologists, etc. access to the N3C enclave data for the purpose of preparing, cleaning, surfacing data , harmonization data for public health including the dashboard tools available for researchers, policy makers and community. To become a member of the [N3C Operational] public health individuals must apply using the DUR process where attestation to follow the data user agreement, code of conduct, security training and human subjects training are required.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-13E1A6", "title": "Long-term Neurologic Sequelae Following SARS-2 Infection In Patients With Neurodegenerative And Demyelinating Disorders", "task_team": false, "dur_project_id": "DUR-429B30B", "workspace_status": "CLOSED", "lead_investigator": "Khavir Sharieff, DO, MBA", "research_statement": "Project Summary:\nNeurocognitive impairment is one of the significant sequelae following severe acute respiratory syndrome 2 (SARS-2) infection. However, the longitudinal data on neurocognitive impairment following SARS-2 is sparse. Even though the pathophysiologic factors and mechanisms leading to neurocognitive impairments continue to be elucidated, much remains unknown. Neuroinflammation leading to brain damage through multilineage neural cell dysregulation has recently been proposed as a mechanism for neurocognitive impairment. It is hypothesized that activation of microglia into neurotoxic state likely results in loss of myelinated axons impairing the structure and function of neuronal networks. Given that myelin insulates axons and is critical to the speed of electrical conduction along neurons and to axonal metabolism, in effect, loss of myelination can lead to overall impairment in neurocognitive function. Interestingly, the neurocognitive impairment seen in patients post SARS-2 appears to resemble chemotherapy related cognitive impairment with microglial reactivity with neural dysregulation. Similar pathophysiology maybe at play in neurodegenerative (Parkinson's and Alzheimer's) and demyelinating (multiple sclerosis, amyotrophic lateral sclerosis) disorders and the neurocognitive sequelae following SARS-2 may impart significant morbidity in this vulnerable cohort leading to poor outcome. \n\nThe aim of this proposal is to evaluate the outcomes of SARS-2 infection in patients with neurodegenerative and demyelination disorders (NDDDs). Our aim is to study progression of neurologic symptoms in a cohort of patients with neurodegenerative and demyelination disorders compared to healthy controls following SARS-2 infection.\n\nThe proposed study is a retrospective case-controlled study evaluating the retrospective data reported to N3C enclave. The objective will be focused on identifying cohorts of patients with NDDDs and neurocognitive impairment post SARS-2 infection. Neurocognitive symptom severity (Class I: nearly asymptomatic/mild symptoms, II: moderate symptoms, III: severe symptoms) will be assessed following index infection with SARS. Data on subsequent progression at six, 12 and 24 months will be assessed. Collected data will be compared to age, sex, race matched healthy cohorts with SARS-2 during the same assessment. Descriptive statistics, relative risks, odds ratios will be analyzed. As appropriate, clinical and demographic variables will be compared between groups with an independent t-test, Mann-Whitney, or Chi-square tests. Linear regression model will be used to compare covariates between the two groups including age, sex, race and the degree and progression of neurocognitive impairment.\n", "accessing_institution": "Nova Southeastern University" }, { "uid": "RP-9DCAC8", "title": "Exploratory Data Analysis - Synthetic Data Set", "task_team": false, "dur_project_id": "DUR-4307269", "workspace_status": "CLOSED", "lead_investigator": "Angela Natcheva", "research_statement": "Exploratory data analysis of NC3 Synthetic Data Set to understand scope of NC3", "accessing_institution": "Citizen Scientist" }, { "uid": "RP-0B5DCB", "title": "Pulmonary Limitations from Covid-19; Effects on Robotic Prostatectomy", "task_team": false, "dur_project_id": "DUR-431DB26", "workspace_status": "CLOSED", "lead_investigator": "Cristian Sirbu", "research_statement": "Robotic-Assisted Laparoscopic Radical Prostatectomy (RALP) is the most common way prostate cancer is treated in men and has become the standard surgical option. However, the steep Trendelenburg position required for the surgery does limit the procedure in certain instances such as a morbidly obese patient or a patient with significant pulmonary compromise. A more recent source of pulmonary compromise is Covid-19 or coronavirus infection. Long-term effects of Covid-19 are still being determined, but Inflammation within and around the airways may induce concentric fibrosis around the bronchioles resulting in airway narrowing or obliteration. Development of constrictive bronchiolitis may result in persistent dyspnea after resolution of the acute infection with an associated obstructive defect on pulmonary function tests. This could lead to difficulty during RALP surgery and more pulmonary demise. The purpose of this study is to determine if Covid-19 infection, immunization, or antibody introduction prior to RALP leads to more pulmonary complications considering the need for steep Trendelenburg and the pulmonary risks from both issues separately. We hypothesize that the rate of RALP pulmonary complications will be higher in patients with a previous Covid-19 infection compared to patients without such infection. The role of immunization, antibody introduction, previous pulmonary complications post-Covid-19 and time since the Covid-19 infection in RALP patients with a previous Covid-19 infection will be evaluated. ", "accessing_institution": "Charleston Area Medical Center" }, { "uid": "RP-F2199E", "title": "Phenomenological Predictive Model to investigate COVID-19 incidence data for U.S.", "task_team": false, "dur_project_id": "DUR-43B1533", "workspace_status": "CLOSED", "lead_investigator": "Jiayue Liu", "research_statement": "In this project, we want to explore a predictive model of infection probability based on input location/age/gender\nFind relationships between COVID infection and patient phenotypes (obesity, asthma, tobacco, etc.)\n", "accessing_institution": "Duke University" }, { "uid": "RP-5C74AD", "title": "Moderation effects of smoking in the association of comorbidities and COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-43B8537", "workspace_status": "ACTIVE", "lead_investigator": "Dongmei Li", "research_statement": "Recent studies have shown COVID-19 patients with comorbidities have an increased risk for severe illness of COVID-19. Numerous studies have showed the link between smoking and comorbidities as smoking is a well-known risk factor for common comorbidities. However, the association of smoking and severity of COVID-19 is still unclear with inclusive results from recent studies. Given the association of comorbidities with both smoking and COVID-19, we propose to investigate the moderation effects of smoking in the association of comorbidities and COVID-19 using de-identified data (level 2) from the National COVID Cohort Collaborative (N3C). Our research question is whether smoking has moderation effects in the association of comorbidities with COVID-19 outcomes such as whether a patient is hospitalized, admitted to the ICU, and dead due to COVID-19. The proposed study will contribute to the literature on the direct and indirect association of both smoking and comorbidities with COVID-19 outcomes. Further we also plan to determine the health disparity relationship with smoking in COVID-19.", "accessing_institution": "University of Rochester" }, { "uid": "RP-8D86E8", "title": "Identifying factors leading to long-COVID using a novel interpretable machine learning framework", "task_team": false, "dur_project_id": "DUR-496AA88", "workspace_status": "CLOSED", "lead_investigator": "Matthew Baucum", "research_statement": "This study introduces a novel interpretable machine learning framework, RGID (Rapid Global Interpretability Dashboard), for quickly identifying important predictors and interactions in black-box machine learning models. To showcase the value of this tool, we gather data on COVID patients from N3C and train a machine learning model to distinguish patients with long COVID from those without, following Pfaff et al. 2022 (\"Identifying who has long COVID in the USA: a machine learning approach using N3C data\"). We then use RGID to identify complex interactions among patient characteristics predicting long COVID, and derive clinical insights from this analysis. ", "accessing_institution": "Florida State University" }, { "uid": "RP-CA4D64", "title": "Using Machine Learning to differentiate COVID-19 infection with seasonal flu ", "task_team": false, "dur_project_id": "DUR-499B729", "workspace_status": "CLOSED", "lead_investigator": "Ran Dai", "research_statement": "With the COVID-19 pandemic ongoing and the flu season approaching, there will be a challenge for people to differentiate the two diseases based on their symptoms. Therefore, the COVID-19 testing centers will potentially face challenges of reaching testing capacity and higher risks for infection due to increased number of people going on site tests. Our goal is to use machine learning method to build a classification model to establish a pre-test screening system to help people self-evaluate their risks with COVID-19 during the flu season to decrease their possibility with COVID-19 infection. We are requesting Level 2 de-identified data for this purpose. ", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-D0C3E4", "title": "The effect of non-invasive respiratory support on outcome and its risks in SARS-COV-2-related hypoxemic respiratory failure (NORMO2)", "task_team": false, "dur_project_id": "DUR-4598332", "workspace_status": "ACTIVE", "lead_investigator": "Lucas Fleuren", "research_statement": "In patients with COVID-19, the lungs are the main organ affected. Part of the patients with COVID-19 develop a severe lung infection with lung failure, requiring admission to the intensive care unit (ICU) and invasive mechanical ventilation. Invasive mechanical ventilation involves a tube that is passed through the mouth and is placed in the airway, which is invasive for patients and their loved ones. Patients who are mechanically ventilated with a tube have a high chance of dying in the ICU (30-40%). Patients that survive are often admitted to the ICU for weeks and have long-term complaints after the ICU admission (e.g. muscle weakness, confusion, tiredness). In addition to the consequences for the patient, we saw a shortage of ICU capacity during COVID-19, which led to the scaling down of regular care and also fears of a ?code black scenario? (a scenario in which patients would have to be denied care due to a lack of capacity). \n\nAn important question from a patient and societal perspective is whether invasive mechanical ventilation with a tube can be prevented by making use of less complex respiratory support. High Flow Nasal Oxygen (HFNO) and non-invasive ventilation (NIV) are used for various causes of lung failure to provide non-invasive mechanical ventilation and avoid a tube. With HFNO, humidified oxygen is delivered under flow through a cannula in the nose of the patient. Non-invasive ventilation involves a mask placed over the mouth and nose of the patient, through which pressure and flow of air can be provided. This can be done in the ICU as well as in the lung ward. However, it is still largely unknown whether HFNO and NIV are effective in preventing mechanical ventilation with a tube in COVID-19 patients. For other causes of lung failure, it is known that starting HFNO or NIV too late or continuing for too long can be harmful to the patient. It is therefore important to identify patients who may benefit from HFNO or NIV as early as possible. \n\nIn this proposal we aim to answer the following questions:\n? Is the use of HFNO or NIV associated with a reduced risk of tube ventilation, ICU admission, long stay ICU admission and death in COVID-19 patients with lung failure?\n? Can the doctor/nurse use a number of disease characteristics to predict which patients can be successfully treated with HFNO and NIV?\n? What is the relationship between the moment of invasive mechanical ventilation and patient outcomes?\n? Is the use of HFNO or NIV associated with higher chances of survival in patients who are no longer eligible for ICU admission or invasive mechanical ventilation?\n\nWe would make use of de-identified data from COVID-19 patients. These data include patient demographics, vital signs, ventilator data, laboratory measurements, medication and clinical observations. These answers fill current knowledge gaps, can be used to revise guidelines of non-invasive mechanical ventilation and better inform patients and their families.", "accessing_institution": "Erasmus MC" }, { "uid": "RP-B0AC40", "title": "[N3C Operational] Level 3 Logic Liaison Workspace", "task_team": false, "dur_project_id": "DUR-45EE4E4", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "NOTE: This DUR is a supplemental workspace for templates that require L3 data and does not replace the main L2 Logic Liaison DUR. The [N3C Operational] Logic Liaisons data use request pertains to a small set of N3C staff and community members who are in the Logic Liaison role (supporting creation of generic templates for use by Domain Teams). This DUR will provide this team shared access to a workspace where they can use IDENTIFIED data for the purpose of preparing, cleaning, and harmonizing broad generic templates. These templates will later be shared in the Knowledge Store so that they can quickly be customized for Domain Teams according to their specific needs. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-D0080F", "title": "Quantifying Uncertainty to Inform Time-Limited Trials of Invasive Mechanical Ventilation", "task_team": false, "dur_project_id": "DUR-470C680", "workspace_status": "CLOSED", "lead_investigator": "Anica Law", "research_statement": "About 800,000 patients are initiated on invasive mechanical ventilation in the United States annually; over 80,000 patients are subsequently unable to be weaned and remain on prolonged invasive ventilation, with even higher numbers during COVID pandemic surges. Prolonged invasive ventilation is a state with few days home alive that is discordant with the goals of many patients. Therefore, the decision to initiate invasive ventilation can be fraught for patients with acute respiratory failure who have an uncertain likelihood of recovery, and whose priorities are to attempt recovery but avoid prolonged invasive ventilation if recovery is not feasible. In such patients, invasive ventilation can be initiated as a time-limited trial (TLT). A TLT is an explicit agreement between clinicians and a patient/family to try life-sustaining therapy for a defined period; the patient?s response to therapy is used to improve prognostic certainty and guide subsequent decisions. By balancing patient priorities, TLTs are endorsed by professional societies to facilitate goal-concordant care.\n\nDespite the potential for TLTs to improve alignment of care with goals of patients with acute respiratory failure, there are many knowledge gaps that preclude the optimal use of TLTs. As a first step, we will investigate the feasibility of using N3C to develop and validate models that will provide an evidence base for patient selection and optimal duration of TLTs. We are requesting the synthetic dataset to understand the sample size. \n\n", "accessing_institution": "Boston University" }, { "uid": "RP-244DE1", "title": "Correlates and predictors of Long Covid subtypes", "task_team": false, "dur_project_id": "DUR-476BB90", "workspace_status": "CLOSED", "lead_investigator": "Jeremy Rossman", "research_statement": "It is estimated that 15% of acute SARS-CoV-2 infections go on to develop long-term persistent symptoms. There are several theories as to the mechanism of Long Covid, however, current diagnostic test yield inconsistent results. This study will look for correlates and predictors of each different sub-type of Long Covid in the hopes of finding a marker that can be used to reliably identify Long Covid patients. ", "accessing_institution": "Research Aid Networks" }, { "uid": "RP-27A072", "title": "Anticoagulation in Spine Surgery ", "task_team": false, "dur_project_id": "DUR-47C20CA", "workspace_status": "CLOSED", "lead_investigator": "Comron Saifi", "research_statement": "This initiative will evaluate the efficacy of perioperative anticoagulation with direct oral anticoagulants during spine surgery. ", "accessing_institution": "Houston Methodist Research Institute" }, { "uid": "RP-BD78ED", "title": "COVID-19 Synthetic Data Validation", "task_team": false, "dur_project_id": "DUR-480BC92", "workspace_status": "CLOSED", "lead_investigator": "Justin Starren", "research_statement": "There is uncertainty about which kinds of analyses can be supported by the synthetic derivative and which will require the LDS. The purpose of this research will be to conduct a number of analyses on both the Synthetic Derivative (SD) and the Limited Data Set (LDS) and compare the results. In particular, we will focus on applying a variety of deep-learning methods.", "accessing_institution": "Northwestern University" }, { "uid": "RP-7996BB", "title": "Severity of SARS-CoV-2 infection as a predictor for new onset of Diabetes Mellitus", "task_team": false, "dur_project_id": "DUR-4954EAD", "workspace_status": "ACTIVE", "lead_investigator": "Lorena Gonzalez-Sepulveda", "research_statement": "SARS-CoV-2 has been proposed as a potential inducer of diabetes mellitus (DM) due to its capacity to directly damage the pancreas, potentially leading to alterations in glucose metabolism. Damage to the pancreas can also be induced by multiple other viruses (e.g., enterovirus, rubella, cytomegalovirus, Epstein-Barr, and varicella-zoster virus), which have been strongly related to the onset of type 1 diabetes mellitus (T1DM). Even though no clear evidence has been shown on the association between SARS-CoV-2 and DM onset, the severity of SARS-CoV-2 infection could be a relevant factor determining damage to the pancreas and therefore inducing DM. In a previous study by Liu et al., approximately 1-2% and 17% of patients with non-severe and severe SARS-CoV-2 infection exhibited pancreatic injury, respectively. This study aims to assess SARS-CoV-2 infection severity as a predictor for DM onset using level 2 De-identified Electronic Health Record (EHR) data from the National COVID Cohort Collaborative (N3C) data enclave. The objective of this project is to ascertain the risk associated with developing DM among individuals who have contracted SARS-CoV-2, based on the severity of their infection. In addition, factors that may be potentially involved in the causal pathway will be evaluated. ", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-1D3121", "title": "N3C Diabetes and Obesity Domain Team - Level 3 PPRL", "task_team": false, "dur_project_id": "DUR-6A8B37C", "workspace_status": "ACTIVE", "lead_investigator": "Steve Johnson", "research_statement": "The purpose of the N3C Diabetes and Obesity Domain team is to do work in support for specific studies to examine the relationship of baseline factors (drugs, labs, other diagnoses) in diabetic or obese patients with positive and negative results for COVID-19 with their health outcomes. Studies will be undertaken examining the relationship between baseline A1c as an index of glycemic control as well as prescription of certain diabetes drugs on outcomes such as hospitalization, ICU stays, various levels of respiratory support, death, and length of stay. ", "accessing_institution": "University of Minnesota" }, { "uid": "RP-D12020", "title": "The Impact of COVID-19 on the ISC population outcomes and their readmission rate ", "task_team": false, "dur_project_id": "DUR-4A0E3B5", "workspace_status": "CLOSED", "lead_investigator": "Tamas Gal", "research_statement": "Individuals with compromised or suppressed immune systems (ISC) are considered high-risk for developing severe or life-threatening symptoms due to viral infections; however, little is known about the impact of COVID-19 on ISC populations. The ISC population is diverse and includes individuals with advanced chronic liver diseases (sometimes referred to as having immune paralysis state), active cancer and immune compromising medical conditions, such as solid organ transplant (SOT) patients that require therapies to prevent graft rejection. Also, very little is known about readmission rate in ISC patients, and general population for that matter, after their first hospitalization with COVID-19. Our team is requesting access to the Level 2 de-identified data to gain a better understanding of how COVID-19 affects the aforementioned populations. Our initial research will focus on a subset of target populations: advanced chronic liver diseases, SOT and patients with liver cancer (hepatocellular carcinoma and cholangiocarcinoma). For each of these, we will ask the following questions: \n1) What is the incidence of COVID-19 infection in the target population, and is it higher than in those without the ISC conditions? What is the readmission rate among these groups?\n2) What is the outcome of the target population after treatment for COVID-19 within those that are hospitalized (e.g. ventilator, LOS, death)? \n3) What are the risk factors (predictors) associated with the outcomes in the target population? \n4) Do the duration and modality of treatment affect outcomes in patients who are post liver transplant or have liver cancer? ", "accessing_institution": "Virginia Commonwealth University" }, { "uid": "RP-02631E", "title": "From Individual Experience to Collective Evidence: An Incident-Based Framework for Identifying Systemic Discrimination", "task_team": false, "dur_project_id": "DUR-4B1CB8E", "workspace_status": "ACTIVE", "lead_investigator": "Jessica Dai", "research_statement": "When an individual receives a decision that leads to a suboptimal outcome, what can they do about it? In many cases, \"bad outcomes\" may be not just individual incidents, but rather parts of systematic patterns of injustice. However, as an individual, there is no way to know whether their experience was an idiosyncratic, unlucky occurrence or whether it reflected a broader problem with the decisionmaking process, such as discrimination. This is further complicated by the fact that, in many cases, the question of whether a (normative or legally-prosecutable) harm has occurred depends explicitly on aggregate statistics, which would be impossible for an individual to know. In this work, we develop algorithms and theory for how to aggregate individual reports of potential harm in a sequential manner in order to identify true as soon as statistically possible. A key challenge involves doing so without predefining which demographic subgroups are considered, and instead analyzing all possible combinations of demographic features (which is, naively, exponential in the number of features). ", "accessing_institution": "University of California, Berkeley" }, { "uid": "RP-7B659A", "title": "Statistical and Machine learning method development for LONG COVID research", "task_team": false, "dur_project_id": "DUR-4C7170E", "workspace_status": "ACTIVE", "lead_investigator": "Ran Dai", "research_statement": "With our existing experience with the LONG COVID analyses. We found several issues from the N3C data. For example, the heterogeneity of LONG COVID reporting across different sites, the scarcity of the LONG COVID labeling and the complexity of the risk factors. In this project, we aim to develop novel data analysis and informatic tools to help overcome the deficiencies from the data, including missingness with complex mechanisms, high-dimensionality, and large scales. ", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-11698A", "title": "Towards the development of a learning health system in the COVID-19 pandemic: analysis of patient data to assist clinical decision making", "task_team": false, "dur_project_id": "DUR-4DDA9B0", "workspace_status": "CLOSED", "lead_investigator": "Paul Rathouz", "research_statement": "We intend to use the NCATS N3C Data Enclave to perform data-driven analysis of patients hospitalized with COVID-19 to improve prognosis and treatment decision-making. We plan to 1. Identify prognostic indicators of COVID-19 outcomes in hospitalized patients 2. Compare the clinical benefits of different treatments and interventions and 3. Identify subsets of patients benefiting from specific treatments using immunological biomarkers. To perform these analyses, we will use risk-stratification analysis, propensity scoring, prognostic modeling, and longitudinal data analysis, and machine learning predictive algorithms. We expect to mainly use R and its built in packages to do this work, although we may also use python. We are requesting de-identified data from Level 2 of the N3C Data Enclave to complete this work because we want to link patient demographics with treatments and outcomes.", "accessing_institution": "The University of Texas at Austin" }, { "uid": "RP-A54BE2", "title": "[N3C Operational] FHIR Repository Harmonization Project for N3C COVID Research", "task_team": false, "dur_project_id": "DUR-5086271", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "The FHIR Repository Harmonization Project for N3C COVID Research has been formed to develop a FHIR instantiation within the N3C Enclave that will supersede the existing OMOP analytics model. The FHIR instance is a part of a transition of many HHS agencies like NIH, CMC, ONC and others to standards based open-source technology like HL7 FHIR. HL7 is an ANSI/ISO standard used in clinical care for messaging technology of clinical, financial and billing information. NCATS has been working with FDA, ONC, and NCI on this architecture for over 4 years part of this work are already incorporated as the foundational mapping of multiple CDMs incorporated into N3C", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-3ABD59", "title": "Postoperative complications following shoulder arthroplasty in COVID positive patients", "task_team": false, "dur_project_id": "DUR-5090DC3", "workspace_status": "ACTIVE", "lead_investigator": "Deven Carroll", "research_statement": "This study provides insight into the impact of a recent COVID-19 diagnosis on postoperative outcomes after elective shoulder replacement surgery - an area where the outcomes are still unknown. The nuances of total shoulder arthroplasty should be explored separate from the context of hip and knee arthroplasty as there are differences in specific factors such as surgical anesthesia, duration of surgery, and postoperative care. This study aims to evaluate the effects from a recent COVID-19 diagnosis on outcomes after elective shoulder arthroplasty, providing insight into optimizing the timing for elective surgery and identifying which post-operative complication rates may be elevated due to a recent COVID diagnosis.", "accessing_institution": "Rosalind Franklin University of Medicine and Science" }, { "uid": "RP-4293B2", "title": "Examining anxiety, depression and trauma-related symptoms among children within the N3C Data Enclave. ", "task_team": false, "dur_project_id": "DUR-5172378", "workspace_status": "CLOSED", "lead_investigator": "Suzanne McCahan", "research_statement": "This project will examine anxiety, depression and trauma-related symptoms among children within the N3C Data Enclave. Children may be disproportionately impacted by SARS-CoV-2/COVID-19. We will describe the incidence, timing, and severity of sequelae of anxiety, depression and PTSD symptoms among children diagnosed with SARS-CoV-2 infection, including determining incidence rates across different racial/ethnic groups and urban and rural populations. This analysis will assist us in developing risk prediction models for identifying children who are at-risk for recurring and/or persistent mental health symptoms as a result of the pandemic. ", "accessing_institution": "Nemours" }, { "uid": "RP-AC7168", "title": "Leveraging the common risk factors of Cytokine Release Syndrome in COVID-19 to cancer immunotherapies with mechanistic modeling to support individualized dose prediction optimization", "task_team": false, "dur_project_id": "DUR-6AB10E7", "workspace_status": "CLOSED", "lead_investigator": "Philippe Robert", "research_statement": "Immune-activating agents used in immuno-oncology, like T-cell-based therapies (CAR-T-cells or bispecific antibodies), carry the risk for immune overactivation, potentially resulting in Cytokine Release Syndrome (CRS). CRS can be a life-threatening condition due to the vascular leak related to the effects of the cytokines on the endothelium, which in turn causes hypotension hypoxia and, ultimately, multi-organ failure. Cytokine blocking therapies or corticosteroids are commonly used to mitigate CRS. However, the risk for CRS is often dose-limiting with cancer immunotherapies.