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

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作者
Patrick Schwab
Arash Mehrjou
Sonali Parbhoo
Leo Anthony Celi
Jürgen Hetzel
Markus Hofer
Bernhard Schölkopf
Stefan Bauer
机构
[1] F. Hoffmann-La Roche Ltd,Department of Medicine
[2] Max Planck Institute for Intelligent Systems,Department of Medical Oncology and Pneumology
[3] ETH Zurich,undefined
[4] John A. Paulson School of Engineering and Applied Sciences,undefined
[5] Harvard University,undefined
[6] Beth Israel Deaconess Medical Center,undefined
[7] Harvard Medical School,undefined
[8] MIT Critical Data,undefined
[9] Laboratory for Computational Physiology,undefined
[10] Institute for Medical Engineering and Science,undefined
[11] Harvard-MIT Health Sciences and Technology,undefined
[12] University Hospital of Tübingen,undefined
[13] Department of Pneumology,undefined
[14] Kantonsspital Winterthur,undefined
[15] CIFAR Azrieli Global Scholar,undefined
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摘要
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.
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