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Developing a COVID-19 mortality risk prediction model when individual-level data are not available

Author

Listed:
  • Noam Barda

    (Clalit Health Services
    Ben Gurion University of the Negev
    Ben Gurion University of the Negev)

  • Dan Riesel

    (Clalit Health Services)

  • Amichay Akriv

    (Clalit Health Services)

  • Joseph Levy

    (Clalit Health Services)

  • Uriah Finkel

    (Clalit Health Services)

  • Gal Yona

    (Weizmann Institute of Science)

  • Daniel Greenfeld

    (Technion University)

  • Shimon Sheiba

    (Technion University)

  • Jonathan Somer

    (Technion University)

  • Eitan Bachmat

    (Ben Gurion University of the Negev)

  • Guy N. Rothblum

    (Weizmann Institute of Science)

  • Uri Shalit

    (Technion University)

  • Doron Netzer

    (Clalit Health Services)

  • Ran Balicer

    (Clalit Health Services
    Ben Gurion University of the Negev)

  • Noa Dagan

    (Clalit Health Services
    Ben Gurion University of the Negev
    Ben Gurion University of the Negev)

Abstract

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

Suggested Citation

  • Noam Barda & Dan Riesel & Amichay Akriv & Joseph Levy & Uriah Finkel & Gal Yona & Daniel Greenfeld & Shimon Sheiba & Jonathan Somer & Eitan Bachmat & Guy N. Rothblum & Uri Shalit & Doron Netzer & Ran , 2020. "Developing a COVID-19 mortality risk prediction model when individual-level data are not available," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18297-9
    DOI: 10.1038/s41467-020-18297-9
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