Evaluating performance of early warning indices to predict physiological instabilities
Evaluating performance of early warning indices to predict physiological instabilities
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DOI：
10.1016/j.jbi.2017.09.008
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发表时间：
2017-11-01
影响因子：
4.5
通讯作者：
Daluwatte, Chathuri
中科院分区：
文献类型：
--
作者：
Scully, Christopher G.;Daluwatte, Chathuri
分析来自生理信号的多个特征的患者监测算法可以产生用作特定关键健康事件或生理不稳定性的预测或预后测量的指数。诸如灵敏度和阳性预测值的经典检测度量经常用于评估用于这种目的的新的患者监测指标，但是由于这些度量没有考虑监测的连续性质，因此对警告系统的评估以通知用户关键健康事件仍然是不完整的。在这篇文章中，我们提出了新的预警指数的性能评估的挑战，并提出了一个框架，提供了一个更完整的表征预警指数的性能预测的关键事件，包括预警的及时性。该框架考虑1）在有意义的时间窗口内提供通知的敏感度的评估，2）导致事件的累积敏感度，3）一旦警报被激活，警报是否保持直到事件发生的特征，以及4）警报时间的分布和附加警报的负担（例如，假警报率），其可以与感兴趣的事件相关联或不相关联。使用一个例子，从出血的实验研究，我们研究如何这种特性可以区分两个预警系统的及时性的警告和警告的负担。
Patient monitoring algorithms that analyze multiple features from physiological signals can produce an index that serves as a predictive or prognostic measure for a specific critical health event or physiological instability. Classical detection metrics such as sensitivity and positive predictive value are often used to evaluate new patient monitoring indices for such purposes, but since these metrics do not take into account the continuous nature of monitoring, the assessment of a warning system to notify a user of a critical health event remains incomplete. In this article, we present challenges of assessing the performance of new warning indices and propose a framework that provides a more complete characterization of warning index performance predicting a critical event that includes the timeliness of the warning. The framework considers 1) an assessment of the sensitivity to provide a notification within a meaningful time window, 2) the cumulative sensitivity leading up to an event, 3) characteristics on if the warning stays on until the event occurs once a warning has been activated, and 4) the distribution of warning times and the burden of additional warnings (e.g., false-alarm rate) throughout monitoring that may or may not be associated with the event of interest. Using an example from an experimental study of hemorrhage, we examine how this characterization can differentiate two warning systems in terms of timeliness of warnings and warning burden.