Real-time prediction of COVID-19 related mortality using electronic health records

Patrick Schwab (), Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf and Stefan Bauer
Additional contact information
Patrick Schwab: F. Hoffmann-La Roche Ltd
Arash Mehrjou: Max Planck Institute for Intelligent Systems
Sonali Parbhoo: John A. Paulson School of Engineering and Applied Sciences, Harvard University
Leo Anthony Celi: Beth Israel Deaconess Medical Center, Harvard Medical School
Jürgen Hetzel: University Hospital of Tübingen
Markus Hofer: Department of Pneumology, Kantonsspital Winterthur
Bernhard Schölkopf: Max Planck Institute for Intelligent Systems
Stefan Bauer: Max Planck Institute for Intelligent Systems

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.

Date: 2021
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DOI: 10.1038/s41467-020-20816-7

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