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

Medicine and Health

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

P. Schwab, A. Mehjoo, et al.

Discover CovEWS, a groundbreaking risk scoring system that predicts COVID-19 mortality risk using electronic health records. Developed by a team of experts including Patrick Schwab, Arash Mehjoo, Sonali Parbhoo, and others, this innovative tool showcases exceptional predictive performance, allowing for timely interventions that could save lives.

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~3 min • Beginner • English
Abstract
Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by 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. We present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk developed using over 2863 years of observation time from 66,430 patients across more than 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% 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 hours prior to mortality events. CovEWS could enable earlier intervention and may therefore help in preventing or mitigating COVID-19 related mortality.
Publisher
Nature Communications
Published On
Feb 16, 2021
Authors
Patrick Schwab, Arash Mehjoo, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer
Tags
COVID-19
mortality risk
electronic health records
predictive performance
intervention

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