\n\nBeyond T cell activating therapies, CRS of different grades ?naturally? occurs in response to some infections; high-grade CRS is observed with infections such as severe SARS-CoV-2 infection, dengue, or sepsis, suggesting common immunological etiology and shared risk factors between these conditions.\n\nInterestingly, the cancer immunotherapy clinical databases show that it is challenging to correlate a certain level of cytokine (e.g., IL-6, INF-?, TNF) in the blood with a corresponding CRS severity (i.e., CRS grade). In individual patients, the same level of cytokines can cause either a low or a high-grade CRS, highlighting the need to consider the patients? predisposing factors such as immune-mediated and cardiovascular comorbidities, gender, age, or concomitant treatments. Similarly, COVID-19 patients may have aggravating factors predisposing them to cytokine storm (i.e., high-grade CRS), generally more severe disease, and sometimes death. This parallel between COVID-19- and cancer-immunotherapy-induced CRS is anticipated to enable leveraging COVID-19 clinical databases to identify CRS risk factors to be applied in immunotherapy clinical development.\n\nQuantitative integration of patient-specific factors into predicting the individualized risk of developing CRS would allow adapting the dosage of antitumor drugs while minimizing adverse effects and, therefore, can improve the individual risk-benefit ratio for patients and improve individual health outcomes.\n\nMining significant longitudinal health records of patients with CRS across diverse CRS-related pathological scenarios is a reliable approach to revealing correlations between patient-specific factors (e.g., demographic factors, comorbidities, co-treatments, etc.) to the onset and severity of CRS.\n\nUsing the N3C Data enclave, the project aims at finding multi-dimensional predictive markers for developing CRS that align with the risk factors of CRS during immunotherapies. The risk factors will be included in a mechanistic model of the immune response to SARS-CoV-2 infection and antitumor treatments, which will support the translation of these markers to risk stratification and prediction of safe dosing of T cell activating immunotherapies, and personalized dosing.\n", "accessing_institution": "University of Basel" }, { "uid": "RP-930D3D", "title": "Exploring Patterns of Stimulant and Opioid Use Among the N3C Cohort Through Application of the NCHS Stimulant Algorithm", "task_team": false, "dur_project_id": "DUR-51DC88A", "workspace_status": "ACTIVE", "lead_investigator": "Doreen Gidali", "research_statement": "The rise in stimulant use amidst the ongoing opioid epidemic warrants continued research on substance use. The National Center for Health Statistics (NCHS) has developed an algorithm to identify clinical cases involving stimulants and opioids using data from Electronic Health Records (EHR) and medical claims in the 2020 National Hospital Care Survey (NHCS). The algorithm identifies encounters that involve: 1) the use of illicit stimulants (e.g., cocaine, methamphetamines), 2) misuse of prescription stimulants (e.g., methylphenidate), and 3) the co-use of stimulants (as defined above) and opioids (illicit or prescribed but misused). The proposed project has two primary objectives. To test the generalizability of the NCHS Stimulant Algorithm by running the algorithm on N3C De-identified (Level 2) data; and to use the resulting N3C dataset with stimulant and opioid use flags, to answer the following research questions related to stimulant and or opioid use among patients diagnosed with COVID-19 in the N3C population. 1) What is the prevalence of stimulant use, opioid use, and the co-use of stimulants and opioids among patients with COVID-19 in the N3C data? 2)Are there any associations between gender, region, race/ethnicity on the use of stimulants/opioids among patients with COVID-19? ", "accessing_institution": "Centers for Disease Control Division of National Center for Health Statistics" }, { "uid": "RP-31717D", "title": "Tracking monoclonal antibody effectiveness over time against protection of severe disease as a consequence of COVID-19 infection", "task_team": false, "dur_project_id": "DUR-536611D", "workspace_status": "ACTIVE", "lead_investigator": "Istvan Bartha", "research_statement": "Monoclonal antibodies are used for the treatment of COVID-19 infections [1]. Effectiveness of any treatment against a continuously evolving virus depends on the genotype of the infectious agent and different COVID-19 strains have been shifted several times over the last 2.5 years. Therefore here we propose to estimate the effectiveness of the different types of monoclonal antibodies over time in monthly or bi-monthly time bins. To answer this question we request access to the Level-2 de-identified data. We will search the electronic medical records for evidence of monoclonal antibody treatment, along with indications of hospitalizations. Finally we will estimate the effectiveness monoclonal antibody treatments to prevent hospitalization. We aim to carry this out on a basis of shifting time windows so that we get sufficient insight into the changes inflicted on the treatment by various viral strains.\n\n[1] Corti, Davide, et al. \"Tackling COVID-19 with neutralizing monoclonal antibodies.\" Cell 184.12 (2021): 3086-3108.\n", "accessing_institution": "Vir Biotechnology Inc" }, { "uid": "RP-7767AC", "title": "Analytic methods for investigating effects of COVID-19 during pregnancy on birth outcomes", "task_team": false, "dur_project_id": "DUR-546551C", "workspace_status": "ACTIVE", "lead_investigator": "Louisa Smith", "research_statement": "Research on COVID-19 during pregnancy has suggested that the disease may increase the risk of adverse birth outcomes such as preterm birth. However, it is difficult to estimate the effects of infections and other exposures during pregnancy because the magnitude of those effects may differ depending on gestational age. In addition, people with shorter pregnancies are less likely to be infected while pregnant, making infection appear protective against time-dependent outcomes like preterm birth. Finally, severe COVID-19 in a pregnant person can be an indication for delivery even if the pregnancy is not yet at term. When an indicated preterm delivery occurs, such as by Caesarean section, we don?t know when labor would have begun spontaneously, making it difficult to tease apart physiologic effects on preterm birth vs. iatrogenic effects, and the magnitude of the latter may have changed throughout the pandemic as treatments were established. In this project, we aim to develop methods that allow researchers to ask and answer more specific research questions about effects of exposures during pregnancy and to use those methods to assess the impact of COVID-19 on birth outcomes. ", "accessing_institution": "Northeastern University" }, { "uid": "RP-F4DDC9", "title": "WiSER [Wildfire Smoke Exposure Response]", "task_team": false, "dur_project_id": "DUR-5477342", "workspace_status": "ACTIVE", "lead_investigator": "Fintan Hughes", "research_statement": "Wildfire Smoke Exposure-Response [WiSER]\nClimate change is threatening the stability of global ecosystems and in turn harming human health. Over the past 15 years, population?s exposure to wildfires has increased in 128 countries, yet the health impact of the smoke on exposed populations is not well described. The COVID pandemic coincided with recorded levels of wildfire smoke in the summers of 2020 and 2021 which led to a unique combination of wildfire smoke exposure and viral pandemic. COVID is, of course, a respiratory disease but its effects on coagulation, cardiac function and systemic inflammation have great overlap with the underlying mechanisms of wildfire smoke exposure.\n\nWe set out to investigate the baseline exposure response function linking wildfire smoke and human health, and to quantify how the interaction with environmental factors amplified the effects of COVID infection. We will link environmental data from the Copernicus Earth Observation Programme, and GEOS-Chem derived wildfire-specific particulate matter concentrations to the admission data demonstrating the incidence of cardiac, respiratory, obstetric and neonatal outcomes. Using COVID status as a modifier we will investigate how co-morbid COVID infection increases sensitivity to environmental stressors.", "accessing_institution": "Duke University" }, { "uid": "RP-52D9D5", "title": "The Impact of Regional Economic Conditions on COVID-19 Pandemic", "task_team": false, "dur_project_id": "DUR-5515467", "workspace_status": "CLOSED", "lead_investigator": "Brijesh Patel", "research_statement": "The COVID-19 pandemic has caused significant economic impact. However, regional, pre-COVID 19 pandemic economic conditions related outcomes are unknown. The purpose of is study is to look at how regional economic conditions played a role in COVID-19 related outcomes.", "accessing_institution": "West Virginia University" }, { "uid": "RP-E77B79", "title": "COVID-19 Outcomes in Vaccinated Patients with Liver Diseases", "task_team": false, "dur_project_id": "DUR-5598B4C", "workspace_status": "ACTIVE", "lead_investigator": "Jin Ge", "research_statement": "We have previously demonstrated using the National COVID Collaborative (N3C) database that patients with cirrhosis who tested positive for COVID-19 had 2.38x adjusted hazard of death at 30 days compared with patients with cirrhosis who tested negative. The advent of effective SARS-CoV-2 vaccinations at the end of 2020 has altered the trajectory of the COVID-19 pandemic. Despite the inclusion of nearly 100,000 participants in clinical trials for SARS-CoV-2 vaccinations, data for patients with liver diseases are extremely limited. Moreover, previous studies have demonstrated that patients with advanced liver diseases have well-recognized immune dysfunction resulting in attenuated responses to other vaccinations. In this study, we aim to leverage the N3C Database to determine the outcomes of SARS-CoV-2 infection in vaccinated patients with cirrhosis versus patients with chronic liver diseases (and no cirrhosis) and patients without liver disease.", "accessing_institution": "University of California, San Francisco" }, { "uid": "RP-E01C43", "title": "Malnutrition and COVID-19 Outcomes", "task_team": false, "dur_project_id": "DUR-8B67B2D", "workspace_status": "ACTIVE", "lead_investigator": "Alfred Anzalone", "research_statement": "Malnutrition is a global health crisis that effects up to one out of two older adults and results in an estimated annual cost of $51.3 billion per year in the United States. Malnutrition is linked to weaker immune systems, reduced cardiac output, poor diaphragmatic and respiratory muscle function and impaired gastrointestinal function. Early studies have explored the prevalence and severity of malnutrition in COVID-19 patients. However, small sample sizes and single-site studies limit the generalizability of results. Currently, a gap in knowledge exists regarding the relationship of malnutrition in hospital admissions in COVID-19 patients and the impact of malnutrition on clinical outcomes.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-CA0563", "title": "Inhibition of coronavirus assembly by thiopurines", "task_team": false, "dur_project_id": "DUR-5800290", "workspace_status": "CLOSED", "lead_investigator": "Eric Pringle", "research_statement": "There is an outstanding need for broadly acting antiviral drugs to combat emerging viruses. We showed previously that the clinically approved thiopurine 6-thioguanine (6-TG, aka Tioguanine) selectively inhibits influenza A virus (IAV) replication by interfering with the processing and accumulation of viral glycoproteins [Slaine et al., 2021. PMID: 33762409]. We have recently found that these thiopurines inhibit the replication of the betacoronaviruses HCoV-OC43 and SARS-CoV-2, and to a lesser extent, the alphacoronavirus HCoV-229E. This antiviral effect correlated with abnormal glycosylation and processing of the Spike protein. As Tioguanine is a clinically approved drug, we aim to use the synthetic data set to determine if patients who are currently taking thiopurines are a greater or lesser risk for experiencing more severe disease outcomes with SARS-CoV-2 infection. ", "accessing_institution": "Dalhousie University" }, { "uid": "RP-24D161", "title": "Sex hormone augmentation or antagonism as predictors of AKI in COVID-19 patients", "task_team": false, "dur_project_id": "DUR-580278A", "workspace_status": "CLOSED", "lead_investigator": "Olivia Fuson", "research_statement": "As an estimated 10% of hospitalized COVID-19 patients develop AKI as part of their disease course, this disease process places a significant burden on our healthcare system, and demands further study. Increased age and male sex have both been identified as potential predictors of AKI in COVID-19. Several mechanisms have been suggested to explain the increased risk of AKI in males, including males? lack of estrogen-receptor signaling (as estrogen-receptor signaling may convey a protective effect against AKI in the setting of COVID-19). It has been suggested that estrogen?s protective effects may stem from its ability to decrease the ratio of ACE to ACE2 expression, reducing inflammation and promoting tissue healing. In contrast to the protective effects of estrogen receptor signaling, increased expression of androgens may constitute a risk factor for development of AKI. This risk is attributed to these androgens? ability to stimulate TMPRSS2--a surface protein on endothelial cells which helps facilitate the viral entry of SARS-CoV-2--and the role of androgens in the release of pro-inflammatory cytokines. Men with prostate cancer receiving androgen depression therapy have a lower risk of developing SARS-CoV-2 and lower risk of developing severe SARS-CoV-2 infection than patients who did not receive ADT, indicating that androgen suppression therapy may have a protective role against SARS-CoV-2 infection. The exact role of androgen depression therapy and estrogen replacement therapy in the development of AKI in the setting of COVID-19 is still unclear, however. This retrospective cohort study aims to assess the protective effects of ADT against AKI in males with COVID-19, the potential protective effects of estrogen replacement therapy in post-menopausal females infected with COVID-19, and the impact of sex hormone augmentation or antagonism in trans individuals in an attempt to determine whether sex hormone augmentation or antagonism may serve as predictors of AKI in COVID-19.", "accessing_institution": "Oregon Health & Science University" }, { "uid": "RP-AE8A3D", "title": "Integrating N3C and molecular data to identify drug candidates in patients with COVID-19-associated AKI ", "task_team": false, "dur_project_id": "DUR-592E624", "workspace_status": "ACTIVE", "lead_investigator": "Fadhl Alakwaa", "research_statement": "COVID-19 arises from complications brought on by exposure to the coronavirus, SARS-CoV2. According to the American Diabetes Association (ADA), people with diabetes had much higher rates of serious complications and death than people without diabetes. Despite hundreds of ongoing clinical trials testing drug efficacy, there is no approved drug or vaccine for COVID-19. These have included drugs based on their promising effects against related coronaviruses, SARS and MERS, or on their ability to block host target proteins, such as angiotensin-converting enzyme 2 (ACE2), a SARS-CoV2 receptor (1). Acute kidney injury (AKI) occurs often in patients with COVID-19 (Hirsch et all, KI, 2020) and detection of SARS-CoV2 RNA in the kidney is associated with disease severity (2) implicating the virus directly in renal complications. SARS-CoV2 is presumed to infect kidney by targeting cells expressing ACE2 and other co-receptors (3-5) type specific receptors in kidney cells. Focusing efforts on identifying approved drugs as repurposing candidates can have an immediate impact on patients suffering with COVID-19 through off-label and compassionate use, rapidly accelerating bench-to-bedside, bringing aid to COVID-19 patients sooner. Integrating drugs exposure data from N3C and single cell RNA-seq data can help in this effort and we propose to leverage data from patients with COVID-19 to accelerate discovery.", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-6CFEFD", "title": "Acute Invasive Fungal Sinusitis in COVID-19 Patients", "task_team": false, "dur_project_id": "DUR-5962E34", "workspace_status": "ACTIVE", "lead_investigator": "Jenny Ji", "research_statement": "Acute invasive fungal sinusitis (AIFS) is an aggressive infection of the sinuses that progresses rapidly and is often fatal. While it is rare, there have been increased rates in COVID-19 patients, especially among diabetics. In this study, we want to see if there has been an increased rate of AIFS among patients with COVID-19 in the US, what fungi are causing the AIFS, what risk factors are associated with its development, and outcomes of the disease.", "accessing_institution": "Washington University in St. Louis" }, { "uid": "RP-2E3DDC", "title": "Learning Real-World Sex-Specific Clinical Factors Influencing the Susceptibility to Infection, Immune Response, Treatment Utilization and Outcomes Among Individuals Infected with SARS-CoV-2 Infection", "task_team": false, "dur_project_id": "DUR-5AA2809", "workspace_status": "ACTIVE", "lead_investigator": "Rohit Vashisht", "research_statement": "Characteristics of sex-specific clinical factors that may influence the susceptibility to SARS-CoV-2 infection, dictate an underlying immune response, and influence treatment choices during hospitalization of the patients with SARS-CoV-2 infection remain elusive. We aim to use real-world clinical data across the University of California Health System (UC Health) to identify potential clinical factors such as pre-existing conditions, prior medication use, medical procedures, and type of clinical visits that might influence the susceptibility to SARS-CoV-2 infection, immune response, treatment choices and outcomes among patients hospitalized with SARS-CoV-2 infection. First, we aim to identify potential clinical factors by training, testing, and validating interpretable machine learning models using electronic health records (EHRs) across UC Health. Next, we aim to characterize sex-specific clinical factors associated with the immune response to SARS-CoV-2 infection and vaccine-mediated immunity in terms of the sensitivity, specificity, positive and negative predictive values of an array of antibody tests used in a real-world setting as surrogate markers of the immune response. Further, we aim to construct and characterize real-world treatment utilization pathways of over 92 drugs that are under evaluation based on NIH treatment guidelines. Lastly, we aim to conduct real-world comparative effectiveness studies of various drugs used for clinical management of SARS-CoV-2 infection using a propensity score-matched population approach. We believe that a systematic analysis of real-world clinical data will help us to identify sex-specific clinical parameters associated with SARS-CoV-2 infection, immune response, and treatment outcomes to help shape medical and regulatory decision making. ", "accessing_institution": "University of California, San Francisco" }, { "uid": "RP-AA9BBC", "title": "Preservation of functionals of the data-generating distribution in synthetic data: A study of sub-group specific treatment effectiveness in hospitalized COVID-19 patients.", "task_team": false, "dur_project_id": "DUR-5B08C7A", "workspace_status": "ACTIVE", "lead_investigator": "Zachary Butzin-Dozier", "research_statement": "Our proposal is substantially motivated by statistical methodology related to sequential data analysis (pseudo real time analysis) and is part of a collaboration of the Center of Targeted Learning (CTL) at Berkeley and the Gates Foundation, particularly the knowledge integration (ki) program. First, we want to examine the date after the pandemic started when the information for certain treatment options based upon real world data (RWD) became sufficient to inform treatment decisions for hospitalized COVID-19 patients. For instance, we can order the data and act as if we have a series of new boluses of data released over time. Using Targeted Learning methodology developed at UC Berkeley, we can harness machine learning to create efficient estimators of treatment effects as more data (historically) were available. Given the rich data on both patient outcomes and baseline health characteristics, we believe the N3C data provide a rich opportunity to explore research methods that can maximize the information available in RWD, particularly in the context of evolving data during a future crisis. In addition to these analyses proposed on the de-identified (level 2) data, we also are working with Gates ki to explore the utility of synthetic data made available during a crisis like COVID-19. In the second part of our study, we will compare the results we obtained on the de-identified data and compare them to equivalent analyses on the synthetic (level 1) data. We will examine how statistical inferences for our parameters of interest (e.g., adjusted treatment impacts) change in the synthetic versus the actual (de-identified) data.", "accessing_institution": "University of California, Berkeley" }, { "uid": "RP-98F593", "title": "Contextual Risk Factors Associated with Invasive Fungal Infections", "task_team": false, "dur_project_id": "DUR-5C815B0", "workspace_status": "ACTIVE", "lead_investigator": "Lucy Li", "research_statement": "Invasive fungal infections (IFIs) are a significant and growing public health concern, affecting 13 million people worldwide and resulting in over 1.5 million deaths annually. Environmental factors are critical components of IFI acquisition, particularly for mold and endemic fungal infections, and marginalized communities are thought to be particularly vulnerable with greater exposure to high-risk environmental factors, leading to higher IFI rates. COVID-19 also substantially increases the risk for fungal infection with an associated increase in mortality rate even among patients without traditional host risk factors for IFIs. Prior assessments of IFI risk factors, irrespective of COVID-19 coinfection, have focused on host and fungal factors, limiting accurate assessment and quantification of the impact of local environment on disease acquisition and severity, especially among populations with a history of socioeconomic marginalization. We propose to examine the geographic distribution of IFI and outcomes among patients with vs. without COVID-19, controlling for both environmental (built and natural) and social determinants of health. ", "accessing_institution": "Johns Hopkins" }, { "uid": "RP-AEC732", "title": "Examining Associations between Vitamin D Status and COVID-19 Test Results", "task_team": false, "dur_project_id": "DUR-5D65683", "workspace_status": "ACTIVE", "lead_investigator": "Thomas Best", "research_statement": "There is strong evidence from pre-COVID-19 data that vitamin D treatment decreases the incidence of viral respiratory tract infection, especially in vitamin D deficiency. Initial analysis indicates that vitamin D might also protect against COVID-19, but additional studies are urgently needed, ideally using large multi-site datasets. Our objective is to examine whether vitamin D status, reflecting vitamin D levels and treatment, is associated with COVID-19 test results among data in the National COVID Cohort Collaborative (N3C) enclave, and to submit findings for peer review by October 31, 2020. We will examine whether a patient?s most recent vitamin D status before COVID-19 testing is associated with their first COVID-19 test result with the use of multi-variable statistical models that mitigate potential confounding. This work will attempt to replicate our initial smaller-sample, single-site findings of associations between vitamin D status and COVID-19 test results, published in JAMA Network Open on September 3, 2020 (Meltzer et al. 2020). If we do not find similar results to our earlier analysis, we will seek to understand the reasons for different findings. If we do find similar results to our earlier analysis, it would increase confidence in our earlier findings to inform current decision making and provide further support for robust prospective studies. ", "accessing_institution": "University of Chicago" }, { "uid": "RP-2B9622", "title": "Assessing and predicting the clinical outcomes of pregnant women with COVID-19 using machine learning approach", "task_team": false, "dur_project_id": "DUR-5E419EC", "workspace_status": "ACTIVE", "lead_investigator": "Tianchu Lyu", "research_statement": "It has been a global public health response to contain the negative impacts of COVID-19 pandemic after its first outbreak in Wuhan, China. Our knowledge about the impacts of COVID-19 on pregnancy health is limited despite of the fact that pregnant women are considered as an at-risk population for COVID-19.1 Some descriptive analyses and case reports found that pregnant women with COVID-19 infections were at higher risk of developing severe illness and worse clinical outcome. However, no study has examined and predicted the impact of COVID-19 on pregnant women utilizing big data and machine learning tools. The project aims to explore the association between COVID-19 infection and maternal health using both descriptive and inferential analyses. We will also develop a predictive model to forecast the clinical outcomes of pregnant women with COVID-19 using machine learning techniques. Anticipated outcomes of this study will help gain a better understanding of the impact of COVID-19 on pregnant women. The explored association and the predictive model will provide important evidence for implementing better clinical treatment for pregnant women with COVID-19. ", "accessing_institution": "University of South Carolina" }, { "uid": "RP-139B77", "title": "Evaluating the impact of oncologic therapy on COVID-19 vaccine efficacy", "task_team": false, "dur_project_id": "DUR-6082884", "workspace_status": "ACTIVE", "lead_investigator": "Sage Copling", "research_statement": "COVID-19 remains a major cause of death in the United States and impacts many cancer patients. The objective of this study is to determine how COVID-19 infections and vaccinations impact cancer outcomes and disease progression, and how oncologic therapy impacts the effectiveness of COVID-19 vaccination. The desired data points for this study include COVID-19 infection dates, vaccination dates, PDL1 status on tumors, treatment dates (immunotherapy, radiation therapy, surgery, and chemo), and oncologic outcomes including distant-metastasis free survival, progression-free survival, and overall survival in patients with non-small cell lung cancer, melanoma, and Hodgkin?s lymphoma. ", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-A0ED02", "title": "Exploring the relationship between COVID and Tuberculosis coinfection", "task_team": false, "dur_project_id": "DUR-60B34AC", "workspace_status": "CLOSED", "lead_investigator": "Jeremiah Hayanga", "research_statement": "Proposal:\n\nCo-infection rate of patients who died from COVID-19 with simultaneous diagnosis of TB, compared to patients only with COVID. \n\nCohorts: \n1.