人工智能启用心电图用于在患有鼻节律的患者中进行术前检测心房动和死亡风险
Chiao-Chin Lee1,2, Chin-Sheng Lin1, Wen-Yu Lin1
1Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, R.O.C.
International journal of medical sciences
|January 26, 2026
概括
人工智能模型可以从心电图中检测到隐藏的心房动 (AF),从而改善接受手术的患者的风险评估. 这个AI工具可以识别高风险的新发性AF和相关死亡率的患者.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 前庭动 (AF),既是先前存在的,也是新发病的 (NOAF),是重要的外科手术期间风险因素.
- NOAF与手术后的短期死亡率和不良事件的增加有关.
- 目前对外科手术前AF的风险评估方法可能不足.
研究的目的:
- 开发和验证一种人工智能模型,用于从鼻腔节律心电图中检测隐藏的AF.
- 在非心脏外科手术中评估先前存在的AF和NOAF的预后相关性.
- 通过使用人工智能来改善外科手术期间风险分层.
主要方法:
- 一个人工智能模型被训练并验证,以检测ECG隐藏的AF.
- 该模型对NOAF和短期结果的预测能力在没有已知的AF的患者中进行了评估.
- 传统的临床风险得分与人工智能模型的性能进行了比较.
主要成果:
- 人工智能模型在开发过程中实现了0.87的AF预测AUC.
- 以前存在的AF和NOAF与增加的30天死亡率有显著的关联.
- 通过AI识别的高风险患者的30天死亡率 (HR 17.33) 显著增加,并且在临床评分中表现出色.
结论:
- 人工智能准确地识别了患有高额外科外关期AF相关风险的患者.
- 这种基于人工智能的方法可以促进有针对性的干预措施,以改善患者的治疗结果.
- 人工智能增强了对心房动的术后风险评估.
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