Institute of Biomedical Engineering, Old Road Campus Research Building (Off Roosevelt Drive), University of Oxford, Oxford OX3 7DQ, UK.
Resuscitation. 2011 Aug;82(8):1013-8. doi: 10.1016/j.resuscitation.2011.03.006. Epub 2011 Mar 23.
To develop an early warning score (EWS) system based on the statistical properties of the vital signs in at-risk hospitalised patients.
A large dataset comprising 64,622 h of vital-sign data, acquired from 863 acutely ill in-hospital patients using bedside monitors, was used to investigate the statistical properties of the four main vital signs. Normalised histograms and cumulative distribution functions were plotted for each of the four variables. A centile-based alerting system was modelled using the aggregated database.
The means and standard deviations of our population's vital signs are very similar to those published in previous studies. When compared with EWS systems based on a future outcome, the cut-off values in our system are most different for respiratory rate and systolic blood pressure. With four-hourly observations in a 12-h shift, about 1 in 8 at-risk patients would trigger our alerting system during the shift.
A centile-based EWS system will identify patients with abnormal vital signs regardless of their eventual outcome and might therefore be more likely to generate an alert when presented with patients with redeemable morbidity or avoidable mortality. We are about to start a stepped-wedge clinical trial gradually introducing an electronic version of our EWS system on the trauma wards in a teaching hospital.
基于有风险的住院患者生命体征的统计特性，开发一个早期预警评分（EWS）系统。
使用床边监护仪从 863 名急性住院患者中采集了 64622 小时的生命体征数据，使用这些数据来研究四个主要生命体征的统计特性。为四个变量中的每一个绘制了归一化直方图和累积分布函数。使用聚合数据库为基于百分位数的报警系统建模。
我们人群的生命体征的均值和标准差与之前发表的研究非常相似。与基于未来结局的 EWS 系统相比，我们系统中的临界值在呼吸频率和收缩压方面差异最大。在 12 小时轮班中每四小时观察一次，在轮班期间，大约每 8 名有风险的患者中就会有 1 名触发我们的报警系统。
基于百分位数的 EWS 系统将识别出有异常生命体征的患者，而不管其最终结局如何，因此在遇到有可挽回的发病率或可避免的死亡率的患者时，更有可能发出警报。我们即将开始一项逐步楔形临床试验，在一所教学医院的创伤病房逐步引入我们的电子 EWS 系统。
