Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Microparticles (MPs), membrane fragments of 0.1–1.0 μm, are derived from many cell types in response to systemic inflammation. Acute liver failure (ALF) is a prototypical syndrome of systemic inflammatory response syndrome (SIRS) associated with a procoagulant state. We hypothesized that patients with ALF develop increased procoagulant MPs in proportion to the severity of systemic complications and adverse outcome. Fifty patients with acute liver injury (ALI), 78% of whom also had hepatic encephalopathy (HE; ALF), were followed until day 21 after admission. MPs were characterized by Invitrox Sizing, Antigen Detection and Enumeration, a light-scattering technology that can enumerate MPs as small as 0.15 μm, and by flow cytometry. Procoagulant activity was assessed by a functional MP-tissue factor (MP-TF) assay. Sixteen patients (32%) died and 27 (54%) recovered without liver transplantation (LT). Total MPs (0.15–1.0 μm) were present in nearly 19-fold higher concentrations in ALI/ALF patients, compared to healthy controls (P < 0.0001). MP-TF assays revealed high procoagulant activity (9.05 ± 8.82 versus 0.24 ± 0.14 pg/mL in controls; P = 0.0008). MP concentrations (0.28–0.64 μm) were higher in patients with the SIRS and high-grade HE, and MPs in the 0.36–0.64-μm size range increased in direct proportion to SIRS severity (P < 0.001) and grade of HE (P < 0.002). Day 1 MPs (0.28–0.64 μm) correlated with laboratory predictors of death/LT (higher phosphate and creatinine; lower bicarbonate), and day 1 and 3 MPs were higher in patients who died or underwent LT, compared to spontaneous survivors (P ≤ 0.01). By flow cytometry, 87% of patients had circulating CD41+ MPs, indicating platelet origin. Conclusion: Highly procoagulant MPs of specific size ranges are associated with the SIRS, systemic complications, and adverse outcome of ALI/ALF. MPs may contribute to the multiorgan system failure and high mortality of ALF.

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