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

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 Balicer and Noa Dagan ()
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Noam Barda: Clalit Health Services
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
Noa Dagan: Clalit Health Services

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18297-9

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