\tPatients with concomitant TB and COVID-19 infection\n2.\tPatients with solely COVID-19 infection\n\nCovariates:\n1.\tAge\n2.\tRace\n3.\tInsurance status\n4.\tRUCA code/some urban-rural distinctions\n5.\tSex\n6.\tTB treatments\n7.\tHTN\n8.\tCAD/MI\n9.\tPAD\n10.\tDM diagnosis\n11.\tCurrent smoking\n12.\tCOPD/Emphysema \n13.\tInterstitial lung disease\n14.\tSupplemental oxygen use\n15.\tDialysis\n16.\tHistory of malignancy\n17.\tDeath from respiratory cause (COVID/TB)\n\nOutcomes:\n1.\tThromboembolic events/limb/visceral ischemia\n2.\tRespiratory failure requiring intubation\n3.\tTracheostomy\n4.\tDuration from diagnosis to death/adverse event\n\nExclusion criteria:\n1.\tActive malignancy\n2.\tHospice status\n3. Other terminal diagnosis ? IPF, end-stage COPD\n4.\tHistory of transplant (immunosuppressed at baseline, unless there is a significant number of patients we are excluding, then we could perform subgroup analysis)\n\nAnalysis:\n1.\tUnweighted outcomes (parametric/nonparametric tests)\n2.\tPropensity-weighted outcomes\n3.\tIPTW\n4.\tCox hazard models\n5.\tKaplan-Meier analysis\n\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-D0D524", "title": "Cancer and COVID-19: Examining Long-Term Outcomes by Cancer Types and Demographic Factors", "task_team": false, "dur_project_id": "DUR-618DEE4", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "The COVID-19 pandemic has had a profound impact on individuals with cancer, a population already at heightened risk due to immunocompromised states, weakened physical health, and the need for ongoing medical treatment. While much has been learned about the short-term effects of COVID-19 on cancer patients, the long-term outcomes remain less understood, particularly as the pandemic continues to evolve and as cancer treatments are altered or delayed due to the pandemic?s strain on healthcare systems. This research project aims to examine the long-term outcomes of cancer patients infected with COVID-19, with a focus on variations across different cancer types and demographic factors such as age, sex, race, and socioeconomic status. \nUsing data from the N3C enclave, the study will assess key outcomes including cancer progression, survival rates, recurrence, treatment delays, and overall quality of life with special attention given to the impact of COVID-19 on patients with various types of cancer (e.g., breast, lung, colorectal, hematologic) and how different demographic factors may influence these outcomes. Demographic information, including race, ethnicity, age, and socioeconomic status.\nThe analysis will employ statistical techniques, including multivariable regression models, to identify independent associations between demographic factors, COVID-19 infection, and outcomes among Cancer patients. \nThe goal of this study is to provide critical insights into how COVID-19 influences the long-term health trajectories of cancer patients, inform clinical guidelines for managing cancer care during pandemics, and highlight the need for targeted interventions based on cancer type and demographic characteristics. This research is essential for improving the care and outcomes of cancer patients during and after the COVID-19 pandemic, ensuring that healthcare strategies are both effective and equitable for all patient populations. \n", "accessing_institution": "login.gov" }, { "uid": "RP-959A36", "title": "Using Machine Learning to Predict Clinical Outcomes of COVID-19 Patients with Diabetic Mellitus", "task_team": false, "dur_project_id": "DUR-62D60C2", "workspace_status": "ACTIVE", "lead_investigator": "Katherine Zhong", "research_statement": "Diabetes mellitus (DM) is one of the major comorbidities associated with worsened outcomes in patients with coronavirus disease (COVID-19). However, the outcomes of COVID-19 in populations with certain diabetic complications such as diabetic retinopathy (DR) have not been fully studied. The purpose of this project is to develop a machine learning model to predict the severity of COVID-19 in COVID-positive patients with pre-existing DM or certain diabetic complications such as DR. Level 2 De-identified Electronic Health Record (EHR) data will be drawn from the National COVID Cohort Collaborative (N3C) data enclave and be used to train the machine learning model. The goal of this project is to build a prognostic model that predicts the clinical outcomes in individuals with COVID-19 infection and DM or complications of DM. The model can help health care providers identify high-risk patients who are likely to develop severe complications of COVID-19 and make clinical decisions accordingly. Additionally, a machine learning model can analyze a wide range of variables and identify the risk factors associated with severe COVID complications in patients with DM.", "accessing_institution": "Brown University" }, { "uid": "RP-FDE6A5", "title": "Comparison of outcomes among persons with Influenza and SARS-CoV-2", "task_team": false, "dur_project_id": "DUR-649EDA5", "workspace_status": "ACTIVE", "lead_investigator": "Sally Hodder", "research_statement": "Annual influenza related mortality in the US is estimated at 12,000-52,000, despite the availability of effective antiviral agents.1 Though lower mortality was described among rural compared with urban populations during the 1918 pandemic, more recent analyses have found higher rural mortality.2 However, adjustment for potentially confounding factors was variable in these studies. The clinical impact of adding corticosteroids to the influenza treatment regimen is unclear. Though some studies in animals have shown decreased lung injury and mortality with corticosteroids, clinical influenza guidelines recommend against use of corticosteroids due to potential prolongation of viral shedding and worse clinical outcomes reported in some observational studies.3,4 \nPrevious work by the IDeA state consortium using the N3C database demonstrated that Sars-Cov-2 infected persons in rural areas of the US were 36% more likely to die compared with their urban counterparts even after adjustment for demographics and comorbidities.5 Comparison of outcomes for influenza and COVID-19 will indicate whether or not worsened outcomes are unique to a new virus (SARS-CoV-2) or extend to another RNA virus which has been in the human population for a number of years.\nThe IDeA state consortium also used machine learning approaches to assess the impact of treatment on COVID-19 outcomes, finding that likelihood of hospital discharge was increased when corticosteroids alone or in combination with anticoagulants or antiviral agents were given.6,7 Evaluation of impact on outcomes of corticosteroids among hospitalized COVID-19 patients compared with influenza patients will provide further insights into treatment of viral diseases.\n\nCDC. https://www.cdc.gov/flu/about/burden/index.html. \n2.\tSingh GK, et al. Widening Rural?Urban Disparities in All-Cause Mortality and Mortality from Major Causes of Death in the USA, 1969?2009. Journal of Urban Health: Bulletin of the NY Academy of Medicine, 2013. Vol. 91, No. 2. \n3.\tUyeki TM, et al. Clinical Practice Guidelines by IDSA: 2018 Update. Clin Infect Dis. 2019 Mar 15; 68(6): 895?902.\n4.\tNi Y-N, et al. Effect of corticosteroids on mortality of patients with influenza pneumonia: systematic review and meta-analysis. Critical Care Med. (2019) 23:99 \n5.\tAnzalone AJ, et al. Higher Hospitalization and Mortality Rates among SARS-CoV-2 Infected persons in Rural America. Jour of Rural Health 2022. 10:1-16.\n6.\tMoradi H, et al. Assessing Effects of Therapeutic Combinations on SARS-CoV-2 Infected Patient Outcomes: A Big Data Approach. [Currently under peer review]\n7.\tPrice B, et al. The Impact of COVID-19 Treatments on Patient Outcomes: A Probabilistic View. [Currently under peer review]", "accessing_institution": "West Virginia University" }, { "uid": "RP-27CEA5", "title": "Identifying predictors for ECMO need in COVID 19 patients", "task_team": false, "dur_project_id": "DUR-6538D2D", "workspace_status": "CLOSED", "lead_investigator": "Ahmed Said", "research_statement": "The novel SARS-CoV2 virus and resulting COVID-19 global pandemic have put unforeseen strain on healthcare systems globally. It is incredibly challenging to know when and where to deploy resources, this is especially true for utilizing extracorporeal membrane oxygenation (ECMO). This technology functions as life sustaining therapy for the most severely affected patients, but only exists in centers with expertise. Utilizing ECMO puts a strain on local resources, and strain on healthcare systems as a whole when deciding when patients need to be transported to experienced regional centers.\n\nWe plan to analyze our the N3C data to identify factors that are associated with the need for ECMO support in COVID-19 patients. We will analyze the demographics, therapeutics, laboratory values, vital signs, as well as change in these values associated with the binary outcome of necessitating ECMO support in addition to outcomes for patients supported by ECMO.", "accessing_institution": "Washington University in St. Louis" }, { "uid": "RP-988C63", "title": "DPI Privacy ", "task_team": false, "dur_project_id": "DUR-66A7BCC", "workspace_status": "CLOSED", "lead_investigator": "Lenore Zuck", "research_statement": "Computing efficient distributed classifiers for COVID19 related decisions ", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-2A9223", "title": "Spatio-temporal Analysis with Tensor Factorization and Visualization for Mobile COVID19 Vaccination", "task_team": false, "dur_project_id": "DUR-6743805", "workspace_status": "CLOSED", "lead_investigator": "JOERG HEINTZ", "research_statement": "Clinical Motivations: \nCurrently, 12% of the U.S. population has received at least one COVID-19 vaccine. This is significantly below the projected 70-90% required to achieve herd immunity to the virus. While vaccine hesitancy exists among multiple demographics, it is a significant issue among rural populations. This proposal aims to develop a model that predicts the vaccine hesitancy related COVID-19 morbidity and mortality each county in the United States is likely to experience, along with the most likely causes of vaccine hesitancy in each county. Also included is a toolkit of up-to-date, engaging and highly visual materials that will enable rural community members to make informed decisions about receiving the vaccine.\n\nTechnical Challenges: \nThe data utilized in the predictive model is highly complex and breaks standard assumptions used in machine learning and data mining algorithms. These include spatial and temporal correlation of observations, dynamic and sudden changes in data, and missing values in observations.\n\nProposed Work: \nThis project will develop a hybrid predictive construct of neural networks and epidemiological (SIR) models to predict vaccine-preventable deaths in each county in the U.S. and the most likely reasons for vaccine hesitancy among populations. A toolkit will help guide rural populations, who are frequently vulnerable and under served, in their decision-making about accepting the COVID-19 vaccine. \n\nExpected Deliverables: \nThis proposal will deliver a predictive model of vaccine-preventable deaths related to COVID-19 for each county in the U.S., an integrated model of the most likely reasons for vaccine hesitancy among populations of interest and a multi-modal toolkit designed to counter vaccine hesitancy. Results from the pilot project will be used to inform iterative development of the model and the toolkit. \n\nBroad Impact: \nLeveraging predictive models and toolkits developed using HCD, this work will assist rural communities in moving beyond vaccines to vaccinated populations, thus contributing to herd immunity and national and the global security. \n\n", "accessing_institution": "University of Illinois at Urbana Champaign" }, { "uid": "RP-EC975D", "title": "Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) in patients with cancer", "task_team": false, "dur_project_id": "DUR-67B7A21", "workspace_status": "CLOSED", "lead_investigator": "Meera Mohan", "research_statement": "Cancer patients with coronavirus disease of 2019 (COVID-19) infections often suffer from a chronic form of COVID-19 commonly referred to ?long COVID? or, more recently, as ?Post-Acute Sequelae of SARS-CoV-2 infection (PASC)?. Currently, there is very limited information on clinical outcomes of cancer patients suffering from PASC. We plan to utilize the data and resources of N3C database to study the prevalence and clinical outcomes of PASC in cancer patients. We also plan to identify certain risk factors, such as patient demographics, including race, ethnicity, medical comorbidities, smoking, cancer type and treatments, and severity of COVID-19 that may predispose cancer patients to PASC. Some clinicians also believe that PASC may have led to unnecessary delays and discontinuation of cancer treatment, which may have adversely impacted clinical outcomes in cancer patients. Hence, we also wish to study the impact of PASC on cancer care delivery. This information will help us improve clinical outcomes in this vulnerable group of patients and may assist in formulating future guidelines to inform clinical practice in the post-pandemic setting.", "accessing_institution": "Medical College of Wisconsin" }, { "uid": "RP-7C5E62", "title": "COVID-19 Outcomes in Patients with Cirrhosis", "task_team": false, "dur_project_id": "DUR-693A7BD", "workspace_status": "ACTIVE", "lead_investigator": "Jin Ge", "research_statement": "We have previously demonstrated using the National COVID Collaborative (N3C) database that patients with cirrhosis who tested positive for COVID-19 had 2.38x adjusted hazard of death at 30 days compared with patients with cirrhosis who tested negative. The advent of effective SARS-CoV-2 vaccinations at the end of 2020 has altered the trajectory of the COVID-19 pandemic. Despite the inclusion of nearly 100,000 participants in clinical trials for SARS-CoV-2 vaccinations, data for patients with liver diseases are extremely limited. Moreover, previous studies have demonstrated that patients with advanced liver diseases have well-recognized immune dysfunction resulting in attenuated responses to other vaccinations. In this new/extended study, we aim to leverage the N3C Database to determine the outcomes of SARS-CoV-2 infection in vaccinated patients with cirrhosis versus patients with chronic liver diseases (and no cirrhosis) and patients without liver disease.", "accessing_institution": "University of California, San Francisco" }, { "uid": "RP-DEFAA4", "title": "The Impact of Acute COVID-19 Pharmaceutical Treatments on the Conversion to Long COVID-19 (PASC)", "task_team": false, "dur_project_id": "DUR-6A457BD", "workspace_status": "CLOSED", "lead_investigator": "Amanda Brooks", "research_statement": "As we struggle to understand what constitutes long COVID, it is equally important to understand how our treatments and interventions during an acute COVID infection may influence the risk that a patient's disease will persist and progress to long COVID or PASC. In our previous study we hypothesized that treatment with either famotidine or celexoib would alter this progression. Working with the N3C data, we were able to develop an analysis method and found that these drugs do seems to change a patient's risk of developing long COVID. Thus, during the course of this study, we will use the same strategy to identify other medications and treatments that have either a positive or negative effect on an individual's risk of developing long COVID.", "accessing_institution": "Rocky Vista University" }, { "uid": "RP-985DA6", "title": "Effects of IL-4/ IL-13 inhibitor Dupilumab on COVID-19 outcome. ", "task_team": false, "dur_project_id": "DUR-6BBC45A", "workspace_status": "CLOSED", "lead_investigator": "Jenna Donaldson", "research_statement": "Throughout the SARS-CoV-2 (COVID-19) pandemic, certain medical comorbidities have put patients at risk for hospitalization and worse outcomes from infection. . However, although asthma was previously identified to be a risk factor, in actuality patients with asthma, specifically allergic asthma from Th2 inflammation, do not appear to be at risk. Previous studies have examined the effect of certain cytokines- IL-4, IL-5, and IL-13- associated with allergic asthma, and has established that IL-13 is the cytokine of interest for providing a measure of protection against COVID-19 due to its ability to downregulate ACE2 expression as well as attenuate viral and cell shedding in human airway epithelial (HAE) cells, leading to possible protective mechanisms regarding viral entry, replication, and spread. \nRecent drug developments for Th2 inflammation have introduced biologics that can target either IL-13 itself or the receptor to which it bonds. This blocks the pathway for the cytokine, inhibiting its effect. Dupilumab, approved in 2019 for treating asthma, is an IL-4/IL-13 receptor antagonist. This study would investigate whether this biologic, compared to others, increases the risk of a severe COVID-19 outcome in a patient. \nN3C, through the Limited Dataset (level three) would provide the information necessary to assign patients to cohorts based on demographic information as well as determine the risk for of a severe COVID-19 outcome for allergic asthmatics on Dupilumab compared to other biologics. \n\n", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-B6B91B", "title": "Studying COVID-19 Remission, Recrudescence, Recurrence, and Reinfection", "task_team": false, "dur_project_id": "DUR-6BCA669", "workspace_status": "ACTIVE", "lead_investigator": "James Cimino", "research_statement": "The purpose of the study is study patients who have had one or more positive SARS-CoV-2 tests (tests for the virus that causes COVID-19), followed by one or more negative tests, followed by one or more positive tests in order to examine the frequency of reinfection versus recurrence of infection.", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-0784A4", "title": "Risk of Elective Surgery during the COVID-19 Pandemic", "task_team": false, "dur_project_id": "DUR-6C4B7C4", "workspace_status": "CLOSED", "lead_investigator": "Julia Carinci", "research_statement": "The COVID-19 pandemic has presented unprecedented challenges. One of which was the decision to postpone and cancel elective surgeries around the world in order to preserve hospital capacity and direct scarce resources to quelling the battle against COVID-19. As a consequence, many patients are missing their window of opportunity for optimal treatment. Although the initial strategy to prioritize urgent surgeries was beneficial during the peak of the pandemic, such protocols need to be adapted to deal with the growing issue of surgical backlog. A few studies have shown that surgery conducted in COVID positive patients resulted in surgical complications, but little research has been done to assess specifically what clinical and surgical factors explain these adverse outcomes. Thus the aim of this study is to investigate to what extent patient and disease induced risk factors explain surgical complications in patients undergoing surgery during the COVID-19 pandemic.", "accessing_institution": "Erasmus University" }, { "uid": "RP-AB483A", "title": "The Impact of Famotidine and Celecoxib on the Course of Long COVID-19 (PASC)", "task_team": false, "dur_project_id": "DUR-6E3DDDC", "workspace_status": "CLOSED", "lead_investigator": "Amanda Brooks", "research_statement": "During the most acute phase of the COVID-19 pandemic, reports emerged on the benefit of taking both famotidine (Peptide AC) as well as, to a more limited extent, celecoxib on the clinical outcomes of both hospitalized and non-hospitalized patients. However, as with many of the acute treatments, it is not clear what impact such regimens may have on the development and course of long-COVID (PASC). This project aims to identify patients both on a pre-acute COVID treatment regimen of famotidine and/or celecoxib as well as those treated for acute COVID-19 illness with either of these drugs. Additionally, we will examine what, if any, demographic or environmental factors many contribute to the development of long COVID. The study will then consider the prevalence of long-COVID (PASC) in these patients vs the general population. Finally, we will do a cluster analysis to identify any other promising pharmaceutical interventions that may alter the rate of conversion from acute COVID-19 to long COVID.", "accessing_institution": "Rocky Vista University" }, { "uid": "RP-895617", "title": "Ethics of scarce resource allocation: applying analytical models to inform decision making", "task_team": false, "dur_project_id": "DUR-7046330", "workspace_status": "CLOSED", "lead_investigator": "William Parker", "research_statement": "Crisis standards of care are procedures for administering care in a disaster that acknowledge not all patients will get the treatment they need. These policies contain objective scarce resource allocation protocols to decide which patients get critical care in the setting of a shortage. The COVID-19 pandemic pushed many US health systems to the brink of performing such triage. While there is a vigorous theoretical debate over these protocols, there has been no empirical assessment of their performance. Moreover, Sequential Organ Failure (SOFA), upon which many protocols are based, was developed prior to COVID-19 and was not developed for the purpose of resource allocation. Furthermore, the SOFA score would disadvantage Black Americans by inappropriately penalizing chronic lab abnormalities such as elevated creatinine values. There is a vital need to determine if these scarce resource allocation protocols fairly and effectively triage scarce critical care resources to critically ill patients before crisis standards of care are required. \n\nThis project will evaluate the ethical performance of prominent scarce resource allocation protocols using data from the National COVID Cohort Collaborative (N3C) Limited Data Set.", "accessing_institution": "University of Chicago" }, { "uid": "RP-D54257", "title": "Effects of ACEi, ARB and SGLT2i on COVID19-patients with a history of nephrectomy", "task_team": false, "dur_project_id": "DUR-7339C32", "workspace_status": "ACTIVE", "lead_investigator": "Meng-Hao Li", "research_statement": "This study will investigate the impacts of selected medications on recurrence or the development of the end stage kidney disease (ESKD) identified as the need of dialysis), chronic kidney disease (CKD) Stages IV and V, Focal Segmental Glomerular Sclerosis (FSGS) and patient mortality in COVID-19 patients who have one kidney attributable to nephrectomy as a result of live donation of kidney, or for cancer. Medications to be investigated will include ACE inhibitors (lisinopril, enalapril, etc.), ARB (irbesartan, losartan, etc.) and SGLT2i (empagliflozin, dapagliflozin etc.).", "accessing_institution": "George Mason University" }, { "uid": "RP-5EBB85", "title": "The Impact of Policy on COVID Trends in North Dakota ", "task_team": false, "dur_project_id": "DUR-7407684", "workspace_status": "CLOSED", "lead_investigator": "Olivia Persinger", "research_statement": "This project will look at the COVID incidence and mortality trends in North Dakota throughout the first few years of the pandemic. These trends will then be mapped against the COVID policy that was in place nationally and in North Dakota. These trends will then be used to determine which were the most effective policies to better advise policy makers in the case of future infectious disease outbreaks. This research is done to evaluate how policy impacted the COVID trends of North Dakota with its unique geographic and population makeup. I will aim to compare the North Dakota trends with those on the national scale to better get an understanding of the effectiveness of state policy in guiding infectious disease outbreaks. ", "accessing_institution": "North Dakota State University" }, { "uid": "RP-7FEF96", "title": "Analyzing Test-negative Design Study Data with Time-to-event Methods", "task_team": false, "dur_project_id": "DUR-742B7B7", "workspace_status": "ACTIVE", "lead_investigator": "Shangchen Song", "research_statement": "The widespread availability of COVID-19 testing has enabled the use of test-negative designs (TNDs) for evaluating vaccine efficacy in relation to the disease. These designs involve enlisting individuals who display COVID-19 symptoms and seek medical attention, subsequently testing them for the virus to distinguish cases from controls. TNDs help reduce selection bias stemming from healthcare-seeking behaviors. Nonetheless, the prevalent method for analyzing TND data is conditional logistic regression, which is essentially a special case of Cox regression and necessitates additional matching for potential confounders. In this study, we broaden the analysis method by utilizing the recurrent-event Cox regression model with time-dependent covariates to assess vaccine effectiveness. Furthermore, we present simulation studies for covariates of full and booster doses of COVID-19 vaccines, along with up to two infections for each individual. We also planned to conduct real data studies to support our proposal.", "accessing_institution": "University of Florida" }, { "uid": "RP-30E327", "title": "Neurological Manifestation of SARS-CoV-2 infection in African Americans: AI-based Novel Approach of Prognostic and Risk Stratification Models", "task_team": false, "dur_project_id": "DUR-753DBED", "workspace_status": "CLOSED", "lead_investigator": "Nizar Souayah", "research_statement": "An increasing body of literature suggests that COVID-19 patients (C-Ps) often have neurological symptoms and signs, either revealing or SARS-CoV-2 Infection, associated with poor prognosis. In parallel, mortality and prevalence estimates show a consistent pattern of racial/ethnic differences. Specifically, members of the African American (A-A) community. \nMain hypothesis: we hypothesize that: 1) neurological manifestations of SARS-CoV-2 Infection in A-A correlate with demographics, comorbid conditions including functional and biological markers\nAim1: i) Characterize, survival, demographic profile, comorbid conditions, disease progression, and outcomes of A-A C-Ps with/without neurological complications compared to Caucasians \nAim 2: Predict C-Ps' outcome and occurrence of neurological complications with and without specific nervous system involvement in A-A compared to Caucasian patients. \nAim 3: Prospectively investigate the correlation between, on the one hand, demographic, clinical, radiological, functional, and biological markers, and on the other hand, A-A COVID mortality outcomes and occurrence of neurological complications. \n", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-5AF910", "title": "COVID-19 associated Facial Palsy", "task_team": false, "dur_project_id": "DUR-7559814", "workspace_status": "ACTIVE", "lead_investigator": "Kyle Ishikawa", "research_statement": "Is there a higher incidence of facial palsy following COVID-19 infection compared to the non-COVID-19 annual incidence of 12-50 / 100,000 people? Level-2 data will be used to assess demographics, comorbidities, and clinical features associated with facial palsy. The history of those with facial palsy will be reviewed for condition recurrence, incomplete recovery, complication, treatment, and efficacy of said treatment.", "accessing_institution": "University of Hawaii System" }, { "uid": "RP-6CB002", "title": "Mental Health Disparities in COVID-19: the Effect of Demographic, and Insurance Status on Developing Psychosis Signs During Hospitalization for COVID-19", "task_team": false, "dur_project_id": "DUR-756A730", "workspace_status": "ACTIVE", "lead_investigator": "Hadis Hashemi", "research_statement": "The COVID-19 pandemic has significantly impacted mental health worldwide, with emerging evidence suggesting varying effects across different demographic groups. Recent studies have highlighted profound increases in anxiety, depression, and other psychiatric conditions such as psychosis among different populations. This project seeks to explore disparities in the development of psychosis symptoms among hospitalized COVID-19 patients.