N3C Domain Teams enable researchers with shared interests to analyze data within the N3C Data Enclave and collaborate more efficiently in a team science environment. Project Teams are groups exploring targeted research questions using the N3C Data Enclave.
Institutional Collaboration Map
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Collaboration Networks
The Acute Kidney Injury (AKI) Clinical Domain Team aims to investigate risk factors associated with kidney injury and recovery, as well as use of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs) in relation to COVID-19. Created: 2020-10-01 Learn More |
The Applicable Data Methods & Standards (ADM&S) Domain Team aims to explore which data methods & standards are applicable to the range of questions researchers will pursue within N3C. Cross CuttingCreated: 2020-10-02 Learn More |
The Cardiothoracic and Vascular Surgery Domain Team seeks to establish risk factors for morbidity and mortality in COVID-19 patients who underwent cardiothoracic surgical procedures. Created: 2021-05-12 Learn More |
The mission of the Cardiovascular Disease Clinical Domain Team is to better understand the effects of COVID-19 on patients with new and existing cardiovascular disease. Created: 2020-11-25 Learn More |
The goal of this Domain Team is to study the impact of vaccinations for combating the COVID-19 pandemic. Created: 2021-02-12 Learn More |
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.Lead Investigator: Marie Lluberes Accessing Institution: University of Puerto Rico ID: RP-E7FAAD (DUR-53C4B69) |
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.
This 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.
Lead Investigator: Marie Lluberes Accessing Institution: University of Puerto Rico ID: RP-74F392 (DUR-917CCEA) |
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. Lead Investigator: Tzu Chun Chu Accessing Institution: University of Georgia ID: RP-496D5C (DUR-B5A3B39) |
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.Lead Investigator: Md Osman Gani Accessing Institution: University of Maryland, Baltimore County ID: RP-2DA60E (DUR-140E37D) |
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.
The 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.
Lead Investigator: Mahbubur Rahman Accessing Institution: Data-Automata LLC ID: RP-E0D331 (DUR-3BCBC8D) |
Date: 2025/2/1 Authors: Narmeen Abd El Qadir, Harrison N Jones, David A Leiman ...Journal of Clinical Anesthesia https://doi.org/10.1016/j.jclinane.2024.111688 |
Date: 2025/2 Authors: Nicholas R Nelson, Nicholas Farina, Denise H Rhoney ...Clinical and Translational Discovery https://doi.org/10.1002/ctd2.70027 |
Date: 2025/1/6 Authors: Md Mozaharul Mottalib, Thao-Ly T Phan, Carolyn T Bramante ...Childhood Obesity https://doi.org/10.1089/chi.2024.0256 |
Date: 2025/1/25 Authors: Beshoy Gabriel, Henry Hoang, En Chang ...Journal of Orthopaedic Reports https://doi.org/10.1016/j.jorep.2025.100551 |
Date: 2025/1/23 Authors: A Jerrod Anzalone, Carol R Geary, Ran Dai ...BMC Health Services Research https://doi.org/10.1186/s12913-024-12168-5 |
This work is supported by the National Institutes of Health's Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Grant number 3R01HD105939-01S1 and the National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.