Tarassenko L, Hann A, Young D
Department of Engineering Science, Parks Road, University of Oxford, Oxford OX1 3PJ, UK.
Br J Anaesth. 2006 Jul;97(1):64-8. doi: 10.1093/bja/ael113. Epub 2006 May 17.
Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to +/-3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as 'True' by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts.
最近，人们对检测高危患者的策略兴趣大增，以便触发医疗急救团队（MET）（也称为快速反应团队，RRT）的及时干预。我们回顾了一个实时自动化系统BioSign，它通过整合普通病房非侵入性监测的生命体征信息来跟踪患者状态。BioSign融合生命体征以生成患者状态的单参数表示，即患者状态指数。BioSign采用的数据融合方法是一个五维正态概率模型，该模型先前从从具有代表性的患者样本获取的生命体征数据中学习得到。当单个生命体征偏离其正常值接近+/-3个标准差时，或者当两个或更多生命体征偏离正常范围但幅度较小时，BioSign就会发出警报。在一项针对高危择期/急诊手术患者或内科患者的试验中，BioSign平均每8小时发出一次警报；临床专家将其中95%的警报分类为“真警报”。回顾性分析还表明，BioSign中的数据融合算法能够在单通道警报之前检测到关键事件。
