Dataset Information

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

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 11 ^th 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.

SUBMITTER: Prieto-Alhambra D

PROVIDER: S-EPMC7941629 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

Prieto-Alhambra Daniel D Kostka Kristin K Duarte-Salles Talita T Prats-Uribe Albert A Sena Anthony A Pistillo Andrea A Khalid Sara S Lai Lana L Golozar Asieh A Alshammari Thamir M TM Dawoud Dalia D Nyberg Fredrik F Wilcox Adam A Andryc Alan A Williams Andrew A Ostropolets Anna A Areia Carlos C Jung Chi Young CY Harle Christopher C Reich Christian C Blacketer Clair C Morales Daniel D Dorr David A DA Burn Edward E Roel Elena E Tan Eng Hooi EH Minty Evan E DeFalco Frank F de Maeztu Gabriel G Lipori Gigi G Alghoul Heba H Zhu Hong H Thomas Jason J Bian Jiang J Park Jimyung J Roldán Jordi Martínez JM Posada Jose J Banda Juan M JM Horcajada Juan P JP Kohler Julianna J Shah Karishma K Natarajan Karthik K Lynch Kristine K Liu Li L Schilling Lisa L Recalde Martina M Spotnitz Matthew M Gong Mengchun M Matheny Michael M Valveny Neus N Weiskopf Nicole N Shah Nigam N Alser Osaid O Casajust Paula P Park Rae Woong RW Schuff Robert R Seager Sarah S DuVall Scott S You Seng Chan SC Song Seokyoung S Fernández-Bertolín Sergio S Fortin Stephen S Magoc Tanja T Falconer Thomas T Subbian Vignesh V Huser Vojtech V Ahmed Waheed-Ul-Rahman WU Carter William W Guan Yin Y Galvan Yankuic Y He Xing X Rijnbeek Peter P Hripcsak George G Ryan Patrick P Suchard Marc M

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<b>Background:</b> 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. <b>Methods:</b> We conducted a descriptive cohort study using a federated network of data partn ...[more]

PMID: 33688639

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

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Dataset Information

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

Publications

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

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