\n By examining factors such as age, sex, race, ethnicity, and insurance status, we aim to identify how these variables influence mental health outcomes during hospitalization. Utilizing data from the National COVID Cohort Collaborative (N3C), this study will conduct a detailed analysis to uncover patterns and potential risk factors associated with the onset of psychosis. \nThe goal is to generate actionable insights that can inform targeted interventions and policies, ultimately contributing to a more equitable healthcare system in future public health crises. This research will help understand the broader impact of COVID-19 on mental health and guide healthcare strategies to mitigate these effects.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-1144A9", "title": "Is Long COVID associated with an increased risk of mortality? - Johanna Loomba", "task_team": false, "dur_project_id": "DUR-7937888", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "TEAM QUALIFICATIONS: \n\nDon Brown: UVA iTHRIV CTSA Co-PI, Senior Associate Dean of Research UVA School of Data Science, UVA PI N3C and PASC RECOVER, N3C Neuro Domain Team Co-Lead. Data scientist and co-author or senior author on multiple N3C manuscripts and other translational science research (https://scholar.google.com/citations?user=SSPvo1IAAAAJ&hl=en&oi=ao?).\nKaren Johnston: UVA iTHRIV CTSA Co-PI, Harrison Distinguished Professor of Neurology, Department of Neurology Associate Vice President for Clinical & Translational Research Office of the Vice President for Research University of Virginia (https://www.researchgate.net/profile/Karen-Johnston-10).\nJohanna Loomba: UVA iTHRIV CTSA Director of Informatics, Lead of N3C Logic Liaison Team, N3C Neuro Domain Team Co-Lead. Co-author on a large number of N3C manuscripts (?https://scholar.google.com/citations?user=GCTZm7kAAAAJ&hl=en). \nAndrea Zhou: UVA iTHRIV CTSA Data Scientist, Lead developer, maintainer and trainer for Logic Liaison templates. Creator and maintainer of N3C Phenotype explorer (https://unite.nih.gov/workspace/slate/documents/phenotype-dashboard). \nSuchetha Sharma: UVA Data Scientist, second-author on the N3C PASC RECOVER risk manuscript (https://www.researchgate.net/scientific-contributions/Suchetha-Sharma-2216516264). \nSaurav Sengupta: UVA School of Data Science PhD candidate, lead author on PASC RECOVER risk identification methods manuscript (https://scholar.google.com/citations?user=vSW9jPMAAAAJ&hl=en).\nSihang Jiang: UVA System Engineering PhD candidate, lead author on PASC RECOVER inpatient risk identification manuscript (https://scholar.google.com/citations?user=UqKNRPsAAAAJ&hl=en).\n\nNOTE: All references cited can be found at the end of the Justification section of our submission form.\n\nSince the pandemic began in 2020, numerous researches have focused on the acute symptoms of a SARS-CoV-2 infection as well as the longer lasting symptoms that characterize post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. The longer lasting PASC symptoms include things such as fatigue, cognitive dysfunction, post-exertional malaise, shortness of breath, and depression [1][2]. Little work has been done to investigate the relationship between PASC and mortality.\n\nWith the introduction of the ICD-10 Long COVID diagnosis code (U09.9) we can label patients who clinicians have identified as suffering from PASC. Some N3C sites are also sending data on Long COVID clinic visits, so we will use this as a second potential flag for PASC. Identifying controls is more difficult as many patients in the N3C dataset who have not been labeled with U09.9 may still have PASC, but not received this diagnosis at the N3C institution contributing their data. Our eligibility criteria for controls is carefully designed to mitigate this, using multiple strategies including use of the computable phenotype for PASC generated by Pfaff et al [3] to eliminate patients with high likelihood of PASC from the control population. Note that in all our analyses, we will only use patients from sites reporting U09.9 in their N3C data. Not all sites reflect this code as it was only recently introduced and the data models they use to map their source data to a common data model may not have been updated since that time. \n\nIn order to explore the quantitative and qualitative relationship between Long COVID and COVID related mortality as described in the PHASTER query aims, we plan to perform this project analysis in two parts:\nIn a cohort of both Long COVID and non Long COVID patients (with and without documented COVID-19 infection), we will train supervised machine learning models for prediction of mortality within one year of the COVID index date, with demographic features and patient CCI score at the time of COVID index used as features.\nIn a cohort of Long COVID patients, we will train supervised machine learning models for prediction of mortality within one year, with demographics, individual conditions from the Charlson Comorbidity Index (CCI) [4][5] as well as CDC identified risks of complications from COVID. We will also add features for other existing Long COVID risk factors (such as hospitalization at the time of COVID) identified in our prior N3C research [6]. \n\nMATCHING for ANALYSIS 1: In the first part, non Long COVID patients would include people who had COVID and people who didn?t have COVID. In order to balance our small known PASC cohort with the COVID positive and COVID negative controls, we will use matching, randomly assigning 1 case to 1 controls in each control group (for a total of two control patients for each COVID+ PASC case). Matching will be done without replacement, selecting patients from the same health system, age +/- 10 years, index date +/- 45 days, and post-index observation period within +/- 60 days of corresponding case's post-index observation period. These matching features are essential, particularly in a death analysis where differences in follow-up will impact the likelihood of death data being captured. Also, the phase of the pandemic will impact mortality risk for patients in both COVID+ cohorts. \n\nNote that we will use the PPRL linked death data that is available for some sites in the enclave. Because we are matching cases to controls as described above, and because we are controlling for site we are comfortable including this data without limiting our analysis only to sites who enable PPRL.\n", "accessing_institution": "University of Virginia" }, { "uid": "RP-048447", "title": "Monocytopenia in COVID-19", "task_team": false, "dur_project_id": "DUR-7AA3220", "workspace_status": "CLOSED", "lead_investigator": "Nathan Cummins", "research_statement": "Abnormalities in peripheral blood leukocytes subsets have been widely described in COVID-19. However, there have been conflicting reports regarding whether monocytes are decreased in COVID-19, and whether a decrease in monocytes is associated with severe COVID-19 or poor outcomes. We propose to analyze the Synthetic N3C dataset to determine if circulating monocytes are reduced in COVID-19, and if monocytopenia is associated with worse outcomes.", "accessing_institution": "Mayo Clinic" }, { "uid": "RP-C06B65", "title": "Does metformin show a reduction of severe outcomes of COVID-19 or of Long COVID in the N3C Data Enclave - Carolyn Bramante", "task_team": false, "dur_project_id": "DUR-7B95115", "workspace_status": "ACTIVE", "lead_investigator": "Carolyn Bramante", "research_statement": "In vitro data show metformin inhibits severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus1-3 and pathogenic inflammatory responses to the virus.4,5 Clinical trial data show metformin prevents severe Covid-19 and Long Covid.3,6-8 However these trials did not enroll low-risk adults nor adults with prior SARS-CoV-2 infection, so generalizability to the current state of the pandemic is unknown. \n\nThe primary aim is to assess whether metformin started at the time of Covid-19 diagnosis is associated with lower incidence of Long Covid using observational data to emulate a clinical trial.\n\nKey secondary objectives include assessing whether starting metformin earlier in the course of infection is associated with a larger effect size; and describing the frequency of different definitions of Long Covid across the different waves of the pandemic.\n\nRetrospective cohort analysis using trial emulation techniques in the National Covid Cohort Collaborative (N3C) database in adults with documented SARS-CoV-2 infection.9 The index date will be the date of first documented SARS-CoV-2 infection, and the exposure of interest: new metformin prescription on Day -2 to +3 of Covid-19 infection. This time window may be adjusted if the numbers are small. ", "accessing_institution": "University of Minnesota" }, { "uid": "RP-43426A", "title": "Use of the N3C enclave and machine learning to create generalizable algorithms that predict patient outcomes at (a) diagnosis, and (b) time of hospitalization - Andrew Barros", "task_team": false, "dur_project_id": "DUR-84B8C35", "workspace_status": "CLOSED", "lead_investigator": "Andrew Barros", "research_statement": "In this project, we propose to use, develop, and validate a machine learning approach for predicting outcomes at the time of outpatient diagnosis and hospitalization. To accomplish this, we will utilize existing N3C shared logic to build a cohort of patients with COVID, abstract relevant features, and develop predictive models for hospitalization, organ failure, and death using both gradient boosted trees and machine learning derived linear models. We will then internally validate the model across time and geography. Additionally, we will compare two approaches which balance flexibility and interpretability. This proposal is highly responsive to the stated aims. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-0585B0", "title": "Investigating the Epidemiology of SARS-CoV-2 and Influenza A Co-Infection Among Pediatric Patients in the United States Using the National COVID Cohort Collaborative (NC3)", "task_team": false, "dur_project_id": "DUR-7C176C8", "workspace_status": "ACTIVE", "lead_investigator": "Ozair Naqvi", "research_statement": "SARS-CoV-2 and influenza A are respiratory diseases that can lead to severe outcomes in children, such as hospitalization, multisystem inflammatory syndrome (MIS-C), and death. Co-infection or co-detection of these diseases has been reported among children; however, a lack of studies that explicitly address the epidemiology of coinfection has limited our understanding of the impact and significance of this public health issue. A need exists for more robust study designs to allow for a precise evaluation of the incidence, specific risk factors, and associated outcomes with pediatric SARS-CoV-2/influenza A co-infections. Characterizing the epidemiology of SARS-CoV-2/influenza A co-infections among children warrants special consideration, given that children play a significant role as viral vectors in households and schools and often affect school attendance. For this proposal, we seek to implement a large-scale multicenter study within the National COVID Cohort Collaborative (N3C) to 1) describe SARS-CoV-2/influenza A co-infection cases, hospitalizations, and severe outcomes over time, 2) identify differences in risk factors and outcomes between pediatric patients with SARS-CoV-2/influenza A co-infection versus single infection, with an emphasis on patients with underlying medical conditions; and 3) develop and validate a mathematical model to understand and predict the transmission dynamics of pediatric SARS-CoV-2/influenza. These findings are anticipated to have implications for equitable healthcare strategies in combating the impact of SARS-CoV-2/influenza A co-infection on vulnerable pediatric populations.", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-7672AE", "title": "CURE ID Data Utility Verification", "task_team": false, "dur_project_id": "DUR-7C97CDB", "workspace_status": "ACTIVE", "lead_investigator": "Ruth Kurtycz", "research_statement": "To demonstrate the utility of the EHR data collected in the CURE ID Drug Repurposing project funded by the FDA, we are executing a series of trial emulation analysis with COVID-19 as a use case, using causal inference modeling to replicate the trial methodology on observational data. To date, we have explored use of dexamethasone and remdesivir within the extracted EHR data in CURE ID. Access to NC3 limited data is requested to verify the results of these emulation analyses on a similar data source. Demonstrating the utility of observational data within treatment of COVID-19 will allow for use of these data and methods in evaluating treatment options of rare and emerging diseases in the future.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-A82ED1", "title": "Factors that influence respiratory outcomes after COVID19", "task_team": false, "dur_project_id": "DUR-7DB5C39", "workspace_status": "ACTIVE", "lead_investigator": "Julian Solway", "research_statement": "In this project, we will explore factors that influence the development or worsening of respiratory symptoms (e.g., dyspnea, fatigue, weakness, cough, chest discomfort, etc) or lung diseases (e.g., pulmonary fibrosis, interstitial lung disease, airways disease, etc) during or after COVID19 (including post-acute sequelae of COVID [PASC], or ?long COVID?), and compare their roles in COVID19 vs other conditions that cause respiratory symptoms through the use of the N3C data enclave. Among factors to be studied are the course of treatment and critical care and social determinants of health.\n", "accessing_institution": "University of Chicago" }, { "uid": "RP-EB6E67", "title": "Consortium for Clinical Characterization of COVID-19 by EHR", "task_team": false, "dur_project_id": "DUR-7E1DCD7", "workspace_status": "CLOSED", "lead_investigator": "Priyam Das", "research_statement": "4CE is an international consortium for electronic health record (EHR) data-driven studies of the COVID-19 pandemic. The goal of this effort?led by the i2b2 international academics users group?is to inform doctors, epidemiologists and the public about COVID-19 patients with data acquired through the health care process. Specifically, our research question is whether the clinical course of patients' laboratory test trajectories predicts the severity of their acute and long-term COVID disease, and how does this vary for patients who were infected during different waves of the pandemic.\n\n\n", "accessing_institution": "Harvard University" }, { "uid": "RP-6595B2", "title": "High-Throughput Covid-19 (sub)-Phenotyping for Personalized Patient Prognosis and Care", "task_team": false, "dur_project_id": "DUR-7ED828B", "workspace_status": "CLOSED", "lead_investigator": "Ravi Srinivasan", "research_statement": "Most studies of COVID-19 risk factors (related to age, gender, diabetes, heart disease and hypertension, smoking, blood type, obesity, environmental and genetic factors, etc.) have considered the effect of one ?factor? at a time, using standard cross-sectional multivariate modeling techniques. As such, they do not quantify the simultaneous effect of multiple, heterogeneous factors, and the factors considered are relatively few and hand-picked. For example, all the studies mentioned in a recent meta-survey of EHR-based data analysis [Chen et al., IEEE JBHI, 2016] fall in this category. Similarly, in terms of patient progression over time, only classical biostatistics approaches such as Cox proportional hazards (conditioned on a few covariates) have been considered [Chen et al., J. Biomed. Inform, 2015]. Both types of studies are handicapped by not being able to consider the dynamic multi-way interactions among numerous potential factors that uniquely characterize COVID-19. There is a compelling need to identify a large number of fine-grained patient subgroups that are relatively homogeneous in terms of infection progression and treatment needs, which can then enable us to provide much more personalized and targeted treatments for substantially improved outcomes. Such groups can be determined by developed COVID-specific sub-phenotypes, however traditional phenotyping approaches (such as eMERGE) take many months of human expertise to produce a small number of ?gold-standard? phenotypes. This proposal provides a much faster alternative to a year-plus wait for golden COVID-19 phenotypes. Our research team is highly qualified for carrying out this project with a strong track record of publications in computational phenotyping, including two best paper awards (AMIA 2017, KDD18), and a First Runners-up for AMIA?s 2018 Best Dissertation Award.", "accessing_institution": "The University of Texas at Austin" }, { "uid": "RP-5208B2", "title": "Long-term health complications of SARS-CoV2 exposure.", "task_team": false, "dur_project_id": "DUR-8314135", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Hicks", "research_statement": "As SARS-CoV2 is a novel pathogen and is only now becoming well characterized and understood, there has yet been insufficient data to predict long-term post-acute sequelae of the virus. Acute respiratory complications occasionally leading to mortality has been successfully identified, but long-term complications are not yet understood. Preliminary studies show cognitive complications, among others, in those who contracted COVID-19 which can persist beyond the acute contraction of the virus. In this study, we seek to use N3C medical record data to identify long-term effects of SARS-CoV2 in the population by comparing diagnosis rates in the population that has contracted the virus to the population that has not. Limited data is requested for this project to associate positive SARS-CoV2 tests to diagnoses rates with importance to difference in time between the two events.", "accessing_institution": "Nemours" }, { "uid": "RP-AAE3BE", "title": "Strength and weaknesses of in silico replication of epidemiological studies: Benzodiazepines treatment and survival analysis among COVID patients with history of cancer. ", "task_team": false, "dur_project_id": "DUR-83D9821", "workspace_status": "ACTIVE", "lead_investigator": "Stephanie Hong", "research_statement": "Objective \nTo evaluate mortality outcomes and disease progression in COVID patients with history of cancer exposed to benzodiazepine prescriptions, including alprazolam (Xanax), diazepam (Valium), lorazepam (Ativan), or clonazepam (Klonopin), which are commonly used to manage anxiety during cancer treatment. This study specifically examines benzodiazepine prescriptions administered within 30 days prior to a COVID-19 diagnosis or positive test result, utilizing retrospective electronic health record (EHR) data from patients with a history of cancer.\n\nWhat are the implication of our findings?\nCertain benzodiazepines have off-target activation of GPR68, and prior studies suggest that activation of GPR68 signaling exacerbates acute respiratory distress syndrome (ARDS).4 Observed changes in incidence rates over pandemic eras may highlight the critical role of mortality outcomes and disease severity among patients exposed to specific types of benzodiazepines and among cohorts who had history of breast, brain, colon, and pancreatic cancer.\n\nBackground\nBenzodiazepines are a class of drugs that suppress the activity of the central nervous system, helping to alleviate symptoms of anxiety, insomnia, and seizures. Cancer patients are often prescribed benzodiazepines to manage these issues, which may arise as a result of their disease or treatment. However, limited research has been conducted on how benzodiazepine use might influence cancer outcomes and mortality.\nA previous study reported differences in patient outcomes and the association between the use of different classes of benzodiazepines and survival in patients with pancreatic cancer. When researchers examined the disease stage, progression, and treatment received in relation to specific benzodiazepines, they found that patients prescribed alprazolam had a lower risk of disease progression or death compared to those who did not take alprazolam. In contrast, patients prescribed lorazepam had a 3.83-fold higher risk of disease progression or death than those who did not take lorazepam.2\nResearch Design and Methods\nUsing both National COVID Cohort Collaborative (N3C) dataset we wish to replicate the findings that the use of benzodiazepine lorazepam (Ativan), but not benzodiazepine alprazolam (Xanax) is associated with the cancer progression and death. We will also examine whether lorazepam use, but not alprazolam use is associated with worse prognosis after COVID-19 diagnosis: the mortality outcome as the primary outcome. We will also examine how the mortality outcome is associated with emergency room visits, hospitalizations, and severity of disease progression. We will characterize each cancer types; pancreatic, brain, prostate, colon and breast cancer, and associated mortality. \n", "accessing_institution": "Johns Hopkins" }, { "uid": "RP-4845A4", "title": "Study the Epidemiology and Outcomes of COVID-19 in Patients with Sickle Cell disease: A National Cohort Study", "task_team": false, "dur_project_id": "DUR-86BB652", "workspace_status": "ACTIVE", "lead_investigator": "Anjali Sharathkumar", "research_statement": "Inflammation and vasculopathy plays pivotal role in pathogenesis of Novel Coronavirus 2019 SARS-CoV-2 (COVID-19) disease.. Sickle cell disease is a chronic inflammatory condition and associated with procoagulant milieu. It is associated with multiorgan involvement including lung, brain, and kidneys. Majority of these patients are treated with disease modifying therapy with hydroxyurea. It is unclear if hydroxyurea therapy offers any protective effect with COVID-19 The proposed study leverages upon the wealth of N3C Data Enclave and its powerful analytic capabilities. The deidentified dataset from N3C data will be used to characterize the epidemiology and outcomes of COVID-19 in patients with sickle cell disease. The research activities in this proposal will not only enable us to develop tailored strategies for better prevention and treatment of COVID-19 in hemophilia population but will guide us about appropriate regimen for factor replacement therapy during this pandemic.", "accessing_institution": "University of Iowa" }, { "uid": "RP-9D62C2", "title": "The Risk Factors of Death of Covid 19", "task_team": false, "dur_project_id": "DUR-86CE633", "workspace_status": "CLOSED", "lead_investigator": "Jianqiao Ma", "research_statement": "purpose: 1) among the covid positive population, comparing the baseline characteristics of hospitalized and non-hospitalized and non-hospitalized groups; 2)among the covid positive population, comparing the baseline characteristics of death and surviving groups.\nmethods: Using logistic regression, factor analysis, ROC, survival analysis, and Decision Curve Analysis to investigate the associations between different characteristics and outcomes.\n", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-645ECC", "title": "The Use of Pulmonary Vasodilators in Patients with COVID-19 in the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-87329E1", "workspace_status": "ACTIVE", "lead_investigator": "Adeel Abbasi", "research_statement": "Pulmonary vasodilators offer several potential benefits to patients with refractory hypoxemia in the setting of severe acute respiratory distress syndrome (ARDS), including improved ventilation-perfusion mismatch, oxygenation, pulmonary pressures and right ventricular function.1 While randomized trials of inhaled nitric oxide, a pulmonary vasodilator often used in the acute setting, in patients with ARDS have not demonstrated a significant mortality benefit, the efficacy of newer pulmonary vasodilators in patients with ARDS is unclear as studies are lacking.2-5 Pulmonary vasodilators are nevertheless used as adjunctive therapies to rescue patients with refractory hypoxemia in the setting of severe ARDS, and have been used to treat patients with severe COVID-19 associated ARDS.6-9 However, effects on mortality, oxygenation, length of ICU stay or mechanical ventilation, use of vasopressors, and side effects are not known. The practice patterns of pulmonary vasodilator use in patients with severe COVID-19 are limited to single-center studies. We aim to describe the use of pulmonary vasodilators in patients hospitalized with severe COVID-19 within the multicenter National COVID Cohort Collaborative (N3C), and investigate the efficacy of pulmonary vasodilator therapy in patients with severe COVID-19.", "accessing_institution": "Brown University" }, { "uid": "RP-3AB7C6", "title": "Developing and validating AI-COVID, a machine learning model predicting COVID-19 using only routine blood tests", "task_team": false, "dur_project_id": "DUR-876669C", "workspace_status": "CLOSED", "lead_investigator": "Peter Toshev", "research_statement": "Biocogniv Inc. has developed a machine learning model, AI-COVID, to rule out COVID-19 using only routine blood tests among adults presenting to emergency departments. We request the N3C Level 2 (de-identified) data to validate model performance metrics?including AUROC, sensitivity, specificity and negative predictive value analysis?on routine laboratory tests ordered from emergency departments (ED) visits. We also aim to explore model performance on laboratory tests ordered from non-ED patient encounters. The primary objective of this effort is to further demonstrate model robustness across clinical centers, patient pools, geography and demography, laboratory testing equipment and other idiosyncrasies of patient data collection. Our secondary objective is to explore the model performance on inpatient encounters and ED encounters involving asymptomatic patients. . Use of the AI-COVID model may help clinicians cohort potentially infectious patients, inform selective use of PCR-based testing for the diagnosis of COVID-19 infections, and improve quality of care for clinical institutions suffering limited resources.", "accessing_institution": "Biocogniv" }, { "uid": "RP-7A6648", "title": "Medications and comorbid conditions affecting COVID-19 progression", "task_team": false, "dur_project_id": "DUR-8867A68", "workspace_status": "CLOSED", "lead_investigator": "Andrey Rzhetsky", "research_statement": "We aim to develop, benchmark, and disseminate a collection of models that will predict patient-specific disease severity on the basis of partial and locale-specific population information, individual health and treatment histories, locally circulating viral strains, and the infection?s temporal and spatial dynamics. This collection will account for data heterogeneity, data incompleteness, and differences in modeling approaches, so that forecasts can be averaged over a spectrum of theories and encapsulated into probabilistic models. ", "accessing_institution": "University of Chicago" }, { "uid": "RP-305A2C", "title": "COVID-19 and GI Bleeding: Outcomes in Emergency Department and Inpatient Settings", "task_team": false, "dur_project_id": "DUR-8A44714", "workspace_status": "CLOSED", "lead_investigator": "Alfred Anzalone", "research_statement": "The purpose of this study is to define incidence of GI bleeding in Covid-19 positive adults seen in the ED or admitted to the hospital at sites participating in the N3C. Further, we plan to characterize outcomes in these patients (length of stay, mortality, readmission, etc) as well as factors that may predispose to GI bleeding (use of anti-platelet medications, anticoagulants, age, comorbidities). ", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-E4474B", "title": "AIM-AHEAD Education Project", "task_team": false, "dur_project_id": "DUR-8A868BE", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "Enclave Data Science Educational Enclave contains access to synthetic data resources such as (CMS SynPUF / SYNTHEA). The Data Science Educational Enclave is an open resource available for the teaching, development of best practices, and open science in the domains of data harmonization, quality, curation, machine learning, team science and collaborative analytics environments. \n\nMembers of the Data Science Educational Enclave will have full access to the N3C Clinical Collaborative Analytic Environment including but not limited to shared educational material, concept sets, knowledge objects, public datasets (census, SDI), LLM, Jupyter notebooks and other tools.