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| Consortium for Clinical Characterization of COVID-19 by EHRs | |
|---|---|
| Name | Consortium for Clinical Characterization of COVID-19 by EHRs |
| Formation | 2020 |
| Type | Research consortium |
| Purpose | Clinical characterization of COVID-19 using electronic health records |
| Region served | International |
| Headquarters | Multi-center |
| Languages | English |
Consortium for Clinical Characterization of COVID-19 by EHRs is an international research consortium formed in 2020 to analyze electronic health records for clinical insights into COVID-19. The consortium brought together academic medical centers, healthcare systems, research institutes, and public health agencies to aggregate and harmonize clinical data. Through multicenter analyses, it aimed to inform clinical care, support epidemiologic surveillance, and accelerate translational research during the SARS-CoV-2 pandemic.
The consortium emerged amid the 2019–20 COVID-19 pandemic following calls from leaders at World Health Organization, Centers for Disease Control and Prevention, National Institutes of Health, and academic centers such as Harvard Medical School and University of Oxford for large-scale, interoperable clinical data. Early collaborators included investigators from Mount Sinai Health System, Massachusetts General Hospital, Johns Hopkins Hospital, and University of Pennsylvania Health System, who sought to leverage Electronic health record systems from vendors like Epic Systems and Cerner Corporation. Its formation paralleled initiatives such as National COVID Cohort Collaborative, ISARIC, and WHO Solidarity Trial to coordinate data-driven responses to SARS-CoV-2 and COVID-19.
Primary objectives included rapid characterization of clinical phenotypes, risk factors, comorbidities, and outcomes associated with COVID-19 across diverse populations. The scope encompassed inpatient and outpatient EHR data from institutions in North America, Europe, and Asia, enabling comparative analyses across sites such as University College London Hospitals NHS Foundation Trust, Karolinska University Hospital, and Peking Union Medical College Hospital. Secondary goals addressed treatment patterns, laboratory trajectories, and prognostic modeling to inform decision-making at institutions like Cedars-Sinai Medical Center and University of California, San Francisco.
Membership spanned academic health centers, research consortia, and biomedical informatics groups including Stanford University School of Medicine, Yale School of Medicine, University of Chicago Medicine, University of Toronto, and Max Planck Society affiliates. The consortium established working groups for clinical phenotyping, informatics, biostatistics, and governance, drawing participants from Broad Institute, Wellcome Trust, European Commission, and national ministries such as UK Department of Health and Social Care. Leadership roles rotated among principal investigators affiliated with institutions like Columbia University Irving Medical Center and Imperial College London.
Data sources included inpatient claims, laboratory systems, medication administration records, and imaging reports extracted from EHR platforms used by partners such as Mayo Clinic, Northwell Health, and Kaiser Permanente. Harmonization employed common data models exemplified by Observational Medical Outcomes Partnership and terminologies including ICD-10, LOINC, and SNOMED CT. Statistical methods integrated survival analysis, mixed-effects modeling, and machine learning frameworks developed with tools from TensorFlow, PyTorch, and platforms like SAS Institute and R (programming language). Quality control drew on standards from Clinical Data Interchange Standards Consortium and reproducibility best practices endorsed by Nature (journal) and The Lancet.
Consortium analyses produced multicenter reports on risk factors such as age, sex, and comorbidities including Diabetes mellitus, Hypertension, Chronic obstructive pulmonary disease, and Chronic kidney disease, and documented laboratory predictors like elevated D-dimer and lymphopenia. Publications appeared in journals including The New England Journal of Medicine, The Lancet Respiratory Medicine, JAMA, and Nature Medicine. Findings influenced clinical guidance from European Centre for Disease Prevention and Control and informed modeling studies by groups at Imperial College London and Johns Hopkins University. The consortium also contributed datasets and analytic code to repositories associated with GitHub and preprint servers such as medRxiv.
Governance frameworks combined institutional review board review at sites like University of Michigan and McGill University Health Centre with data use agreements and federated analysis models used by OHDSI and PCORnet. Ethical oversight referenced declarations and guidelines from Declaration of Helsinki, Belmont Report, and data protection laws such as General Data Protection Regulation and national statutes administered by agencies including European Data Protection Board and Office for Civil Rights (United States Department of Health and Human Services). Privacy-preserving methods included de-identification, distributed queries, and secure enclaves analogous to systems at Amazon Web Services and Google Cloud Platform.
The consortium fostered collaborations with public health bodies like Public Health England, research funders such as Wellcome Trust and Bill & Melinda Gates Foundation, and industry partners including Roche and Siemens Healthineers. Its outputs supported clinical pathways at hospitals including Houston Methodist Hospital and informed vaccine rollout considerations by agencies like European Medicines Agency and Food and Drug Administration. Long-term impacts include strengthened EHR research networks, enhanced data interoperability across organizations like Health Level Seven International, and precedent for rapid multinational clinical characterization during emerging infectious disease events such as Ebola virus epidemic and future pandemics.
Category:COVID-19 research organizations