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
2020-09-18T15:02:20Z
2020-09-18T15:02:20Z
2020
1546-170X
https://doi.org/10.1038/s41467-020-18297-9
http://hdl.handle.net/20.500.12010/13451
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.
9 páginas
application/pdf
eng
Nature communications
reponame:Expeditio Repositorio Institucional UJTL
instname:Universidad de Bogotá Jorge Tadeo Lozano
COVID-19
Mortality risk
Prediction model
Developing a COVID-19 mortality risk prediction model when individual-level data are not available
Artículo
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/acceptedVersion
Abierto (Texto Completo)
https://doi.org/10.1038/s41467-020-18297-9
http://purl.org/coar/resource_type/c_2df8fbb1