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-876915", "title": "Bacterial and fungal infections in COVID-19", "task_team": false, "dur_project_id": "DUR-8A8E2C3", "workspace_status": "ACTIVE", "lead_investigator": "Muhammad Gul", "research_statement": "The research focus will be on investigating the bacterial and fungal infections in COVID-19 and the impact of treatments including steroids on the development of infections. COVID-19 course has been complicated with further in hospital infections and our study wants to compare the effect of therapeutics on the incidence of in hospital infections in COVID patients. We also want to investigate the impact of the infections on the hospital course; Length of Stay (LOS), Intensive care units (ICUs) admission, mortality. The data points included in this study for research analysis would include age, sex, race, length of hospitalization stay, the medical comorbidities, COVID-19 diagnosis date, infections during admission, medical treatments including steroids, remdesivir and tocilizumab use, mechanical ventilation of patients, oxygen requirement, ICU hospitalization, Hi flow requirement, shock, mortality, labs, imaging.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-9282DB", "title": "Radiation Treatment Quality Disparities in the COVID-19 Era", "task_team": false, "dur_project_id": "DUR-8BEB12D", "workspace_status": "CLOSED", "lead_investigator": "Charisse Madlock-Brown", "research_statement": "Prior studies have shown associations between RT interruption and low socioeconomic status, racial minority status, advanced age, rural location, and other factors associated with fragile social support. Poverty closely tracks with health outcome disparities, including elevated cancer incidence and mortality rates. The pandemic is likely to widen these disparities. To our knowledge, there has been no study evaluating potential impact of COVID-19 on downstream RT interruption across cancer patient sociodemographics in any U.S. region, although current pilot investigation at our institution suggests significant increases in treatment interruptions during the pandemic, especially in vulnerable populations. In this study, we aim to confirm feasibility of abstracting and cataloging RT interruption rates in the N3C data to estimate treatment trends during the pandemic, and to identify candidate social risk predictors for interruption across the COVID-19 pandemic timeline. \nWe hypothesize that high COVID-19 transmission rates will associate with high RT interruption, as well as previously identified social risk factors associated with RT interruptions. RT interruption rates after the start of the pandemic (March 15, 2020) will be significantly higher than baseline rates documented at our institution and others before the pandemic.", "accessing_institution": "University of Tennessee Health Science Center" }, { "uid": "RP-BAE95E", "title": "First Episode Psychosis (FEP) among COVID-19 patients: A Time to Event Survival Analysis using Data from the National COVID Cohort Collaborative (N3C)", "task_team": false, "dur_project_id": "DUR-8C04531", "workspace_status": "CLOSED", "lead_investigator": "Xiaoming Zeng", "research_statement": "Neuropsychiatric symptoms have been identified as manifestations of COVID-19 disease, including patients in the long haul COVID. More than 50 case reports so far described the clinical course of psychosis among COVID-19 patients. One study using data from TriNetX identify a statistically significant relationship between new onset of psychotic disorder and COVID-19 diagnoses. We like to replicate the same study using N3C data with a special focus on the younger age group (15-30). The reason to focus on the age group is that early intervention and support for youth FEP patients mitigate the potential trajectory of developing into serious mental illness like schizophrenia. ", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-543440", "title": "Determine Impact of COVID-19 Pandemic-related Social Determinants of Health (SDoH) on Child Mental Health and Suicide Trajectory and Phenotypes", "task_team": false, "dur_project_id": "DUR-8C96363", "workspace_status": "ACTIVE", "lead_investigator": "Yunyu Xiao", "research_statement": "Our overall objective is to examine the long-term impact of pandemic-related SDoH and policies on child MH and SI/SA. We will identify new onset and enduring pandemic-related long-term trajectories of child MH and SI/SA, and use state-of-the-art causal inference technique to estimate the impact of social determinants of health (SDoH) on child mental health and suicidal ideation and attempts. ", "accessing_institution": "Weill Cornell Medicine" }, { "uid": "RP-696509", "title": "Development of a risk stratification tool to identify children and young adults at risk for major adverse cardiac events following SARS-COV2 infection and or exposure.", "task_team": false, "dur_project_id": "DUR-8E7BDB9", "workspace_status": "ACTIVE", "lead_investigator": "Shubhika Srivastava", "research_statement": "SARS-CoV2 infection has been linked to either acute myocardial involvement or myocarditis or a delayed\nmultisystem inflammatory syndrome in children (MIS-C), a potentially serious condition affecting multiple organs\nincluding cardiac and vascular tissues. Myocarditis secondary to SARS-CoV2 may go unrecognized and present\nlate with nonspecific symptoms of fatigue and chest pain. Observations made early during the COVID-19\npandemic, stemming from patients hospitalized with COVID-19, suggest that a considerable (25- 80%)\npercentage manifest clinically-relevant myocardial involvement, as evidenced by elevated cardiac troponin and\nthe appearance of regional functional abnormalities and scarring noted on noninvasive imaging. 1The long-term\ncardiovascular effects of unrecognized myocarditis and MIS-C across this range are not well understood, and at\npresent, there is no widely accepted definition of what constitutes clinically relevant myocardial injury secondary\nto SARS-CoV2 infection. A better understanding of the long-term sequelae of recognized and unrecognized\ncardiac involvement in the pandemic is critically needed. Our team seeks to address these knowledge gaps and\nprovide a framework for better understanding and predicting the effects of infectious and inflammatory conditions\non cardiac performance. In this pilot study, we focus on the risk to children and youth of returning to athletic\nparticipation after SARS-CoV2 exposure.", "accessing_institution": "Nemours" }, { "uid": "RP-A10D67", "title": "Modelling of Covid-19 Progression", "task_team": false, "dur_project_id": "DUR-8EC048A", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Hicks", "research_statement": "Mathematical models currently used to explain the progression of pandemic-inducing pathogens are wholly deficient at explaining and accurately predicting pandemics with ever-evolving characteristics. The current basic SEIR model (susceptible, exposed, infectious, and recovered) can only account for one wave of infection because it does not account for dynamically changing susceptibility, infectiousness, etc. With this project, we aim to develop a mathematical modeling tool developed from chemical reaction kinetics equations that can better predict the dynamic evolution of the COVID-19 pandemic among others, as well as predict the effect of certain policies on transmission (such as mask mandating, social distancing, vaccination, etc.) This model already shows promise with adult populations, as validated by recursive forward prediction. Using patient data on COVID test type, result, date as well as recovery or death dates, a mathematical model can be built to represent changes in infection over time. Also, using deprivation indices and ZIP code, this model may be able to predict infection at a localized level.", "accessing_institution": "Nemours" }, { "uid": "RP-74F392", "title": " Leveraging Machine Learning to Assess Post-COVID-19 Glycemic Control in Diabetic Patients: An Analysis of HbA1c Levels Post-Infection", "task_team": false, "dur_project_id": "DUR-917CCEA", "workspace_status": "ACTIVE", "lead_investigator": "Marie Lluberes", "research_statement": "Diabetic patients are typically diagnosed and monitored through glucose levels, with HbA1c being a critical marker for long-term glycemic control. Health disparities among vulnerable populations often impair access to optimal care, exacerbating outcomes, particularly in chronic conditions like diabetes. The COVID-19 pandemic has significantly disrupted glycemic control and worsened diabetes-related complications, highlighting the need for an in-depth understanding of its impact on HbA1c levels.\nThis study aims to analyze the impact of COVID-19 on glycemic control in diabetic patients, specifically focusing on changes in HbA1c levels post-infection. Traditional statistical methods may not fully capture the complex interactions between COVID-19 and glycemic control. Therefore, this study seeks to leverage machine learning techniques to gain a deeper understanding of the impact of COVID-19 on glycemic control in diabetic patients. By integrating advanced analytical methods with comprehensive data from the NCATS N3C enclave, the research aims to provide valuable insights that can inform targeted interventions and improve diabetes care in the post-pandemic landscape.\n", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-50C554", "title": "Antibody response to SARS-CoV-2 in people with multiple sclerosis treated with B cell depleting therapies", "task_team": false, "dur_project_id": "DUR-93C5979", "workspace_status": "CLOSED", "lead_investigator": "Alfred Anzalone", "research_statement": "We aim to investigate IgG antibody response to SARS-CoV-2 in patients with multiple sclerosis who are being treated with B-cell depleting therapies such as rituximab and ocrelizumab given B-cell deleting therapies may impair or blunt antibody response to SARS-CoV-2 infection and impact future vaccine readiness. ", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-9A216C", "title": "Assessing Severity of Disease based on Immune History and Variant", "task_team": false, "dur_project_id": "DUR-94837A5", "workspace_status": "ACTIVE", "lead_investigator": "Bryan Lewis", "research_statement": "As the COVID-19 pandemic unfolded the the timing of variant waves is necessarily correlated with immune histories. This study will attempt to control for the comorbidities of indviduals and their medically recorded immune histories (bolstered by external records of vaccination rates and model estimate levels of infections) to estimate the relative differences in severity (and differential presentations) of the different waves of variants and sub-variants. Additionally, this may provide a basis for defining cohorts of the population based on their immune histories and variant exposures that may have implications for long covid or other associated co-morbidities in the future.", "accessing_institution": "University of Virginia" }, { "uid": "RP-67D8B5", "title": "N3C Cohort Characterization", "task_team": false, "dur_project_id": "DUR-94BBC49", "workspace_status": "CLOSED", "lead_investigator": "Tell Bennett", "research_statement": "The aims of this project are to clinically and geographically characterize the adults (SA 1) and children (SA 2) in the NIH-funded National COVID Cohort Collaborative (N3C) database and their treatment pathways. This is a N3C Consortium project.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-C0EE8E", "title": "[N3C Operational] Collaborative Analytics: Tools, Algorithms, Variable Assessment, and Validation", "task_team": false, "dur_project_id": "DUR-964520D", "workspace_status": "CLOSED", "lead_investigator": "Joel Saltz", "research_statement": "This is a request for an operational DUR to allow the Collaborative Analytics technical team to develop and validate common N3C infrastructure. ", "accessing_institution": "Stony Brook University" }, { "uid": "RP-E12A44", "title": "Early Antibiotic Use in Severe COVID19", "task_team": false, "dur_project_id": "DUR-981EAC4", "workspace_status": "ACTIVE", "lead_investigator": "Andrew Barros", "research_statement": "Some medications (such as antibiotics, diuretics, and insulin) are commonly used in people who are hospitalized with COVID-19. In the project, we propose to evaluate temporal and inter-center trends in utilization of common medications and use causal inference techniques to evaluate the association of common drug exposures with adverse outcomes and death. ", "accessing_institution": "University of Virginia" }, { "uid": "RP-75E880", "title": "Impact of COVID-19 Infection and the Pandemic Era on Patient Outcomes ", "task_team": false, "dur_project_id": "DUR-9822261", "workspace_status": "ACTIVE", "lead_investigator": "Paul Kuo", "research_statement": "Healthcare delivery was significantly altered during the COVID-19 pandemic. Our main objective is to evaluate the impact of COVID-19 infection on patient outcomes, with secondary objectives to study the impact of the COVID-19 pandemic on patient outcomes. Our research will use the limited dataset version of N3C (National COVID Cohort Collaborative). We will utilize machine learning modelling techniques, in addition to standard univariate and multivariate analysis. ", "accessing_institution": "University of South Florida" }, { "uid": "RP-298398", "title": "Associations between bariatric surgery, chronic kidney disease, and COVID-19 outcomes", "task_team": false, "dur_project_id": "DUR-9838D56", "workspace_status": "CLOSED", "lead_investigator": "Siddharth Madapoosi", "research_statement": "Chronic Kidney Disease (CKD) affects more than 37 million adults in the United States and is one of the leading causes of death [1]. Obesity, hypertension, and diabetes are independent risk factors for the development of CKD and the management of these conditions are critical to prevent the onset of CKD [2-4]. These are also risk factors for severe COVID-19, and there exists a complex interplay between CKD and COVID-19. CKD is associated with an increased risk of severe COVID-19 and mortality, and COVID-19 infection may predispose patients to developing CKD independent of clinically-apparent AKI [5-6]. Weight loss may therefore contribute to improved renal function and lower risk of progression to CKD and severe COVID-19 [4]. Bariatric surgery (BS) provides an option for weight loss with additional well-documented effects, including hypertension, diabetes, and obstructive sleep apnea management as well as cardiovascular disease risk reduction [7-8]. Our prior research conducted at the University of Michigan suggests that BS is associated with a slower rate of eGFR decline in patients with impaired renal function, including those with CKD. The goal of this study is to expand on this research to determine whether BS is associated with a slower rate of eGFR decline and improvement in proteinuria in CKD patients following COVID-19, and whether BS lowers the risk of severe COVID-19 and mortality among patients with CKD. This research will allow us to better understand the risk factors for severe COVID-19 and further elucidate the relationship between CKD, obesity, and COVID-19.\n\n1.\tChronic kidney disease: Common - serious - costly. Centers for Disease Control and Prevention. https://www.cdc.gov/kidneydisease/prevention-risk/CKD-common-serious-costly.html. Published February 28, 2022.\n2.\tNavaneethan SD, Yehnert H. Bariatric surgery and progression of chronic kidney disease. Surgery for Obesity and Related Diseases. 2009;5(6):662-665. doi:10.1016/j.soard.2009.01.006\n3.\tCurrie A, Chetwood A, Ahmed AR. Bariatric Surgery and Renal Function. OBES SURG. 2011;21(4):528-539. doi:10.1007/s11695-011-0356-7\n4.\tNeff KJ, Frankel AH, Tam FWK, Sadlier DM, Godson C, le Roux CW. The effect of bariatric surgery on renal function and disease: a focus on outcomes and inflammation. Nephrology Dialysis Transplantation. 2013;28(suppl_4):iv73-iv82. doi:10.1093/ndt/gft262\n5. Schiffl H, Lang SM. Long-term interplay between COVID-19 and chronic kidney disease. Int Urol Nephrol. 2023;55(8):1977-1984. doi:10.1007/s11255-023-03528-x\n6. Bruchfeld A. The COVID-19 pandemic: consequences for nephrology. Nat Rev Nephrol. 2021;17(2):81-82. doi:10.1038/s41581-020-00381-4\n7.\tBolignano D, Zoccali C. Effects of weight loss on renal function in obese CKD patients: a systematic review. Nephrol Dial Transplant. 2013;28 Suppl 4:iv82-iv98. doi:10.1093/ndt/gft302.\n8.\tWolfe BM, Kvach E, Eckel RH. Treatment of Obesity. Circulation Research. 2016;118(11):1844-1855. doi:10.1161/CIRCRESAHA.116.307591", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-30AB9A", "title": "Predictive analysis of employment type and health outcomes during COVID-19", "task_team": false, "dur_project_id": "DUR-988C02E", "workspace_status": "CLOSED", "lead_investigator": "Charisma Atkins", "research_statement": "During COVID-19 many industries were affected. with healthcare workers being the priority. However, other industries are also grossly affected by their lack of available employees and services for those employees. This research is looking to evaluate the impact of employment on COVID-19 related health outcomes and its health disparity impact. The outcomes of interest are hospitalizations and deaths associated with COVID-19 diagnosis. Understanding the impact of COVID-19 and other infectious diseases of the workforce will help decisionmakers plan an prepare for future public health events. The results of this analysis will be used to help develop workforcee policieis to help the American worker seek and maintain their employment during and after these public health events. ", "accessing_institution": "University of Georgia" }, { "uid": "RP-5BA274", "title": "Thrombosis risk factors and markers of inflammation in pediatric patients with COVID19-related MIS-C ", "task_team": false, "dur_project_id": "DUR-98936AA", "workspace_status": "CLOSED", "lead_investigator": "Amanda Scheuermann", "research_statement": "Infection with the SARS-CoV-2 virus responsible for the COVID-19 pandemic often produces an inflammatory state. With the recent increase in the delta variant, which seems to be infecting more children, we expect an elevation in the number of children suffering from multisystem inflammatory syndrome (MIS-C). GAS6 and AXL levels are increased in adult Covid-19 and predict clinical outcomes, but less is known about protein S and MERTK in Covid19/MIS-C, especially in children. Our current analysis of in silico pediatric gene expression data will help overcome that gap by using existing resources to generate new pertinent findings to compare to protein levels we also plan to identify. The N3C database will allow for comparison of VTE rates and available inflammatory markers in cohorts of pediatric patients with COVID-19-related MIS-C to those with other non-COVID inflammatory disease. ", "accessing_institution": "Medical College of Wisconsin" }, { "uid": "RP-0EBA17", "title": "Methods for generalizable clinical prediction models for prognosis in patients with diagnosis of COVID-19", "task_team": false, "dur_project_id": "DUR-991BCC1", "workspace_status": "ACTIVE", "lead_investigator": "Satyanarayana Vedula", "research_statement": "Prognostic models are not only inaccurate but also unlikely to generalize when treatment patterns are ignored. In the case of Covid-19, treatments have rapidly evolved within a compressed timeframe of 1 year. There is substantial heterogeneity in treatment patterns across sites and over time. The goals for this research are as follows:\n1. Describe heterogeneity in geographic and spatial treatment patterns\n2. Evaluate generalizability of existing methods to predict progression in patients with diagnosis of COVID-19 given treatment heterogeneity\n3. Develop new methods for generalizable clinical prediction models, using Covid-19 as a case study", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-26EDA6", "title": "Perioperative Outcomes in Patients with Cancer Following Recovery from SARS-CoV-2 Infection", "task_team": false, "dur_project_id": "DUR-9929274", "workspace_status": "ACTIVE", "lead_investigator": "Anai Kothari", "research_statement": "There is an increasing number of cancer patients with a prior SARS-CoV-2 infection that will require surgical oncologic treatment. This raises several key questions that have not been answered in prior literature including: Is there an elevated perioperative risk in patients with cancer undergoing surgery with prior SARS-CoV-2 infection? What is the influence on severity of prior SARS-CoV-2 infection on postoperative risk in this population? What is the optimal duration of time to defer cancer surgery in those with prior SARS-CoV-2 infection, if at all? What is the impact of ?long-COVID? syndrome on perioperative outcomes in patients with cancer diagnoses? Most existing literature focuses on small, single center series or does not consider the risk of delaying surgical intervention in patients with cancer. Specifically, recent data suggests a safe postponement period of >8 weeks (Deng, John Z, 2021) across a broad range of surgical procedures. \n\nThe specific aims of this study are: \n1-\tTo measure the risk of adverse postoperative events in patients with prior SARS-CoV-2 infection undergoing cancer-related surgical procedures. \n2-\tUnderstand the prevalence of ?long-COVID? or post-acute sequelae of SARS-CoV-2 infection (PASC) in patients undergoing elective surgery. \n3-\tApplying machine learning to develop risk-stratification tools to aid in the decision-making for optimal timing to proceed with surgery in patients with prior SARS-CoV-2 infection and cancer.", "accessing_institution": "Medical College of Wisconsin" }, { "uid": "RP-42D046", "title": "NCATS Investigation into drug efficacy of disease pathology and post-acute COVID-19 syndrome", "task_team": false, "dur_project_id": "DUR-9A97381", "workspace_status": "CLOSED", "lead_investigator": "Nathan Hotaling", "research_statement": "Answers to several important questions related to COVID-19 treatment and long-term outcomes remain unclear. The overall scope or statistical power of existing studies has largely been limited by small cohorts. Access to the de-identified (Level 2) N3C data will allow us to revisit these questions on an unprecedented scale and better understand rarer long-term consequences of the disease. Both hypothesis and data-driven approaches will be used to carry out investigations into three key areas. \nThe first involves the effect of particular drugs and drug classes in treating different stages and/or cohorts of COVID-19 including but not limited to: SSRIs, ACE inhibitors, corticosteroids, antivirals, and antihistamines. Preliminary studies have indicated that at least some of these therapies may decrease overall mortality or reduce the likelihood of clinical deterioration, although some data are conflicting. The second relates to the quantification and prediction of the relative risk, and the factors contributing to that risk, for COVID-19 patients across the severity spectrum. These models will be analyzed across various vulnerable patient cohorts including but not limited to: diabetics, COPD, kidney disease, immunocompromised patients, etc. Also, assessing the risk of specific complications over a larger population to more comprehensively determine outcome rates and comorbidities will be performed with the N3C dataset. Finally, the third involves assessing the long-term consequences of COVID-19. A more precise definition of ?post-acute COVID-19 syndrome? is needed, and patient phenotyping has yet to be carried out. Thus, both identification, stratification, and prediction of these patients will be performed using the N3C dataset. \n", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-6950DB", "title": "Assessing COVID Re-infection Risk Factors", "task_team": false, "dur_project_id": "DUR-9B017AB", "workspace_status": "CLOSED", "lead_investigator": "Shawn ONeil", "research_statement": "This project aims to identify risk factors associated with identified COVID re-infections, especially those associated with available demographic factors (age, sex, race, ethnicity) and chronic medical conditions (e.g. diabetes, heard disease, neurological conditions, asthma, COPD, anemia). ", "accessing_institution": "Oregon State University" }, { "uid": "RP-420438", "title": "Identifying predictors and outcomes of ECMO support in COVID 19 patients", "task_team": false, "dur_project_id": "DUR-9C7AB58", "workspace_status": "ACTIVE", "lead_investigator": "Ahmed Said", "research_statement": "The novel SARS-CoV2 virus and resulting COVID-19 global pandemic have put unforeseen strain on healthcare systems globally. It is incredibly challenging to know when and where to deploy resources, this is especially true for utilizing extracorporeal membrane oxygenation (ECMO). This technology functions as life sustaining therapy for the most severely affected patients, but only exists in centers with expertise. Utilizing ECMO puts a strain on local resources, and strain on healthcare systems as a whole when deciding when patients need to be transported to experienced regional centers.