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

Author

Listed:
  • Patrick Schwab

    (F. Hoffmann-La Roche Ltd)

  • Arash Mehrjou

    (Max Planck Institute for Intelligent Systems
    ETH Zurich)

  • Sonali Parbhoo

    (John A. Paulson School of Engineering and Applied Sciences, Harvard University)

  • Leo Anthony Celi

    (Beth Israel Deaconess Medical Center, Harvard Medical School
    MIT Critical Data, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Harvard-MIT Health Sciences and Technology)

  • Jürgen Hetzel

    (University Hospital of Tübingen
    Department of Pneumology, Kantonsspital Winterthur)

  • Markus Hofer

    (Department of Pneumology, Kantonsspital Winterthur)

  • Bernhard Schölkopf

    (Max Planck Institute for Intelligent Systems
    ETH Zurich)

  • Stefan Bauer

    (Max Planck Institute for Intelligent Systems
    CIFAR Azrieli Global Scholar)

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.

Suggested Citation

  • Patrick Schwab & Arash Mehrjou & Sonali Parbhoo & Leo Anthony Celi & Jürgen Hetzel & Markus Hofer & Bernhard Schölkopf & Stefan Bauer, 2021. "Real-time prediction of COVID-19 related mortality using electronic health records," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20816-7
    DOI: 10.1038/s41467-020-20816-7
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    Cited by:

    1. Junedh M. Amrute & Alexandra M. Perry & Gautam Anand & Carlos Cruchaga & Karl G. Hock & Christopher W. Farnsworth & Gwendalyn J. Randolph & Kory J. Lavine & Ashley L. Steed, 2022. "Cell specific peripheral immune responses predict survival in critical COVID-19 patients," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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