Abstract
The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use.
| Original language | English |
|---|---|
| Article number | 454 |
| Number of pages | 22 |
| Journal | Scientific Data |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 30 Jul 2022 |
Bibliographical note
Acknowledgments:The investigators acknowledge: This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the National Institute for Health Research (NIHR; award CO-CIN-01); the Medical Research Council (MRC; grant MC_PC_19059); the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907); NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153); NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013); NIHR Clinical Research Network (infrastructure support); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland; Artificial Intelligence for Pandemics (A14PAN) at University of Queensland; the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424). This work uses Data/Material provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The Data/materials used for this research were obtained from ISARIC4C. ISARIC4C Investigators collated the COVID-19 Clinical Information Network (CO-CIN) data.