\nWe plan to analyze the N3C data to identify factors that are associated with the need for ECMO support inCOVID-19 patients. We will analyze the demographics, therapeutics, laboratory values, vital signs, as well as change in these values associated with the binary outcome of necessitating ECMO support.", "accessing_institution": "Washington University in St. Louis" }, { "uid": "RP-953BE4", "title": "Impact of medication on outcomes for diabetic patients", "task_team": false, "dur_project_id": "DUR-9D36C68", "workspace_status": "CLOSED", "lead_investigator": "Andrew Crouse", "research_statement": "Approximately 9% of the world-wide adult population suffers from diabetes. As the SAR-CoV-2 virus continues to spread through the population diabetes has been identified as a risk factor for severe COVID19, the disease associated with this virus. The specific mechanism underlying this is unclear. Preliminary results using our EHR COVIID cohort showed evidence that medications treating diabetes may diminish the negative impact on outcomes associated with the disease. Using the N3C data set to study subsets of the cohort with lab values may not only allow confirmation of previous results, but also may suggest mechanisms of action based on changes in lab values. ", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-A001EA", "title": "Impact of Long COVID and Socio-Demographic Factors on Substance Use Disorders Among Young Adults During the COVID-19 pandemic", "task_team": false, "dur_project_id": "DUR-A174E83", "workspace_status": "ACTIVE", "lead_investigator": "Ling Zhang", "research_statement": "Substance use disorders (SUD) have been a significant contributor to addiction and mental health issues among young adults, especially those aged 18-24 marking increased risk of overdose death. The COVID-19 pandemic along with its long-term effects may have further aggravated these issues, particularly for young adults who engage in illicit drug use. However, there remains a gap in understanding how socio-demographic factors, long COVID, and neurodevelopmental processes interact to influence substance use risk during the pandemic. This study will utilize best-fit statistical modeling to identify the key predictors of SUD and to explore the interaction between long COVID, socio-demographic factors and substance use patterns in young adults aged 18-30, focusing on disparities in long COVID diagnosis and mortality among youths living rural area with substance use disorders. \n", "accessing_institution": "Northern Arizona University" }, { "uid": "RP-630310", "title": "Using machine learning models for COVID-19 hospitalization and severity prediction among adults aged 55 or older", "task_team": false, "dur_project_id": "DUR-A217942", "workspace_status": "ACTIVE", "lead_investigator": "Yingke Xu", "research_statement": "Since the end of 2019, COVID-19 has infected people all over the world and caused millions of deaths. During the early pandemic, it became evident that older age is a risk factor for severe COVID-19. Thus, the disease disproportionately affected elders. This indicated an urgent need to predict hospitalization and severity in old adults to inform them to take appropriate prevention and health care system can distribute the limited resources. \nLiterature proved that machine learning (ML) techniques can efficiently predict numerous diseases. Many ML models have been used to predict COVID-19, but most of these works focused on adult patients, including young and middle age adults. This project aims to use ML models to predict hospitalization and severity for 55 years and older patients with COVID-19. \n", "accessing_institution": "University of Nevada, Las Vegas" }, { "uid": "RP-452372", "title": "Using big data to identify impact of chronic pulmonary diseases on mortality", "task_team": false, "dur_project_id": "DUR-A2613A5", "workspace_status": "CLOSED", "lead_investigator": "WANTING CUI", "research_statement": "The goal of this project is to assess if mortality in COVID-19 positive patients is affected by a history of chronic pulmonary disease in anamnesis. All COVID-19 positive patients will be included in our analysis. A propensity score matching was carried out to match each respiratory condition patient with two patients without history of chronic respiratory diseases in one stratum. Matching will be based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. We will include asthma, COPD, interstitial lung disease, and lung cancer. , ethnicity, race, and BMI as risk factors. Comparisons will be also carried out with COVID-19 negative patients.", "accessing_institution": "University of Utah" }, { "uid": "RP-6B8665", "title": "Predicting Adverse Outcomes in Patients Having Confirmed COVID-19 Infection Using the National COVID Cohort Collaborative (N3C) Data Using Data Mining Methods", "task_team": false, "dur_project_id": "DUR-A37412D", "workspace_status": "CLOSED", "lead_investigator": "Nabeal Saif", "research_statement": "Predicting Adverse Outcomes in Patients Having Confirmed COVID-19 Infection Using the National COVID Cohort Collaborative (N3C) Data Using Data Mining Methods", "accessing_institution": "George Mason University" }, { "uid": "RP-ECA8E0", "title": "Cardiopulmonary Sequelae Post-Acute COVID-19 Infection", "task_team": false, "dur_project_id": "DUR-A381602", "workspace_status": "ACTIVE", "lead_investigator": "Joy Jiang", "research_statement": "An estimated 10-20% of individuals infected with COVID-19 develop Post-Acute Sequelae of COVID-19 (PASC), or termed post COVID-19 condition by the WHO. Respiratory symptomatology defines acute COVID-19 yet approximately 20-30% of patients hospitalized with COVID-19 exhibit myocardial involvement. Preliminary studies have confirmed the presence of cardiopulmonary sequelae among patients post-acute infection. In this study, we aim to characterize the spectrum of cardiopulmonary sequelae following COVID-19 diagnosis among COVID-19 survivors older than 18 years of age and predict cardiopulmonary sequelae using data at initial hospital presentation for risk stratification.", "accessing_institution": "Icahn School of Medicine at Mount Sinai" }, { "uid": "RP-60B81D", "title": "N3C Diabetes and Obesity Domain Team level 2 request for data", "task_team": false, "dur_project_id": "DUR-A577EC9", "workspace_status": "ACTIVE", "lead_investigator": "John Buse", "research_statement": "The purpose of the National COVID-19 Collaborative Cohort (N3C) diabetes task team is to do work in preparation for specific studies to examine the relationship of baseline factors (drugs, labs, other diagnoses) in patients with positive and negative results of COVID-19 PCR tests with health outcomes. Initial plans exist for studies examining the relationship between baseline A1c as an index of glycemic control as well as prescription of certain diabetes drugs on outcomes such as hospitalization, ICU stays, various levels of respiratory support, death, length of stay. Full protocols for all research studies will be the subject of future IRB submission. ", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-8DAD76", "title": "Effect of oral antiviral medications on Post-COVID conditions", "task_team": false, "dur_project_id": "DUR-A87DA8C", "workspace_status": "CLOSED", "lead_investigator": "Mahnaz Derakhshan", "research_statement": "The efficacy of oral antiviral treatment in reducing hospitalization or death from COVID-19 infection has been demonstrated. With emerging post-covid conditions, there is an urgent need to examine how these medications affect post-covid conditions. This project aims to define the effect of antiviral treatment on post-COVID conditions using the N3C deidentified Data Set. The result can provide important information about the long-time effect of antivirals for upcoming related clinical trials.", "accessing_institution": "Conovita Technologies Inc" }, { "uid": "RP-5C7806", "title": "[N3C Operational] Logic Liaison Team", "task_team": false, "dur_project_id": "DUR-AA5ED5C", "workspace_status": "ACTIVE", "lead_investigator": "Johanna Loomba", "research_statement": "The [N3C Operational] Logic Liaison data use request pertains to a small set of N3C staff and community members who are in the Logic Liaison role (supporting creation of derived variables for use by Domain Teams). This DUR will provide this team shared access to a workspace where they can use de-identified data for the purpose of preparing, cleaning, and harmonizing broad generic templates. These templates will later be shared in the Knowledge Store so that they can quickly be customized for Domain Teams according to their specific needs. To become a member of the [N3C Operational] Logic Liaison project, individuals must be identified by leadership as being in this role and apply using the DUR process where attestation to follow the data user agreement, code of conduct, security training and human subjects training are required.", "accessing_institution": "University of Virginia" }, { "uid": "RP-EC2B64", "title": "autoimmune diseases as COVID-19 comorbidities ", "task_team": false, "dur_project_id": "DUR-AACB031", "workspace_status": "CLOSED", "lead_investigator": "Adam Godzik", "research_statement": "Negative outcomes of COVID-19 are strongly correlated with age, male sex and comorbidities such as diabetes or COPD are well documented and studied in literature. There is no comparable consensus on an effect of autoimmune diseases on COVID-19 outcomes. In this project we propose to study a correlation between several autoimmune diseases and the medications used to treat them on the infection rates and outcomes of COVID-19", "accessing_institution": "University of California, Riverside" }, { "uid": "RP-496D5C", "title": "A Bayesian belief network model for predicting major adverse cardiovascular events (MACE) among patients with moderate-to-severe COVID-19", "task_team": false, "dur_project_id": "DUR-ABC0498", "workspace_status": "ACTIVE", "lead_investigator": "Tzu Chun Chu", "research_statement": "Severe cardiovascular events such as acute myocardial infarction or stroke significantly contribute to cardiovascular mortality among patients with moderate-to-severe COVID-19; however, information related to risk factors and prediction models is limited. Therefore, we aim to develop a Bayesian belief network model to predict major adverse cardiovascular events (MACE) during hospitalization for COVID-19 patients. We will utilize the de-identified and high-dimensional N3C data to explore the network features and analyze dependence across variables concerning related risk factors for cardiovascular-related outcomes. This clinical decision-support tool could be a valuable approach to optimize therapies and improve the prognosis of COVID-19 patients. ", "accessing_institution": "University of Georgia" }, { "uid": "RP-6330E3", "title": "FDA CDRH Digital Diagnostics ", "task_team": false, "dur_project_id": "DUR-AEBDCE4", "workspace_status": "ACTIVE", "lead_investigator": "Mrigendra Bastola", "research_statement": "CDRH digital diagnostics program actively involves in regulatory decision making process in medical device diagnostics. The objective of this project is to identify opportunities to use COVID-19 data in regulatory decision making, including EUA/501 applications review, post market surveillance and device performance.", "accessing_institution": "Food and Drug Administration" }, { "uid": "RP-BDEAE3", "title": "Evaluation of the Detection, Use, Benefits, and Adverse Effects of Anticoagulants Among Covid-19 Patients", "task_team": false, "dur_project_id": "DUR-AEDB009", "workspace_status": "CLOSED", "lead_investigator": "Benjamin Bates", "research_statement": "Increased incidence of arterial and venous thromboemboli (VTE) have been reported among individuals infected with SARS-CoV2. Due to the potential increased risk of VTE, prophylactic methods for COVID-19 hospitalized patients have included the use of higher-than-normal doses of anticoagulation have been proposed. Small, retrospective, single center or regionally based studies, early in the COVID-19 pandemic demonstrated a potential benefit of higher-than-normal doses of anticoagulation. More recent studies have demonstrated no survival benefit with escalating doses of prophylactic anticoagulation, as well as differences of increased risk of major bleeding events with the escalation of anticoagulation intensity. . There is a knowledge gap in the understanding of the utilization and effectiveness of anticoagulation among individuals hospitalized for COVID-19 and utilization and bleeding risk of anticoagulant use among groups of individuals at high risk for bleeding events. \n\nThe National COVID Cohort Collective (N3C) represents a unique electronic health record (EHR) repository supported by the National Institute of Health. The data is structured using Observational Medical Outcomes Partnership (OMOP). Prior to assessing anticoagulant use, we will need to consider how to detect when a drug was ordered, administered, and stopped depending on the type of drug code used. This information concerning anticoagulation is imperative for our study but also will provide important knowledge on how to detect and consider drug exposure among hospitalized patients for present and future studies using N3C and other EHR-based OMOP studies. \n\nOverall, this study will assess the utilization, effectiveness, and bleeding risk based on varying doses of anticoagulation for patients hospitalized for COVID-19, while also evaluating the format of drug exposure within EHR-based OMOP drug structure. The four aims of this study are as follows: \n\n(1)\tHow are anticoagulants and other commonly prescribed medications in the hospital organized by each hospital within the N3C data structure based on route of exposure (e.g. oral, subcutaneous, and intravenous)? \n(2)\tHow are anticoagulants being utilized for individuals hospitalized for COVID-19 in the United States and whether comorbidities and other treatments may influence the dose and type of anticoagulant received? This aim will assess anticoagulant use among an individual throughout their hospitalization \n(3)\tWhat is the effectiveness and general bleeding risk of escalating type and dose of anticoagulant for general cohort hospitalized for COVID-19? \n(4)\tWhat is the use and bleeding risk among individuals who are at increased disposition for clotting and/or bleeding events? ", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-8738E4", "title": "Curating a Knowledge Base for Individuals with Coinfection of HIV and SARS-CoV-2: EHR-based Data Mining", "task_team": false, "dur_project_id": "DUR-B236D81", "workspace_status": "ACTIVE", "lead_investigator": "Chen Liang", "research_statement": "World Health Organization (WHO) confirmed that HIV is a critical risk factor for severe COVID-19. Despite a generally high risk of severe COVID-19 clinical course in individuals with HIV, the interactions between SARS-CoV-2 and HIV infections remain unclear. Risk factors for the severe clinical course of the coinfection are undetermined because individuals with the same or similar severity level of COVID-19 show different clinical characteristics. The high dimensional and interconnected risk factors create unique challenges to delineate the risk factors for individuals with the coinfection. To address these challenges, this study will leverage data mining methods to examine a cohort of individuals with HIV/SARS-CoV-2 coinfection.", "accessing_institution": "University of South Carolina" }, { "uid": "RP-421BA3", "title": "Effects of Covid or long Covid on blood pressure variability", "task_team": false, "dur_project_id": "DUR-B24468C", "workspace_status": "ACTIVE", "lead_investigator": "Marilyn Klug", "research_statement": "Day to day variability in blood pressure has been associated with long term poor health outcomes including decreased survival and increased risk of heart attack, stroke, and hospitalization. One potential cause of blood pressure variability is poor control of the blood pressure by the nervous system. We intend to determine whether having had Covid-19 or \"long haul Covid\" is associated with increased risk for blood pressure variability that might contribute to long term worse health outcomes.", "accessing_institution": "University of North Dakota" }, { "uid": "RP-41206C", "title": "Exploring associations between reported cannabinoid use and COVID-19 test results", "task_team": false, "dur_project_id": "DUR-B391246", "workspace_status": "CLOSED", "lead_investigator": "Thomas Best", "research_statement": "Preliminary analysis of single-site data indicates a potential negative association between reported cannabinoid use (e.g., epidiolex) and COVID-19 test results. This project will explore the potential for these associations to be substantial and significant in the multi-site N3C Data Enclave.", "accessing_institution": "University of Chicago" }, { "uid": "RP-34C578", "title": "COVID-19 Impact on Individuals with Disabilities and their Families ", "task_team": false, "dur_project_id": "DUR-B41B3E2", "workspace_status": "ACTIVE", "lead_investigator": "Lesley Cottrell", "research_statement": "The purpose of this request is to establish a group of experts who will examine the N3C data in terms of COVID's impact on individuals (of all ages) who have at least one disability. Disability types would include: intellectual, developmental, vision impairment, deaf or hard of hearing, mental health conditions, acquired brain injury, and mobility/physical disabilities. We anticipate disparities between individuals based on ability differences in terms of the prevalence of COVID, symptomology, and service utilization. Individuals with disabilities and their families have documented challenges to care and an increased vulnerability to infections and complications due to their disability and other medical conditions. With more than 35% of the nation's population reporting at least one disability, it is important to examine the impact of COVID-19 across the lifespan. Information garnered from this effort would potentially provide opportunities for public health, infectious disease, and other approaches. ", "accessing_institution": "West Virginia University" }, { "uid": "RP-739923", "title": "Reoccurrence of COVID infection and related long term impact", "task_team": false, "dur_project_id": "DUR-B47222C", "workspace_status": "ACTIVE", "lead_investigator": "Ran Dai", "research_statement": "With the progress of COVID pandemic, reoccurrence of COVID cases have been reported not only among people without symptoms or with mild symptoms, but also have occurred with patients who have been hospitalized, recovered with two consecutive negative tests and then infected again with severe symptoms. Understanding the mechanism behind the reoccurrence of COVID and the severity of the disease related to it is important for fighting COVID in the long term. We will develop novel competing risk models and machine learning algorithms to understand this question.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-C1BA2C", "title": "Increased incidence of JIA after COVID - feasibility & exploration", "task_team": false, "dur_project_id": "DUR-B49EC94", "workspace_status": "CLOSED", "lead_investigator": "Michael Wagner", "research_statement": "Our main interest lies in better understanding whether juveniles with COVID are at increased risk for Juvenile Idiopathic Arthritis. To familiarize ourselves with the N3C data enclave and tools, we request access to the synthetic data set, with the expectation that we will apply for access to the limited data set at a later time point and after we share the potential for analyses with our clinical collaborators.", "accessing_institution": "Cincinnati Children's Hospital Medical Center" }, { "uid": "RP-03714E", "title": "Relationship of the COVID-19 vaccine to myocarditis, other end-organ damage, and mortality in comparison to COVID-19 infection", "task_team": false, "dur_project_id": "DUR-B564513", "workspace_status": "ACTIVE", "lead_investigator": "Scott Chapman", "research_statement": " It has been scientifically established that severe COVID-19 infection may lead to the devastating outcome of acute respiratory distress syndrome (ARDS). Additional consequences of severe infection include end-organ damage such as myocarditis, acute renal failure and stroke. While these disease states undoubtedly occur in the COVID-negative population, the intense systemic inflammatory response to the virus is often implicated in the higher incidence of these outcomes in COVID-19 patients.\nEnd organ damage, such as myocardial, kidney, liver, and neurologic injuries are known complications associated with COVID-19 infection. Myocarditis has been a reported complication associated with mRNA vaccines for COVID-19. The aims of this study are to identify the odds of developing myocarditis, kidney injury, liver injury, stroke, mortality, or non-cardiac end-organ damage over time following COVID-19 infection and after receiving an mRNA COVID-19 vaccination. There will be further evaluation of the individuals who developed myocarditis post-COVID-19 infection or mRNA vaccination to identify possible pathophysiology of genetic mimicry between the vaccine and our cells by evaluating the relationship between COVID-19 severity and soluble angiotensin converting enzyme 2 (sACE2) concentrations.", "accessing_institution": "University of Minnesota" }, { "uid": "RP-88A42A", "title": "Quantifying and comparing Covid-19's latent effects on spatial populations across states.", "task_team": false, "dur_project_id": "DUR-B650AB3", "workspace_status": "CLOSED", "lead_investigator": "Rasim Musal", "research_statement": "Geo-Spatial analysis of Covid-19 mortality and morbidity has been lacking in USA. This study is to help fill a gap in spatio-temporal analysis where each unit area impacts other areas that it borders. This allows the accounting of latent variables. In addition to spatial effects, we also controlling for social determinants of health. This research proposes a Hierarchical Bayesian Model to incorporate the effects of social determinants of health in conjunction with spatial and temporal effects. This model would allow us not only to quantify effects of various variables of interest but also to identify areas that behave different than what is expected. N3C Limited Data Set is fundamental for this research as it has the health outcomes and three digit zip codes of the patients? address. ", "accessing_institution": "Texas State University" }, { "uid": "RP-5E7656", "title": "COVID-19 outcomes in rheumatic disease", "task_team": false, "dur_project_id": "DUR-B6B9501", "workspace_status": "CLOSED", "lead_investigator": "Tony Raymond Merriman", "research_statement": "COVID-19 results from a hyper-inflammatory response to SARS-Cov-2 infection. COVID-19 can lead to hospitalization, intensive care treatment and death. There is much interest in understanding risk factors for poor COVID-19 outcomes. For example it is well established that older age, ethnic minorities and obesity are risk factors. However the contribution of arthritis is less understood. Inflammatory arthritis is also characterized by a hyper-responsive immune response. Here we wish to test in the US National COVID Cohort Collaborative (N3C) if common inflammatory arthropathies, including rheumatoid arthritis, gout and lupus, are risk factors for diagnosis, hospitalization and death from COVID-19. We also wish to test if vaccination status modifies outcomes of COVID-19 in arthritis. We, and others, have studies in smaller cohorts suggesting that rheumatoid arthritis is a risk factor for death from COVID-19. We have recently provided evidence that gout is also a risk factor for death from COVID-19, with the effect concentrated in women. However these findings need replication in a larger dataset, i.e. N3C. This dataset would allow stratification, for example by sex and treatment. Testing in N3C would also allow an adequately powered analysis of the risk for poor COVID-19 outcomes in patients with gout and other less common arthropathies (e.g. lupus).", "accessing_institution": "University of Alabama at Birmingham" }, { "uid": "RP-7B67DE", "title": "Differential Clinical Outcomes for Drug Repurposing Candidates Discovered in BSL3 SARS-CoV-2 Infection Model", "task_team": false, "dur_project_id": "DUR-B795BCD", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Sexton", "research_statement": "The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. From a library of 1,441 FDA-approved compounds and clinical candidates, we identified 20 dose-responsive compounds with antiviral effects. This research seeks to determine if the FDA-approved drugs exhibiting direct antiviral efficacy against SARS-CoV-2 in vitro have any clinical benefit. ", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-E7B086", "title": "Evaluating CRP?s Role as a Predictor of Long COVID-19", "task_team": false, "dur_project_id": "DUR-B92760E", "workspace_status": "ACTIVE", "lead_investigator": "Frank Orlando", "research_statement": "Inflammation in the initial COVID-19 episode may be associated with the likelihood of developing long COVID. The goal of this study is to determine the relationship between systemic inflammation in COVID-19 hospitalized adults and symptomatic long COVID. Additional analyses will evaluate whether anti-inflammatory medications prescribed during the hospitalization or at discharge were associated with development of long COVID. Analyses will be adjusted for age, sex, race, and comorbidities. ", "accessing_institution": "University of Florida" }, { "uid": "RP-D345D8", "title": "Adolescence population cohort generation and analysis", "task_team": false, "dur_project_id": "DUR-BA016AB", "workspace_status": "ACTIVE", "lead_investigator": "Wei Chen", "research_statement": "This project aims to generate a adolescence population cohort with N3C synthetic data so that subsequent statistical analysis may be carried out to conduct advanced research and support regulatory decision making.", "accessing_institution": "Food and Drug Administration" }, { "uid": "RP-BB6B85", "title": "Acute Kidney Injury in Pediatric COVID-19 Patients", "task_team": false, "dur_project_id": "DUR-BBEC611", "workspace_status": "CLOSED", "lead_investigator": "ADAM DZIORNY", "research_statement": "Adult patients with COVID-19 have a high incidence of acute kidney injury (AKI). The cause of AKI is multifactorial and is hypothesized to include direct viral injury and inflammation of the kidneys, pre-existing conditions conferring a higher risk, and treatment sequelae such as nephrotoxic medications. The purpose of this study is to better characterize acute kidney injury in pediatric patients (< 18 years old) with and without COVID-19. Specifically we request Level 2 (de-identified) data to ask the following questions: 1) What is the incidence of AKI among pediatric patients, both with and without COVID-19; 2) What risk factors predict the development of AKI in pediatric COVID-19 patients; 3) What are the outcomes of patients with and without AKI; 4) What is the time-course of AKI resolution; and 5) How are treatments, including medication and technology (e.g. CRRT or ECMO) associated with AKI progression or resolution. These final three questions will stratify AKI by KDIGO severity criteria.", "accessing_institution": "University of Rochester" }, { "uid": "RP-2E3D3E", "title": "Long-COVID Risk Factors and Analysis Methods", "task_team": false, "dur_project_id": "DUR-BC625B1", "workspace_status": "ACTIVE", "lead_investigator": "Shawn O'Neil", "research_statement": "Long-COVID is a complex condition with many potential risk factors, including demographic features, pre-existing conditions, and acute-phase treatments or severity. In this study we will assess risk factors for developing Long COVID after a primary COVID-19 infection, using both traditional statistical and machine learning approaches, comparing the methods for accuracy and recall, feature importance metrics, and generalizability across data-contributing sites. Given the complex nature of Long-COVID, we anticipate that these will lend complementary insights and guide approaches to studying such poorly-understood health phenomena.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-660ED8", "title": "Access to COVID-19 therapeutics", "task_team": false, "dur_project_id": "DUR-BC91355", "workspace_status": "ACTIVE", "lead_investigator": "Julia Schaletzky", "research_statement": "Project aims to analyze access to COVID-19 therapeutics in the pandemic over time to see if disparities exist.", "accessing_institution": "University of California, Berkeley" }, { "uid": "RP-967A30", "title": "Predictive modeling of neuropathic sequelae post-COVID-19: Assessing the impact of age, sex, and race on peripheral and autonomic neuropathy", "task_team": false, "dur_project_id": "DUR-BD0CD7A", "workspace_status": "ACTIVE", "lead_investigator": "Alen Delic", "research_statement": "This research project aims to develop predictive models to identify early indicators of neuropathic sequelae in patients recovering from COVID-19. Utilizing machine learning techniques, we will analyze patient data from the N3C enclave to evaluate the impact of demographic factors such as age, sex, and race on the development of peripheral and autonomic neuropathy. By identifying patterns and risk factors associated with these neurological complications, our models will provide valuable insights into the early detection and management of neuropathic conditions in post-COVID-19 patients. The goal is to enhance clinical decision-making and improve long-term outcomes for patients experiencing neurological sequelae after recovering from COVID-19.", "accessing_institution": "Axle Informatics" }, { "uid": "RP-0A0045", "title": "Investigation of COVID-19 Health Outcomes", "task_team": false, "dur_project_id": "DUR-BE59C13", "workspace_status": "CLOSED", "lead_investigator": "John Tilford", "research_statement": "Investigators from the University of Arkansas seek to use the NC3 database to better understand health outcomes associated with COVID-19. The investigators propose three specific aims that could be addressed with the limited NC3 database that has both service dates and zip codes among other variables. The three aims include:\n1.