患者病情恶化的实时监测系统:一个务实的集群随机对照试验
Sarah C Rossetti1,2, Patricia C Dykes3,4, Chris Knaplund5
1Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY, USA. sac2125@cumc.columbia.edu.
Nature medicine
|April 2, 2025
概括
使用护理文档的CONCERN预警系统 (EWS) 显著降低了患者病情恶化的风险. 这种机器学习工具降低了住院死亡率和住院时间,改善了患者的治疗结果.
科学领域:
- 临床信息学 临床信息学
- 患者安全 患者安全
- 医疗保健中的机器学习
背景情况:
- 早期发现患者病情恶化对于改善医院治疗结果至关重要.
- 现有的早期预警系统 (EWS) 在实时患者监测方面存在局限性.
- 护理文档提供了丰富的数据来源,用于识别有风险的患者.
研究的目的:
- 评价COMmunicating Narrative Concerns Entered by RNs (CONCERN) 早期预警系统 (EWS) 在降低住院患者病情恶化风险方面的有效性.
- 为了比较使用CONCERN EWS的护理团队和接受常规护理的护理团队之间的患者结果.
- 评估基于机器学习的EWS对死亡率,停留时间和其他重症监护事件的影响.
主要方法:
- 一年多的实用试验,涉及2个卫生系统的74个临床单位.
- 集群随机化单位进入干预 (CONCERN EWS) 和通常护理组.
- 分析了60,893名成年人住院的情况,重点关注住院死亡率和住院时间的副初级结果.
主要成果:
- 在CONCERN EWS干预组中,瞬间死亡风险降低了35.6% (HR,0.64;P < 0.0001).
- 在干预组,入住时间减少了11.2% (IRR,0.91;P < 0.0001).
- 观察到败血症风险降低了7.5% (HR,0.93;P = 0.0317) ,预期不到的ICU转移增加了24.9% (HR,1.25;P = 0.0011).
结论:
- 一个基于机器学习的EWS,通过护理监测模式获得信息,有效地降低了住院患者病情恶化的风险.
- 康塞恩电子预警系统在减少死亡率和停留时间方面显示出显著的好处.
- 虽然提高了安全性,但该系统对ICU转移的影响需要进一步调查.
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