Shamout Farah, Zhu Tingting, Clifton Lei, Briggs Jim, Prytherch David, Meredith Paul, Tarassenko Lionel, Watkinson Peter J, Clifton David A
Institute of Biomedical Engineering, University of Oxford, Oxford, UK
Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
BMJ Open. 2019 Nov 19;9(11):e033301. doi: 10.1136/bmjopen-2019-033301.
Early warning scores (EWS) alerting for in-hospital deterioration are commonly developed using routinely collected vital-sign data from the whole in-hospital population. As these in-hospital populations are dominated by those over the age of 45 years, resultant scores may perform less well in younger age groups. We developed and validated an age-specific early warning score (ASEWS) derived from statistical distributions of vital signs.
Observational cohort study.
Oxford University Hospitals (OUH) July 2013 to March 2018 and Portsmouth Hospitals (PH) NHS Trust January 2010 to March 2017 within the Hospital Alerting Via Electronic Noticeboard database.
Hospitalised patients with electronically documented vital-sign observations OUTCOME: Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest.
Statistical distributions of vital signs were used to develop an ASEWS to predict the composite outcome within 24 hours. The OUH development set consisted of 2 538 099 vital-sign observation sets from 142 806 admissions (mean age (SD): 59.8 (20.3)). We compared the performance of ASEWS to the National Early Warning Score (NEWS) and our previous EWS (MCEWS) on an OUH validation set consisting of 581 571 observation sets from 25 407 emergency admissions (mean age (SD): 63.0 (21.4)) and a PH validation set consisting of 5 865 997 observation sets from 233 632 emergency admissions (mean age (SD): 64.3 (21.1)). ASEWS performed better in the 16-45 years age group in the OUH validation set (AUROC 0.820 (95% CI 0.815 to 0.824)) and PH validation set (AUROC 0.840 (95% CI 0.839 to 0.841)) than NEWS (AUROC 0.763 (95% CI 0.758 to 0.768) and AUROC 0.836 (95% CI 0.835 to 0.838) respectively) and MCEWS (AUROC 0.808 (95% CI 0.803 to 0.812) and AUROC 0.833 (95% CI 0.831 to 0.834) respectively). Differences in performance were not consistent in the elder age group.
Accounting for age-related vital sign changes can more accurately detect deterioration in younger patients.
用于警示住院病情恶化的早期预警评分（EWS）通常是利用从全体住院患者中常规收集的生命体征数据制定的。由于这些住院患者以45岁以上人群为主，因此得出的评分在较年轻年龄组中可能表现欠佳。我们制定并验证了一种基于生命体征统计分布得出的特定年龄早期预警评分（ASEWS）。
观察性队列研究。
牛津大学医院（OUH），2013年7月至2018年3月；朴茨茅斯医院（PH）国民保健服务信托基金，2010年1月至2017年3月，数据来自医院电子公告栏数据库中的警报信息。
有电子记录生命体征观察数据的住院患者
计划外重症监护病房收治、死亡和心脏骤停的综合结局。
利用生命体征的统计分布来制定ASEWS，以预测24小时内的综合结局。OUH开发数据集包括来自142806例住院患者的2538099组生命体征观察数据（平均年龄（标准差）：59.8（20.3））。我们在一个由来自25407例急诊住院患者的581571组观察数据（平均年龄（标准差）：63.0（21.4））组成的OUH验证集，以及一个由来自233632例急诊住院患者的5865997组观察数据（平均年龄（标准差）：64.3（21.1））组成的PH验证集上，将ASEWS的性能与国家早期预警评分（NEWS）以及我们之前的EWS（MCEWS）进行了比较。在OUH验证集（曲线下面积0.820（95%可信区间0.815至0.824））和PH验证集（曲线下面积0.840（95%可信区间0.839至0.841））中，ASEWS在16 - 45岁年龄组中的表现优于NEWS（曲线下面积分别为0.763（95%可信区间0.758至0.768）和0.836（95%可信区间0.835至0.838））和MCEWS（曲线下面积分别为0.808（95%可信区间0.803至0.812）和0.833（95%可信区间0.831至0.834））。在老年组中，性能差异并不一致。
考虑与年龄相关的生命体征变化能够更准确地检测出年轻患者的病情恶化情况。