\tDevelop predictive models among hospitalized patients with Covid-19 to predict poor outcomes (use of mechanical ventilation, protracted time to recovery, clinical complications, and mortality). \nHypothesis: Predictive models can be created to identify patients at-risk for poor outcomes over time and geographic regions.\n2.\tCompare the use of ventilation, time to recovery and mortality by race and ethnicity among persons diagnosed with Covid-19 focusing on specific treatments (baricitinib, tocilizumab, remdesivir, convalescent plasma, and dexamethasone) that might explain differences in outcomes by race and ethnicity. \nHypothesis: Hospital outcomes for the probability of requiring mechanical ventilation, hospital length of stay, and death within the hospital will differ by race and ethnicity among propensity score matched samples and these differences can be partially explained by differential access to care and therapies over geographic regions.\n3.\tEvaluate systems of care for hospitalized patients by documenting variation in hospital outcomes over time.\nHypothesis: Hospital outcomes will show significant variation over the study period and the need for ventilation and time to recovery will improve over the study period.\nThe aims will be completed by a multi-disciplinary study team in accordance with the data use agreement in place by UAMS. The investigators propose the use of propensity score matching in deductive analyses as a method to improve causality in addition to inductive approaches. All of the data confidentiality agreements will be strictly adhered to with no intention of downloading or linking the data. The findings will be useful for understanding whether specific practice patterns or other clinical factors are associated with improved outcomes.\n\n", "accessing_institution": "University of Arkansas for Medical Sciences" }, { "uid": "RP-209D71", "title": "Long COVID phenotypes", "task_team": false, "dur_project_id": "DUR-BEC0167", "workspace_status": "CLOSED", "lead_investigator": "Nusrat Epsi", "research_statement": "Work in progress", "accessing_institution": "The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc." }, { "uid": "RP-707AE2", "title": "Association between the Covid-19 Pandemic and Adolescent Emergent Behavioral Health Visits", "task_team": false, "dur_project_id": "DUR-CB7881D", "workspace_status": "ACTIVE", "lead_investigator": "David Bard", "research_statement": "Deaths by suicide are a leading cause of adolescent deaths nationally; presentations to the emergency room for suicidal ideation or attempts at self- harm through ingestions or other means are common. This project will expand existing research conducted at OU Health Sciences Center, where it was found that there has been a large increase since the beginning of the COVID-19 pandemic. We will review records using N3C data for patients aged 4-17 with diagnoses associated with suicide attempt or self harm and attempt to identify trends in overall rates within the context of pre-existing patterns, with special attention paid in the period of time after March 2020.", "accessing_institution": "University of Oklahoma Health Sciences Center" }, { "uid": "RP-606B41", "title": "COVID-19 impact on antimicrobial utilization and multi-drug resistant organism development", "task_team": false, "dur_project_id": "DUR-BED0D22", "workspace_status": "ACTIVE", "lead_investigator": "Lee Nguyen", "research_statement": "Antibiotics use causes antimicrobial resistance and result in over 3 million infections and 48,000 deaths annually. Multidrug resistant organisms (MDROs) are the most difficult to treat and has the highest risk for mortality. The rates of MDROs fluctuate over time and is related to antimicrobial consumption. The MDROs of most concern are carbapenem-resistant Acinetobacter baumannii and Enterobacterales, Clostridioides difficile, and extended-spectrum beta-lactamase (ESBL) producing Enterobacterales and in 2019, these MDROs caused nearly one-third of deaths in the hospital setting.(CDC 2019 Threat) In 2020, the severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) infection caused the coronavirus 2019 (COVID-19) pandemic in which hospitalization rates skyrocketed above hospital capacity and overwhelmed the healthcare system where antimicrobial stewardship standards of practice were loosened. In the United Kingdom, antimicrobial use was prescribed to 85% of the SARS-CoV-2 infected patients with only 21% of the patients having a significant positive microbiology result.(Russel, Lancet Microbe 2021) Within the US, antimicrobial prescribing trends is less clearly defined in SARS-CoV-2 patients and whether the prescribing trends will persist post COVID-19 pandemic. There is a critical need to determine the impact of the COVID-19 pandemic on antimicrobial utilization and MDRO acquisition in hospitalized patients. \nOur long-term objective is to exam healthcare policies and practices impacting effective antimicrobial utilization and preventing MDROs. Our short-term goal is to provide clinical evidence on antimicrobial utilization in the midst of a pandemic and to determine its impact on MDRO acquisition. Our hypothesis is that the COVID-19 pandemic changed the antimicrobial prescribing habits to favor broad-spectrum agents and increased the incidence of MDROs. This information is important as hospital survivors of the COVID-19 pandemic are potentially at increased risk for drug-resistant infections such as ESBL producing bacterial pneumonia or urinary tract infections. ", "accessing_institution": "University of California, Irvine" }, { "uid": "RP-E39D65", "title": "N3C Pregnancy Task Team: COVID-19 Incidence, Treatment, and Outcomes in Pregnant Women", "task_team": false, "dur_project_id": "DUR-C001548", "workspace_status": "ACTIVE", "lead_investigator": "ELAINE HILL", "research_statement": "This project seeks to understand associations between SARS-CoV-2 infection and treatment for COVID-19 with maternal and infant outcomes in the N3C cohort. We also seek to understand associations between maternal characteristics, including clinical and environmental factors, with COVID-19 severity among pregnant women. We will describe the incidence, timing, and severity of SARS-CoV-2 infection in pregnant women, as well as ascertain incidence rates differing between pregnant women vs. non-pregnant women of reproductive potential. Maternal outcomes include pregnancy-related complications (e.g., C-section delivery or use of intensive care unit [ICU]), maternal morbidity, and maternal mortality. Infant outcomes include birth outcomes (e.g., birth weight or preterm birth), infant complications, and use of neonatal ICU. If possible, we will assess the likelihood of vertical transmission of SARS-CoV-2 from mother to infant and healthcare utilization during pregnant and postpartum periods among pregnant women.", "accessing_institution": "University of Rochester" }, { "uid": "RP-D359DC", "title": "Linking clinical outcomes and SARS-CoV-2 variants using computational biology ", "task_team": false, "dur_project_id": "DUR-C09E653", "workspace_status": "CLOSED", "lead_investigator": "Tomasz Adamusiak", "research_statement": "There is a wealth of important questions about SARS-CoV-2 pathogenicity that currently remain unanswered. In particular, much remains to be learned about the viral genetic determinants of SARS-CoV-2 pathogenicity. The N3C dataset provides an immediate opportunity to address this knowledge gap rapidly.\nWe will use the N3C dataset to conduct a viral genome-wide association study (VWAS) for SARS-CoV-2 genomic regions associated with severe clinical outcomes. We will follow up on any novel significant SARS-CoV-2 SNP associations with protein-specific analysis to better understand and potentially explain the molecular mechanisms of the clinical impact. Those approaches may include predicting the effect of SNPs on protein folding free energy, using AI/ML to predict the impact of mutations on protein stability and antibody-antigen binding, as well as a mechanistic understanding of the impact on protein 3D structure using tools like AlphaFold.\nMarginal structural models (MSM) will be used to test for possible association between viral genomic sequence variants and severe COVID-19 outcomes while incorporating the full set of demographics, comorbidities, vaccination status, and other data for each patient.\n\nŠ2022 The MITRE Corporation. ALL RIGHTS RESERVED.\nApproved for Public Release; Distribution Unlimited. Public Release Case Number 22-2549.", "accessing_institution": "The MITRE Corporation" }, { "uid": "RP-CBB630", "title": "External validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19", "task_team": false, "dur_project_id": "DUR-C1D748A", "workspace_status": "CLOSED", "lead_investigator": "Charlotte Mann", "research_statement": "In a retrospective, observational study using data from hospitals in Michigan (Mi-COVID19), we developed a parsimonious model that achieved high discrimination to predict the risk of in-hospital mortality among hospitalized patients with COVID-19 (Mann et al., 2021). Our model includes the patient?s age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital?s COVID-19 mortality rate. While we were able to externally validate the model with hospitals in the Mi-COVID19 data registry used in this study, the data is primarily from the beginning of the COVID-19 pandemic (April-August 2020). Therefore, we would like to further validate the model on patients from hospitals outside of the state of Michigan and who were treated in hospitals after August 2020 using the de-identified (level 2) N3C dataset to determine the robustness of the model. Ultimately, we plan to use predictions from this model to improve precision in estimating treatment effects in randomized controlled trials studying COVID-19 treatments and vaccines.\n\nMann, C. Z., Abshire, C., Yost, M., Kaatz, S., Swaminathan, L., Flanders, S. A., Prescott, H. C., and Gagnon?Bartsch, J. A. (2021) ?Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19.? medRxiv, https://doi.org/10.1101/2021.05.04.21256599.", "accessing_institution": "University of Michigan?Ann Arbor" }, { "uid": "RP-43B17A", "title": "Developing an individual-level risk assessment algorithm for severe and long-term health outcomes due to COVID-19 in the United States", "task_team": false, "dur_project_id": "DUR-C21CC6B", "workspace_status": "ACTIVE", "lead_investigator": "Emma Pendl-Robinson", "research_statement": "With the vaccine roll-out and new variants emerging, one-size-fits-all guidance will not be sufficient to help the public navigate the ongoing pandemic. Multiple host and environmental factors contribute to differential susceptibility risks from COVID-19. Certain demographic groups (such as people of color or elderly), patients with certain comorbidities, and unvaccinated individuals face a higher risk of severe health outcomes due to COVID-19 infections. Risk assessment tools that are customized to individual conditions can help the public understand their risks and effective risk management strategies. We aim to use the N3C de-identified data to construct machine learning models predicting the probability of having severe COVID-19 health outcomes, such as hospitalization, intensive care unit admission, and deaths, using risk factors at the de-identified individual patient level. If possible, we will seek to identify risk factors for ?long COVID?, defined as having symptoms after 6 months of the initial infection. To evaluate the potential impact of different variants circulating in the US population, we will examine how the relationship between risk factors and severe COVID outcomes vary during the following phases: Jan - Dec 2020, Jan - June 2021, and after July 2021. We will evaluate the model performance through stratified cross-validation and use metrics including AUROC. The best-performing model will be implemented as part of an individual COVID-19 risk calculator, which is publicly available through an interactive web-based dashboard.", "accessing_institution": "Mathematica Inc." }, { "uid": "RP-4C0985", "title": "Identify important variables for molecular epidemiology of COVID-19 using multi-omics data and deep learning", "task_team": false, "dur_project_id": "DUR-C660406", "workspace_status": "CLOSED", "lead_investigator": "Gholamali Rahnavard", "research_statement": "The novel coronavirus disease (COVID-19) has changed modern human life, and we need to prepare a long-term plan for this infectious disease. Future treatment strategies (e.g., therapeutics, vaccines) require understanding how the body responds during the initial and subsequent treatments. Whether discrete omics features such as molecular level changes, host/viral genomic variants, differential gene expression will exhibit different clinical outcomes. Identifying epidemiological variables and patient characteristics and and omics features to be considered in studies with multi-omics to investigate molecular mechanisms of COVID-19 is an essential task to optimize cost and efforts. We hypothesize that the evolutionary and temporal dynamics of CoV-2 transmission during treatment will generate distinct genomic and biomarker signatures in association with epidemiological data. We will apply statistical and deep learning models to identify important measured omics data (e.g., viral genome, metabolites, proteins, host expression, and microbiome) in association to health outcomes. We use large-scale data from monitored communities such as vaccinated individuals to detect and decipher drug-resistance and, eventually, vaccine-resistance. We will also test seasonality patterns as the pandemic extends beyond one year. In addition our focus will be on pregnancy and cardiovascular domains data. We request Level 2 de-identified data from the N3C to associate patient characteristics and clinical variables with treatments and health outcomes.", "accessing_institution": "George Washington University" }, { "uid": "RP-8B7FDB", "title": "Risk Factors for Long-COVID in Inflammatory Bowel Disease Patients; Stratified by Disease Subtype and Current Medical Therapy", "task_team": false, "dur_project_id": "DUR-C750B3D", "workspace_status": "CLOSED", "lead_investigator": "Matthew Pelton", "research_statement": "There is limited data on the long-term outcomes and risk of long COVID in patients with Inflammatory Bowel Disease (IBD). Preliminary data from a single center study suggests that female gender is associated with increased risk of long COVID. Further data from a Denmark-based cohort found that within subtypes of IBD more severe COVID is associated with increased risk in patients with Crohn's Disease and discontinuation of immunosuppressive therapy is associated with increased risk in patients with Ulcerative Colitis. Both of these studies are limited by small n's, and may be underpowered to identify predictors of long COVID. This study aims to characterize predictors of long COVID in patients with IBD, with subgroup analyses of IBD subtypes and modes of medical management of disease (biologic, immunosuppresive, aminosalicylic acids) ", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-46150C", "title": "Efficacy of common COVID-19 treatments (Remdesevir, etc..): Analysis of short-term COVID-19 outcomes ", "task_team": false, "dur_project_id": "DUR-C7B817E", "workspace_status": "ACTIVE", "lead_investigator": "", "research_statement": "This project aims to evaluate the efficacy of common COVID-19 treatments, including Remdesivir, in improving short-term outcomes for COVID-19 patients. By analyzing data from the N3C Data Enclave, we will assess the impact of these treatments on clinical parameters such as viral load, ICU admission rates, and mortality. The study will focus on identifying the most effective treatment regimens and their associated outcomes, providing valuable insights for optimizing COVID-19 management strategies.\n", "accessing_institution": "login.gov" }, { "uid": "RP-5A73BA", "title": "NIH Long COVID Computational Challenge (L3C)", "task_team": false, "dur_project_id": "DUR-C84B759", "workspace_status": "CLOSED", "lead_investigator": "Akin Johnson", "research_statement": "The primary objective of the Challenge is to spur and reward the development of AI/ML models and algorithms that serve as open-source tools for using structured medical records to identify which patients infected with SARS-CoV-2 have a high likelihood of developing PASC/Long COVID.", "accessing_institution": "University of Illinois at Chicago" }, { "uid": "RP-64A958", "title": "PHASTR - Refreshable Dashboard Visualization", "task_team": false, "dur_project_id": "DUR-CAC8301", "workspace_status": "CLOSED", "lead_investigator": "STEVE MAKKAR", "research_statement": "This project is for copying and automatically refreshing PHASTR project code to be used to feed N3C dashboard ", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-BD23A5", "title": "Inflammation, thrombogenesis and myocardial injury and Covid-19 outcomes", "task_team": false, "dur_project_id": "DUR-CACC26A", "workspace_status": "ACTIVE", "lead_investigator": "Chang Liu", "research_statement": "To investigate the phenotype and outcomes in individuals with activation of one or more of the pathophysiologic pathways of inflammation, thrombosis, and myocardial injury during Covid-19 hospitalization. Sex and race differences will be assessed. Outcomes will adjust for baseline characteristics (demographics and clinical factors). In-hospital outcomes will include need for ICU admission, ventilation, cardiovascular events (MI, stroke, thrombotic events) and death. \n\nData requested: \nDemographics: Age, race, sex, date of admission, location.\nPast medical history: History of cardiovascular disease (heart disease, CAD, MI, stroke, PAD), renal, liver, COPD, other past medical history.\nRisk Factors: History of hypertension, diabetes, smoking, hypercholesterolemia, obesity (height, weight).\nMedications: medications at admission.\nLabs and other measurements during admission: Full blood count, Renal/liver function, pro-calcitonin, troponin, CRP, IL-6, D-dimer levels. \nOther: BP, HR, arrhythmias (Afib).\nMedications during admission: Use of anticoagulants, remdesevir, steroids, others (antibiotics), inotropes.\nDiagnoses during admission: Myocardial infarction, pulmonary embolus, other thrombotic event, stroke, arrhythmias (ventricular, supraventricular), SOFA score (if available). \nOutcomes: Echocardiogram reports; death/discharge to home or long-term care facility, ICU admission, ventilation (yes/no/duration).", "accessing_institution": "Emory University" }, { "uid": "RP-E5D34E", "title": "Alcohol Use and Respiratory Outcomes in COVID19 Infections", "task_team": false, "dur_project_id": "DUR-CAE0366", "workspace_status": "ACTIVE", "lead_investigator": "Alfred Anzalone", "research_statement": "Heavy alcohol use (>4 drinks/day for women and >5 drinks/day for men) is a known risk factor for bacterial pneumonia and the development of ARDS. Certain respiratory viruses such as RSV are also more severe in those that drink heavily. It is not currently known whether alcohol use increases the susceptibility to infection with SARS-CoV2 or worsens disease severity.", "accessing_institution": "University of Nebraska Medical Center" }, { "uid": "RP-EE2B58", "title": "Occurrence of neurological diseases in hospitalized patients with COVID-19 infection", "task_team": false, "dur_project_id": "DUR-CB3A15C", "workspace_status": "CLOSED", "lead_investigator": "Wei Huang", "research_statement": "Specific Aims:\n1. To determine the rates of neurological causes of death among patients diagnosed with COVID-19.\n2. To determine any differences in rates of neurological causes of death in various strata of patients.\nMethods:\nWe will use de-identified data to evaluate the prevalence of stroke (with subtypes), encephalopathy, encephalitis, meningitis, seizures, and neuropathy as primary or secondary causes of death. We will evaluate the prevalence of neurological causes of death in various age, gender, and race/ethnicity subgroups. We will compare the characteristics of patients with COVID-19 with neurological causes of death with those without neurological causes of death.", "accessing_institution": "University of Missouri" }, { "uid": "RP-B6ABA8", "title": "Leveraging deep learning to determine risk factors of COVID-19", "task_team": false, "dur_project_id": "DUR-CB87CA1", "workspace_status": "CLOSED", "lead_investigator": "SIRU LIU", "research_statement": "In this project, we propose to develop deep learning models to explore risk factors of COVID-19 and predict patient's outcomes after the diagnosis. The model could be used to develop a clinical decision support tool to improve healthcare quality. We are requiring to access the EHR data. We will use demographic features, lab data, medication, and features identified from notes to develop the predictive model. ", "accessing_institution": "University of Utah" }, { "uid": "RP-0ABD8E", "title": "Impacts of COVID -19 in Older Adults (Elder Impact Domain)", "task_team": false, "dur_project_id": "DUR-CC65464", "workspace_status": "CLOSED", "lead_investigator": "Soko Setoguchi Iwata", "research_statement": "While older adults represent 24% of overall infections in the US, almost 80% of COVID-related deaths occur in this age group. However, fewer studies have focused on vulnerable older adults especially in those with signifying characteristics of older adults including multi-morbidity, polypharmacy, and reduced cognitive and physical function. It is also not clear if overall excess risk in older adults is fully explained by known individual risk factors. We hypothesize that the older adult population with underlying comorbid conditions will have worse COVID-19-related outcomes following infection with SARS-CoV-2.\nUsing N3C data, we will conduct a series of epidemiologic studies to understand the impact of COVID-19 in older adults defined as age >= 65. Some questions include: 1)describe morbidity and mortality in older adults with COVID-19 including with multi-morbidity, polypharmacy, dementia, or those with functional limitations and how characteristics, management and outcome changed over time since March 2020 and current; 2) Compare COVID-19 among older adults to younger adults; 3) methodological studies to identify cognitive function, physical function, multimorbidity, and polypharmacy collaborating. We will design cohort studies using N3C data for descriptive and analytic studies and work with the NLP group on the methodologic studies. The results of the proposed studies will advance our understanding of impacts of COVID-19 in older adults and can be used by clinicians to protect and better manage older vulnerable adults.\n", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-4A9E27", "title": "[N3C Operational] Data Ingestion and Harmonization", "task_team": false, "dur_project_id": "DUR-D01CCD2", "workspace_status": "ACTIVE", "lead_investigator": "Christopher Chute", "research_statement": "N3C operations to extract, transform, and load data from contributing sites into a common format. Additionally, the team will contribute to the cross-workstream efforts of normalize data elements into a common form, including units of measure and outlier flagging.", "accessing_institution": "Johns Hopkins University" }, { "uid": "RP-582482", "title": "Characterization of COVID outcomes in rare disease patients", "task_team": false, "dur_project_id": "DUR-D276495", "workspace_status": "ACTIVE", "lead_investigator": "Melissa Haendel", "research_statement": "This project aims to determine if there is worsening of outcomes as a result of SARS-CoV-2 infection amongst rare disease patients as compared to the general patient population using the N3C De-Identified Data Set. While some rare diseases have been noted as conditions with greater risk of severe illness from SARS-CoV-2 infection, the listing is not exhaustive. With over 10,000 different rare diseases with a myriad of phenotypic presentations, rare disease patients represent a unique population for studying COVID outcomes. While collectively rare diseases are not ?rare,? each individual rare disease can consist of a relatively small patient population, making it easy to overlook the effect of rare diseases in favor of more prevalent conditions, such as cancer and heart disease. It is critical to study rare diseases in the context of the SARS-CoV-2 pandemic to ensure that these patients are not overlooked.\n\nIdentifying rare disease populations within the N3C enclave and analyzing their COVID outcomes as compared to the general population would provide important insights into the presentation, effectiveness of treatments, and significant outcomes of COVID in rare disease patients. In addition, pre-clustering of rare diseases by associated phenotypes or underlying pathophysiologic derangements may provide additional insights into the key features that could predict negative outcomes from COVID, from highly specific phenotypic features to general organ systems. Furthermore, determining the relative risk of long-COVID in rare disease patients compared to the general population could help elucidate underlying mechanisms of organ-specific long-COVID syndromes.", "accessing_institution": "University of Colorado Anschutz Medical Campus" }, { "uid": "RP-7272E3", "title": "Sequelae of repeat COVID-19 infection", "task_team": false, "dur_project_id": "DUR-D2974C7", "workspace_status": "CLOSED", "lead_investigator": "Benjamin Sines", "research_statement": "Recent research in a Veterans Affairs population has demonstrated increased risk of death, hospitalization and organ-specific morbidity in patients with repeat SARS-CoV-2 infection that persisted as far out as 6 months after the most recent infection. As SARS-CoV-2 continues to evolve and variants evade immunity from prior natural infection or vaccination, the prevalence of repeated infection is increasing. Though hospitalizations and daily mortality rates from the pandemic continue to decrease, this morbidity risk from reinfection is significant and poses as long-term burden to patients and the healthcare system. We propose to study the 6 month risk of hospitalization, mortality rates and multi-organ system specific symptoms and disease in those with recurrent infection as compared to those with a single COVID-19 infection.