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使用人工智能预测急诊室的闭塞心肌梗塞
Axel Nyström1,2, Anders Björkelund2, Henrik Wagner3
1Department of Laboratory Medicine, Lund University, Lund, Sweden.
Journal of the American College of Emergency Physicians open
|January 22, 2026
概括
一个人工智能 (AI) 模型有效地预测急性冠状动脉封闭心肌梗塞 (OMI) 在急诊室患者胸痛. 这种人工智能工具在STEMI标准上表现出更好的灵敏度,可能减少OMI案件的干预时间.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 紧急医疗 紧急医疗
背景情况:
- 急性冠状动脉封闭性心肌梗塞 (OMI) 是一种需要快速诊断和干预的危急情况.
- 目前的诊断标准,如ST升高心肌梗塞 (STEMI),可能会错过很大一部分OMI病例.
- 对OMI的及时干预往往会延迟,导致患者的不良结果.
研究的目的:
- 开发和验证一种人工智能 (AI) 模型,用于预测急诊室 (ED) 患者出现胸痛时的OMI.
- 利用现有的早期ED评估数据,包括心电图 (ECG),病史和初始实验室值.
- 将AI模型的性能与已建立的STEMI标准进行比较.
主要方法:
- 一个深度学习的人工智能模型是使用来自5个瑞典医院的24,511名成年ED患者的胸痛数据开发的.
- 通过注册数据和健康记录审查确定了OMI病例;排除了绕过ED的STEMI病例.
- 人工智能模型在心电图数据上受训,可选地与其他早期ED信息相结合,并在内部验证.
主要成果:
- 人工智能模型在接受器操作特征 (AUC) 下获得了高面积,为95.3%的OMI预测.
- 在97.4%的特异性,人工智能模型显示了62%的灵敏度,明显高于STEMI标准的27%的灵敏度.
- 只有5.4%的OMI病例得到了及时的血管造影,这凸显了当前护理途径的关键差距.
结论:
- 开发的AI模型在识别ED胸痛患者的OMI方面表现出色,在灵敏度方面表现优于STEMI标准.
- 这种人工智能模型的实施可以加快诊断,减少对OMI进行干预的延迟.
- 这些发现表明,通过解决OMI治疗目前的延迟,有可能改善患者的治疗结果.
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