Abstract
AbstractAt 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.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference30 articles.
1. Mahase, E. Coronavirus covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate. BMJ 368, m641 (2020).
2. Ranney, M. L., Griffeth, V. & Jha, A. K. Critical supply shortages—the need for ventilators and personal protective equipment during the Covid-19 pandemic. N. Engl. J. Med. 382, e41 (2020).
3. Wu, J. T. et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat. Med. 26, 506–510 (2020).
4. Centers for Disease Control and Prevention. People Who Are at Higher Risk for Severe Illness, Vol. 2020 (Centers for Disease Control and Prevention, 2020). https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-at-higher-risk.html.
5. Chen, J. H. & Asch, S. M. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N. Engl. J. Med. 376, 2507–2509 (2017).
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