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-8C7C6D", "title": "Neurological disorders in COVID patients (non-stroke)", "task_team": false, "dur_project_id": "DUR-D39306B", "workspace_status": "CLOSED", "lead_investigator": "Ofer Sadan", "research_statement": "This project will describe non-stroke related neurological complications of COVID-19 patients and with an emphasis on critically ill COVID-19 patients as well as those who specifically require neurocritical care service. Initial focus will be placed on encephalopathy, delirium and autonomic dysfunction. The project will investigate risk factors and interventions which are correlated to delirium, or to its absence, as well as the correlation between delirium and outcomes in this patient population. ", "accessing_institution": "Emory University" }, { "uid": "RP-6DA6CD", "title": "Biomarker Mapping To Tests In N3C For Predictive Intervention of COVID-19 and Comorbidities.", "task_team": false, "dur_project_id": "DUR-D488983", "workspace_status": "CLOSED", "lead_investigator": "Raja Mazumder", "research_statement": "Research and development (R&D) aims include testing of machine learning (ML) based algorithms, for evaluating biomarkers in patients with COVID-19 and other co-morbidities. Such applications are initially developed using synthetic patient data developed by us and others and then tested for sensitivity and specificity with N3C data. Biomarker presence and measurement within N3C data will allow refinement of our algorithms and prediction of trends in the data, and patient outcome. Prior work involves collecting a list of COVID-19 biomarkers and mapping them to clinical codes (https://pubmed.ncbi.nlm.nih.gov/34015823/). \n", "accessing_institution": "George Washington University" }, { "uid": "RP-DCB797", "title": "Validate a Machine Learning Model to Predict Decompensation in Patients with COVID-19", "task_team": false, "dur_project_id": "DUR-D583056", "workspace_status": "CLOSED", "lead_investigator": "Mikhail Attaar", "research_statement": "The research question we seek to answer utilizing de-identified data is whether in patients diagnosed with COVID-19, are there any clinical or laboratory factors that can predict which patients will experience clinical deterioration. We define clinical deterioration as one of the following: 1) admission to the intensive care unit; 2) progression to requiring invasive mechanical ventilation; 3) failure of one or more organs; or 4) mortality. The factors we plan to investigate include patient demographics, comorbidities, medications and laboratory values. ", "accessing_institution": "University of Chicago" }, { "uid": "RP-49894A", "title": "[N3C Operational] FDA Connectivity Development and Validation of COVID-19 Use Cases Related to Publicly Available Devices, Procedures and Tests", "task_team": false, "dur_project_id": "DUR-D72550D", "workspace_status": "ACTIVE", "lead_investigator": "Samuel Michael", "research_statement": "Collaborative N3C and FDA operations to foster connectivity between the organizations. This will allow a small set of FDA contractors and staff access to the Enclave for the purpose of performing proof-of-concept studies. Studies will focus on validating N3C data completeness and suitability for FDA observational data analysis use-cases, with the goal of supporting evidence-based decision-making related to COVID-19 tests, devices and procedures.", "accessing_institution": "National Center for Advancing Translational Sciences" }, { "uid": "RP-A37C67", "title": "Long-Term COVID-19 Identification from EHR data", "task_team": false, "dur_project_id": "DUR-D78A161", "workspace_status": "CLOSED", "lead_investigator": "Wenqi Shi", "research_statement": "In this proposal, we aim to develop an AI-enabled clinical decision support system to facilitate the early detection of patients with long-term COVID-19 symptoms from clinical notes. Specially, we will (1) implement an interactive weakly supervision learning mechanism to facilitate evidence-based long-term COVID-19 patient annotation (Specific Aim 1); develop a deep learning algorithm to identify patients with long COVID-19 symptoms using nature language processing (Specific Aim 2); and (3) deploy a healthcare infrastructure with a user-friendly graphical interface for clinical practice and research (Specific Aim 3). ", "accessing_institution": "Georgia Institute of Technology" }, { "uid": "RP-2D9C77", "title": "Pediatric COVID-19 Severity Exploration", "task_team": false, "dur_project_id": "DUR-D934D71", "workspace_status": "CLOSED", "lead_investigator": "Marie Wax", "research_statement": "Assessment of Pediatric COVID-19 severity in EHR (Government institutions only)", "accessing_institution": "Biomedical Advanced Research and Development Authority" }, { "uid": "RP-504BA5", "title": "COVID-19 Treatments Associated with Lower Mortality ", "task_team": false, "dur_project_id": "DUR-D959D24", "workspace_status": "ACTIVE", "lead_investigator": "Sally Hodder", "research_statement": "There are no clear data on the best treatments for COVID-19 to minimize mortality in various populations. As an example, an NIH randomized clinical trial concluded that remdesivir was superior to placebo among hospitalized patients, significantly shortening time to recovery (10 vs. 15 days).1 Though mortality was significantly lower in the remdesivir group at day 15 (6.7% vs. 11.9%), mortality at day 29 trended lower (11.4% vs. 15.2%) but did not reach statistical significance. In contrast, a recent World Health Organization guidelines panel recommended against remdesivir use citing that there is currently no evidence that it improves survival. The WHO guideline was based on analysis of four clinical trials conducted among 7000 patients.2 This project utilizing the N3C database seeks to evaluate what therapies are associated with the lowest mortality among various populations.", "accessing_institution": "West Virginia University" }, { "uid": "RP-15879D", "title": "COVID-19 and Acute Pancreatitis", "task_team": false, "dur_project_id": "DUR-DBDAB47", "workspace_status": "ACTIVE", "lead_investigator": "Seyedeh Haleh Amirian", "research_statement": "Acute pancreatitis is the leading gastrointestinal cause of hospitalization in the United States. There have been reports of higher idiopathic acute pancreatitis (AP) in COVID-19-positive patients when compared to COVID-negative patients with AP (gallstone and alcohol as most common etiologies in this group). However, there is no consistent data on the association of acute pancreatitis with COVID-19.", "accessing_institution": "University of Miami" }, { "uid": "RP-1612C6", "title": "Risk factors for severe COVID-19 outcomes: a study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities (verification on N3C data)", "task_team": false, "dur_project_id": "DUR-DCA6C60", "workspace_status": "CLOSED", "lead_investigator": "Qi Wei", "research_statement": "Background \nCOVID-19 outcomes, in the context of immune-mediated inflammatory diseases (IMIDs), are incompletely understood. Reported outcomes vary considerably depending on the patient population studied. It is essential to interrogate a large population, while considering the effects of the pandemic time period, comorbidities, long term use of immunomodulatory medications (IMMs), vaccination status.\nMethods \nIn this retrospective case-control study, patients of all ages with IMIDs were identified from a large U.S. healthcare system (Providence health care system). COVID-19 infections were identified based on SARS-CoV-2 NAAT test results. Controls without IMIDs were selected from the same database. Outcomes include hospitalization, mechanical ventilation and death. We analysed data from March 1, 2020 - August 30, 2022, looking separately at both pre-Omicron and Omicron predominant periods. Factors including IMID diagnoses, comorbidities, long term use of IMM, and vaccination and booster status were analysed using multivariable logistic regression and extreme gradient boosting. We will validate our trained ML models in the N3C, which is an external, independent cohort.\nFindings \nOut of 2,167,656 patients tested for SARS-CoV-2, 169,993 had confirmed COVID-19 infection: 15,397 patients with IMIDs and 275,458 unmatched controls. Age and most chronic comorbidities were risk factors for worse outcomes, whereas vaccination and boosters were protective. Patients with IMIDs had higher rates of hospitalisation and mortality compared with controls. However, few IMIDs showed risk for worse outcomes, and unexpectedly, both spondyloarthritis and psoriasis were predictive of better outcomes. Most IMMs had no significant impact. Gradient boosting outperformed logistic regression, achieving AUROC 0ˇ77 - 0ˇ92. \nInterpretation \nFor patients with IMIDs, as for controls, age and comorbidities are risk factors for worse COVID-19 outcomes, whereas vaccinations are protective. Most IMIDs and immunomodulatory therapies were not associated with more severe outcomes, and both psoriasis and spondylarthritis were associated with reduced risk. These results can help inform clinical, policy and research decisions for patients with IMIDs during the ongoing pandemic.\nFunding \nNIH, Pfizer, Novartis, Janssen.\n", "accessing_institution": "ISB Science" }, { "uid": "RP-95B144", "title": "Factors that influence treatment response and outcomes in COVID-19 patients", "task_team": false, "dur_project_id": "DUR-DCE2BE2", "workspace_status": "CLOSED", "lead_investigator": "Viktorija Zaksas", "research_statement": "In this project we explore factors that may influence treatment response and outcomes in COVID-19 patients. Factors that will be evaluated include comorbidities, pre-COVID-19 prescribed medicines, treatment course during COVID-19, social and environmental factors. Our goal is to determine factors that might positively or negatively influence the patients? outcomes.", "accessing_institution": "University of Chicago" }, { "uid": "RP-6CB042", "title": "Collaborative Artificial Intelligence Research to Support National COVID-19 Response", "task_team": false, "dur_project_id": "DUR-DCEC1D1", "workspace_status": "ACTIVE", "lead_investigator": "Mary Pat Couig", "research_statement": "How can Artificial Intelligence-derived phenotypic risk classification and transition probabilities directly inform local, tribal, state and national response efforts to accurately describe the population in New Mexico with COVID-19 in comparison to other majority-minority states and the nation?\n\nThe research team plans on using the Limited Data Set (Level 3) of health data on a range of patients (age and different ethnic and racial identification), with diseases and co-morbidities that are known to result in worse outcomes due to the Coronavirus. Examples include heart disease, obesity, kidney disease, lung disease and belonging to a racial or ethnic group other than Caucasian, especially African-American, Hispanic and Native American/American Indian. The data will be analyzed and trends identified?looking for previously unidentified potential comorbidities and protective conditions related to severe COVID-19 symptoms and describing the characteristics of New Mexicans and the characteristics of populations in the other 5 majority-minority states, Hawaii, California, Texas, Nevada, and Maryland and the United States.\n\nThe plan also includes reviewing potential comorbidities with respect to identifying locations to preposition personal protective and other equipment, and/or sites that would need local support or transportation services. The team plans to publish the results of the data analyses and recommendations to improve service delivery, surge capacity and other-related recommendations for responding to the pandemic.\n\n", "accessing_institution": "University of New Mexico" }, { "uid": "RP-096EEC", "title": "Post-recovery COVID and incident heart failure in the National COVID Cohort Collaborative (N3C) study ", "task_team": false, "dur_project_id": "DUR-E65583A", "workspace_status": "CLOSED", "lead_investigator": "Melissa Caughey", "research_statement": "Patients recovered from coronavirus disease 2019 (COVID-19) develop long-term complications that significantly affect their quality of life and increase their risk of premature death. Despite lack of significant lung damage or worsening systolic function in many recovered COVID-19 patients, dyspnea and fatigue are commonly reported. Due to the shared pathophysiological mechanisms between COVID-19 and heart failure with preserved ejection fraction (HFpEF), COVID-19 may be a risk factor for incident HFpEF. In this study, we will analyze the risk of new onset HFpEF associated with COVID-19 infection, using data sourced by the National COVID Cohort Collaborative (N3C) study. This will be the first study to provide data-driven evidence regarding the association between COVID-19 and incident HFpEF. Our investigation will be led by a multidisciplinary team with expertise in epidemiology, heart failure care, and internal medicine, appointed at the University of North Carolina, Duke University, and the University of Arkansas for Medical Sciences. Other investigators are expected and welcome to join this project.", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-7F11C2", "title": "Competing Risk Analysis of Corticosteroid Usage in COVID-19 ICU Patients: An N3C Study", "task_team": false, "dur_project_id": "DUR-DD91361", "workspace_status": "ACTIVE", "lead_investigator": "Christopher Grubb", "research_statement": "COVID-19 has been shown to provoke a deleterious immune overactivation, which leads to severe hypoxia requiring advanced respiratory support modalities, multi-system organ failure, and death. The use of corticosteroids to dampen the immune response has been shown to reduce mortality in COVID-19 patients requiring supplemental oxygen; however, the associated risk of developing superimposed, hospital-acquired infections (HAI) that can lead to septic shock, especially in high-HAI risk settings like the intensive care unit (ICU), remains unclear. This study proposes to elucidate patient profiles of those most at risk for developing HAI and subsequent septic shock in the presence of varying durations of corticosteroid therapy in critically ill COVID-19 patients. Using L2 data, we will identify COVID-19 patients admitted to the ICU who did not acquire an HAI before ICU admission, a multi-state competing risk paradigm will be used to model corticosteroid impact on various disease progression pathways within the ICU. The primary outcome will be mortality, with secondary events of HAI and septic shock. We expect: to identify patient-specific variables associated with longitudinal progressions through the various severe COVID-19 trajectories; and that the duration of corticosteroid usage achieves an optimal point, after which the risks of secondary events outweigh the therapeutic benefits. This will be a significant step towards tailoring future therapeutic approaches.", "accessing_institution": "Virginia Tech" }, { "uid": "RP-C80011", "title": "Convalescent Plasma Administration Effect on COVID-19", "task_team": false, "dur_project_id": "DUR-DD96365", "workspace_status": "CLOSED", "lead_investigator": "Joy Alamgir", "research_statement": "The purpose of this research is to assess the effect of Convalescent Plasma (CP) administration on COVID+ patients against 3 specific clinical endpoints. The endpoints are: death, time to recovery and organ failure. This is an important research as, despite spotty high quality trails data, thousands of patients have received CP as part of the National Expanded Access Program (EAP) for convalescent plasma initiated in early April 2020 and under a subsequent Emergency Use Authorization (EUA) initiated in August 2020 for COVID. This study aims to either confirm or invalidate clinical endpoint effectiveness of such Convalescent Plasma administration using N3C data. As of the writing of this DUR there are around 4600 patients that have plasma administration records (both non-convalescent and convalescent plasma) with N3C. As this number seems lower than our expectation we plan to work with N3C data ingestion team to see if there could be data ingestion improvements to get a higher N for the analysis.", "accessing_institution": "ARI Science" }, { "uid": "RP-489657", "title": "Antithrombotic therapies in COVID-19", "task_team": false, "dur_project_id": "DUR-E0A9348", "workspace_status": "CLOSED", "lead_investigator": "Jonathan Chow", "research_statement": "In a post-pandemic world, it may be neither practical nor feasible to perform placebo-controlled RCTs. Causal effects analysis is able to reduce confounding of observational data, and similar to an RCT, may able to estimate the average causal effect of an intervention. By harnessing the tremendous volume of observations in the NC3 collaborative, we believe that we can answer some of these questions. ", "accessing_institution": "George Washington University" }, { "uid": "RP-A5379B", "title": "Developing Analytics Models to Understand Long-Covid in Pediatric Patients", "task_team": false, "dur_project_id": "DUR-E27E1AA", "workspace_status": "CLOSED", "lead_investigator": "Ashish Gupta", "research_statement": "The pediatric area has been attracting a lot of attention due to ?COVID long-haulers? or sufferers of ?long COVID?. During the early COVID phases when the pandemic was declared, children and young people (0-18years old) appeared to be resilient and invulnerable compared with other groups. However, recent medical records gathered from pediatric patients have shown long-Covid symptoms such as bran fog, headaches, dizziness, and shortness of breath, among others. There is a significant paucity of research on understanding the long-Covid symptoms in children and understanding who is likely to have propensity for developing long Covid symptoms. The purpose of this proposed research project is to establish relationships of COVID-19 with other diseases both in terms of pre-COVID-19 and post-COVID-19 among pediatric patients. The project will further analyze temporal relationships of treatment/diseases once a pediatric patient is infected with COVID-19 in order to study long-COVID and its effects. \n", "accessing_institution": "Auburn University" }, { "uid": "RP-200A0C", "title": "Association between COVID-19 Infection, Electrolyte Abnormalities, and Incident Arrhythmias", "task_team": false, "dur_project_id": "DUR-E45446D", "workspace_status": "CLOSED", "lead_investigator": "Aayush Visaria", "research_statement": "There are several reports of incident ventricular and atrial arrhythmias in hospitalized COVID-19 patients. COVID-19 patients also have high prevalence of electrolyte abnormalities, including hypokalemia and hypocalcemia. These electrolyte abnormalities are speculated to be, in part, due to gastrointestinal losses, activation of the renin-angiotensin-aldosterone system, and other metabolic effects associated with fever. The purpose of this study is to better understand the causes and arrhythmogenic effects of electrolyte abnormalities. We have three specific aims: Aim #1: to determine whether potassium, magnesium, and calcium abnormalities are more prevalent in patients with COVID-19; Aim #2: to determine whether the risk of arrhythmias is higher in COVID-19 infected patients; Aim #3: to understand the predictors on initial presentation to the hospital that increase risk of arrhythmia development. Other questions we will answer include: If electrolyte abnormalities are in fact associated with ventricular and atrial arrhythmias, does pharmacological correction of electrolyte abnormalities reduce risk of arrhythmia formation? Are there subclinical EKG findings associated with COVID-19 infection? This study will be Data Access Level 2.", "accessing_institution": "Rutgers, The State University of New Jersey" }, { "uid": "RP-16EDD2", "title": "EHR-based knowledge discovery for COVID-19 diagnosis and treatment", "task_team": false, "dur_project_id": "DUR-E5A4B3C", "workspace_status": "ACTIVE", "lead_investigator": "Tru Cao", "research_statement": "The aim of this project is to apply existing, and develop new, NLP, statistical, and machine learning methods to learn COVID-19 symptoms, complications, and treatments from N3C given very large EHR corpus. Specific research questions include: (1) Longitudinal and geographical studies of COVID-19 trends and hotspots; (2) Characterization of COVID-19 symptoms and complications; (3) Interaction between COVID-19 and pre-existing health conditions; and (4) Effective and ineffective treatment regimes for COVID-19 patients, with and without risky pre-existing health conditions. The requested data is the Limited Data Set (LDS).", "accessing_institution": "The University of Texas Health Science Center at Houston" }, { "uid": "RP-13C76A", "title": "Changes in body weight and body mass index in Hispanic adults during the COVID-19 pandemic using N3C data: a longitudinal study", "task_team": false, "dur_project_id": "DUR-E705B9A", "workspace_status": "ACTIVE", "lead_investigator": "Kai Guo", "research_statement": "Obesity is a major public health issue and associated with a wide range of chronic diseases. The situation is more severe among Hispanics. COVID-19 may even exacerbate this situation. My study is a retrospective cohort study for Hispanic adults based on data obtained from the N3C database, one of the largest collections of secure clinical data on COVID-19 research in the United States, including demographic and clinical characteristics of participants. Hence, the large cohort study controls for demographic and behavioral factors will delineate the role of COVID-19 pandemic on weight gain for Hispanic adults. The specific aim is to assess the impact of the COVID-19 pandemic on weight and body mass index (BMI) in Hispanic adults. The results of the study will provide insight into the impact of COVID-19 on body weight and population health, particularly in Hispanics who lived in United States during the pandemic; will help us to further explore weight-related lifestyle factors such as changes in dietary choices and physical activity levels during the pandemic; and will provide guidance to generate more highly adaptive strategies and their effective implementation to body weight control.", "accessing_institution": "University of Puerto Rico" }, { "uid": "RP-DCEB2A", "title": "Predictive modeling of COVID related outcome through fair and explainable AI", "task_team": false, "dur_project_id": "DUR-E81C778", "workspace_status": "ACTIVE", "lead_investigator": "Feifan Liu", "research_statement": "We aim to explore machine learning techniques to predict COVID related outcomes, including longCOVID, hospitalization, mortality, socioeconomic impact, etc. We will see how the model perform fairly and explore strategies to mitigate potential bias. ", "accessing_institution": "University of Massachusetts Medical School" }, { "uid": "RP-9E53BD", "title": "Utility of Cardiac Biomarkers for the Prognostications in Patients with COVID-19 infection", "task_team": false, "dur_project_id": "DUR-E8AC8BB", "workspace_status": "CLOSED", "lead_investigator": "Mohammed Osman", "research_statement": "Aim:\nThe purpose of this study is to study the outcomes of COVID-19 infection in patients with elevated cardiac biomarkers (D-Dimer, BNP, and Troponin)\n\nMethods:\nUsing ICD-10 codes we will identify all patients over the age of 18 years with COVID-19 infection. The identified cohort will then be further stratified into two groups based on the presence or absence of elevated Cardiac Biomarkers. Patients with COVID-19 infection and normal cardiac biomarkers will serve as the control population. \n\nStatistical plan: To be determined\n\nOutcomes:\n1.\tThe primary outcome will be to study in-hospital and short term (30 days) all-cause mortality \n2.\tSecondary outcomes include need for mechanical circulatory support devices, new-onset arrhythmias, need for ventilator support, cardiovascular mortality, re-admission rates, total length of stay, and hospitalization cost\n\n", "accessing_institution": "West Virginia University" }, { "uid": "RP-64CD77", "title": "Impact and outcomes from COVID-19 disease and vaccine in an evolving pandemic.", "task_team": false, "dur_project_id": "DUR-EA9B7CA", "workspace_status": "ACTIVE", "lead_investigator": "Amy Olex", "research_statement": "At this point in the pandemic there are clear regional variations across the US and over time, in terms of frequency of infection, access to vaccination, vaccine uptake, and mortality. This may be partially due to local public health policies but also may be related to the ability of the population to access health care. This project aims to better understand how patient risk for infection and severity of outcomes of infection change based on region, over time as the pandemic evolves, and by vaccination status. In addition, the long-term effects of COVID-19 are still mostly unknown, so there is an urgent need to use the data captured within the N3C to better characterize the long-term health impacts of this pandemic. We aim to explore more specifically the relationship of patient location as well as specific event timing to assess infection frequency and severity of COVID-19 over time and region. Research questions will include, but are not limited to: 1) How does risk for infection and severity of outcomes of infection change based on geographical location and/or overtime? 2) Does vaccination status affect risk for infection and severity of outcomes? 3) Does risk for infection or severity of outcome change with respect to the evolving dominant strains of SARS-CoV2 identified in the nation or specific regions? 4) What are the implications of long-term outcomes, including long-COVID, with respect to duration and severity of those outcomes? This research will aid patients and their care providers to adjust their response to this evolving pandemic and ensure or improve positive health outcomes.", "accessing_institution": "Virginia Commonwealth University" }, { "uid": "RP-310675", "title": "Using N3C to Apply Pharmacoepidemiologic Methods to Answer Questions about COVID-19 Treatment", "task_team": false, "dur_project_id": "DUR-EC14CC5", "workspace_status": "CLOSED", "lead_investigator": "Jessica Young", "research_statement": "As the coronavirus disease 2019 (COVID-19) continues to spread globally, researchers are urgently trying to find safe and effective treatment options for this novel disease. The National COVID Cohort Collaborative (N3C) offers a centralized repository of electronic health records (EHR) from over 60 sites, including over 1.5 million patients that can be leveraged to study potential treatments for COVID-19. \nThe N3C enclave is an invaluable tool enabling timely and important research to help combat the COVID-19 epidemic. Assessments of treatment options using these data can inform critical public health questions and impact treatment choices in critically ill patients. In this time of increased urgency to assess treatment options, it will be important that researchers think critically about best practices using these data.\nEHR data have long been used for retrospective observational research evaluating treatment options. These data require specialized study designs and analytic techniques to avoid well-understood biases. We aim to use the N3C data to demonstrate best practices in using these retrospective data, and to illustrate how failure to use these best methods can result in biased study results. \n", "accessing_institution": "University of North Carolina at Chapel Hill" }, { "uid": "RP-0BDD0E", "title": "The impact of the COVID-19 pandemic on weight-gain trends in the US", "task_team": false, "dur_project_id": "DUR-EE01EA2", "workspace_status": "ACTIVE", "lead_investigator": "Rahmatollah Beheshti", "research_statement": "In this project, we aim to study the role of the COVID-19 pandemic on the weight-gain trends in the US. Informed by the existing studies, our hypothesis is that the pandemic had a meaningful adverse impact on the already alarming weight-gain trends. We plan to investigate these trends separately in children and adult populations. While trying to identify such trends at a large scale and on the individual level, we also aim to study the disparities with respect to COVID-19 status, age, gender, race, and social determinant of health (SDOH).", "accessing_institution": "University of Delaware" }, { "uid": "RP-0BB283", "title": "Profile of COVID patients treated with oral antivirals", "task_team": false, "dur_project_id": "DUR-EE3C20B", "workspace_status": "CLOSED", "lead_investigator": "Bin Cai", "research_statement": "This project is to understand the COVID-19 hospitalization rate and associated risk factors such as patients characteristics, underlying conditions, type and time of antiviral treatments, and COVID variants", "accessing_institution": "Shionogi Inc" }, { "uid": "RP-7D14B0", "title": "[N3C Operational] Collaborative Analytics", "task_team": false, "dur_project_id": "DUR-E28778D", "workspace_status": "CLOSED", "lead_investigator": "Samuel Michael", "research_statement": "The [N3C Operational] Collaborative Analytics data user request allows a small set of N3C staff and community members, like common data model subject matter experts, medical specialist, informaticians, otologists, etc. access to the N3C enclave data for the purpose of preparing, cleaning, surfacing data , harmonization data for research. To become a member of the [N3C Operational] individuals must apply using the DUR process where attestation to follow the data user agreement, code of conduct, security training and human subjects training are required. ", "accessing_institution": "National Center for Advancing Translational Sciences" } ]