The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Abstract

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.

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

“…OHDSI held a COVID-19 studyathon in March 2020, during which this study was initiated. To note, the OMOP CDM has also been used to facilitate network COVID-19 studies by the 4CE consortium, in which trajectories of laboratory test measurements among COVID-19 patients were described 23 , and is being used by the N3C consortium to help harmonise EHR data on COVID-19 patients 24 .…”

“…This project has received support from the European Health Data and Evidence Network Edward Burn 1,2,51 , Seng Chan You 3,51 , Anthony G. Sena 4,5 , Kristin Kostka 6 , Hamed Abedtash 7 , Maria Tereza F. Abrahão 8 , Amanda Alberga 9 , Heba Alghoul 10 , Osaid Alser 11 , Thamir M. Alshammari 12 , Maria Aragon 1 , Carlos Areia 13 , Juan M. Banda 14 , Jaehyeong Cho 3 , Aedin C. Culhane 15 , Alexander Davydov 16,17 , Frank J. DeFalco 4 , Talita Duarte-Salles 1 , Scott DuVall 18,19 , Thomas Falconer 20 , Sergio Fernandez-Bertolin 1 , Weihua Gao 21 , Asieh Golozar 22,23 , Jill Hardin 4 , George Hripcsak 20,24 , Vojtech Huser 25 , Hokyun Jeon 26 , Yonghua Jing 21 , Chi Young Jung 27 , Benjamin Skov Kaas-Hansen 28,29 , Denys Kaduk 16,30 , Seamus Kent 31 , Yeesuk Kim 32 , Spyros Kolovos 33 , Jennifer C. E. Lane 33 , Hyejin Lee 34 , Kristine E. Lynch 18,19…”

Zinc against COVID-19? Symptom surveillance and deficiency risk groups

“…Dedicated funding and research efforts are needed so we can learn these distributions of symptoms and their context across the landscape of diseases and individual patient cases. The new COVID-19 mobile symptom tracking apps are one example fitting into this effort, and another are newly established public consortia aiming to harmonize data across institutions [ 31 ]. Data and resulting knowledge from these efforts will help to identify actionable information in support of disease surveillance, diagnosis, and clinical outcome management.…”

The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

“…Initiatives like, for example, the National COVID Cohort Collaborative (N3C) in the US [32], OpenSAFELY in the UK [33] or the international project Secure Collective Research (SCOR) [34] are developing platforms for a secure, cross-institutional analysis of COVID-19 data. Similarly, GECCO is part of the German COVID-19 Research Network of University Medicine [10], which aims to bundle the resources of German university hospitals to improve diagnostics and treatment of COVID-19 patients.…”