Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

• DOI: 10.2147/clep.s323292
• Publication Date: 2022-03-21T16:00:13Z
• Authors: Kristin Kostka; Talita Duarte-Salles; Albert Prats-Uribe; Anthony G Sena; Andrea Pistillo; Sara Khalid; Lana YH Lai; Asieh Golozar; Thamir M Alshammari; Dalia M Dawoud; Fredrik Nyberg; Adam B Wilcox; Alan Andryc; Andrew Williams; Anna Ostropolets; Carlos Areia; Chi Young Jung; Christopher A Harle; Christian G Reich; Clair Blacketer; Daniel R Morales; David A Dorr; Edward Burn; Elena Roel; Eng Hooi Tan; Evan Minty; Frank DeFalco; Gabriel de Maeztu; Gigi Lipori; Heba Alghoul; Hong Zhu; Jason A Thomas; Jiang Bian; Jimyung Park; Jordi Martínez Ro...; Jose D Posada; Juan M Banda; Juan P Horcajada; Julianna Kohler; Karishma Shah; Karthik Natarajan; Kristine E Lynch; Li Liu; Lisa M Schilling; Martina Recalde; Matthew Spotnitz; Mengchun Gong; Michael E Matheny; Neus Valveny; Nicole G Weiskopf; Nigam Shah; Osaid Alser; Paula Casajust; Rae Woong Park; Robert Schuff; Sarah Seager; Scott L DuVall; Seng Chan You; Seokyoung Song; Sergio Fernández-...; Stephen Fortin; Tanja Magoc; Thomas Falconer; Vignesh Subbian; Vojtech Huser; Waheed-Ul-Rahman ...; William Carter; Yin Guan; Yankuic Galvan; Xing He; Peter R Rijnbeek; George Hripcsak; Patrick B Ryan; Marc A Suchard; Daniel Prieto-Alh...
• Keywords: Real world data; 330; Clinical Sciences; OMOP CDM; 610; Infectious and parasitic diseases; RC109-216; 613; real world evidence; Article; OHDSI; SDG 3 - Good Health and Well-being; Clinical Research; open science; Clinical Epidemiology; Aetiology; Original Research; ohdsi; Prevention; name=Epidemiology; omop cdm; 004; Descriptive epidemiology; 3. Good health; real world data; Infectious Diseases; Good Health and Well Being; /dk/atira/pure/subjectarea/asjc/2700/2713; Public Health and Health Services; Real world evidence; Open science; descriptive epidemiology; 2.4 Surveillance and distribution

Abstract Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.