Romero-Brufau Santiago, Huddleston Jeanne M, Escobar Gabriel J, Liebow Mark
Healthcare Systems Engineering Program, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, 200 First Street SW, Rochester, MN, 55905, USA.
Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Crit Care. 2015 Aug 13;19(1):285. doi: 10.1186/s13054-015-0999-1.
Metrics typically used to report the performance of an early warning score (EWS), such as the area under the receiver operator characteristic curve or C-statistic, are not useful for pre-implementation analyses. Because physiological deterioration has an extremely low prevalence of 0.02 per patient-day, these metrics can be misleading. We discuss the statistical reasoning behind this statement and present a novel alternative metric more adequate to operationalize an EWS. We suggest that pre-implementation evaluation of EWSs should include at least two metrics: sensitivity; and either the positive predictive value, number needed to evaluate, or estimated rate of alerts. We also argue the importance of reporting each individual cutoff value.
通常用于报告早期预警评分(EWS)性能的指标,如受试者操作特征曲线下面积或C统计量,对于实施前分析并无用处。由于生理恶化的发生率极低,每位患者每天仅为0.02,这些指标可能会产生误导。我们讨论了这一说法背后的统计推理,并提出了一种更适合用于实施EWS的新替代指标。我们建议,EWS的实施前评估应至少包括两个指标:敏感性;以及阳性预测值、需要评估的数量或估计的警报率中的一项。我们还强调了报告每个单独临界值的重要性。