Developing a COVID-19 mortality risk prediction model when individual-level data are not available
Developing a COVID-19 mortality risk prediction model when individual-level data are not available
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DOI：
10.1038/s41467-020-18297-9
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发表时间：
2020-09-07
影响因子：
16.6
通讯作者：
Dagan, Noa
中科院分区：
文献类型：
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作者：
Barda, Noam;Riesel, Dan;Dagan, Noa
在COVID-19大流行病上，当尚未获得COVID-19患者的个别级别数据时，已经需要风险预测因子来支持预防和治疗决策。在这里，我们报告了一种混合策略来创建这样的预测因子，结合了基线严重的呼吸道感染风险预测因子和一种后处理方法，以校准预测为报道的COVID-19 COVID-19病例效率率。随着COVID-19患者队列的积累，该预测因子得到了良好的歧视（接收器操作特征曲线下的面积为0.943）和校准（与基线预测指标相比明显改善）。在5％的风险阈值下，有15％的患者被标记为高风险，敏感性为88％。因此，我们证明，即使在流行病的大流行病开始时，战争的雾气也笼罩在战争中，也可以提供有用的风险预测因子，该预测因素现在已在大型医疗保健组织中广泛使用。
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.