基于人工智能的预测模型对一般病房患者心脏骤停的临床有效性:非随机对照试验
Mi Hwa Park1, Mincheol Kim2,3, Man-Jong Lee1
1Department of Critical Care Medicine, Inha University, Incheon 22332, Republic of Korea.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
一个预测医院患者心脏骤停的人工智能 (AI) 系统显著减少了心脏骤停事件和死亡率. 这种基于人工智能的软件作为医疗器械 (AI-SaMD) 提高了患者的安全性,而不需要额外的资源.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 临床状况恶化的住院患者面临高死亡风险.
- 传统的快速反应系统 (RRS) 往往无法持续改善患者的治疗结果.
- 需要先进的工具来预测和预防病房患者的不良事件.
研究的目的:
- 评估基于人工智能 (AI) 的心脏骤停预测模型对患者结果的影响.
- 评估人工智能引导干预措施的有效性,与在医院环境中的通常护理相比.
- 确定AI-SaMD警报是否可以优化快速响应系统 (RRS) 的性能.
主要方法:
- 一年期前性非随机干预试验,涉及35,627名通用病房住院患者.
- 实施基于AI的软件作为医疗器械 (AI-SaMD) 用于高风险警报.
- 以AI-SaMD为指导的队列 (n=1409) 与基于对警报的临床反应的常规护理队列的比较.
主要成果:
- 在AI-SaMD指导组中,心脏骤停的发生率显著下降 (2.07%至1.06%,RR调整为0.54).
- 住院死亡率也显著下降 (调整后的RR为0.65).
- 这些改进是在不需要额外的医院资源的情况下实现的.
结论:
- 由AI-SaMD引导的警报有效地减少了心脏骤停和住院死亡率.
- 人工智能系统可以集成到现有的临床工作流程中,以提高患者的安全性.
- 人工智能-SaMD展示了优化快速响应系统性能的潜力.
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