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

Barda, Noam; Riesel, Dan; Akriv, Amichay; Levy, Joseph; Finkel, Uriah; Yona, Gal; Greenfeld, Daniel; Sheiba, Shimon; Somer, Jonathan; Bachmat, Eitan; Rothblum, Guy N.; Shalit, Uri; Netzer, Doron; Balicer, Ran; Dagan, Noa

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.