在未确定来源的栓塞性中风中用鼻节律心电图识别心房动:用可插入心脏监视器进行验证研究
Ki-Hyun Jeon1, Jong-Hwan Jang2, Sora Kang2
1Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Korean circulation journal
|November 16, 2023
概括
一个深度学习算法使用标准心电图准确地检测中风患者的心脏动. 这种人工智能工具有助于识别隐藏的AF,改善中风风险预测.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 阴性心房动 (AF) 是源不明的栓塞性中风 (ESUS) 的重要原因.
- 检测零星的AF是具有挑战性的,因为它的间歇性质.
- 深度学习 (DL) 提供了从鼻腔节律 (SR) 电心电图 (ECG) 中识别隐藏的AF的潜力.
研究的目的:
- 开发和验证一种深度学习算法 (DLA),用于预测ESUS患者的阳性肌痛.
- 改善ESUS中AF检测的诊断性能.
- 将DL与已知的AF风险因素结合起来,以提高预测.
主要方法:
- 从12,666名患者中使用44,085个SR12心电图开发了一个DLA.
- 在221名ESUS患者用可插入心脏监测器 (ICM) 验证了DLA.
- 与传统预测模型 (CHARGE-AF,C2HEST,HATCH) 进行DLA性能比较.
主要成果:
- 在内部验证中,DLA在曲线下的面积 (AUC) 达到0.862.
- 在ESUS患者的外部验证显示DLA诊断准确度为0.811和AUC为0.827.
- DLA的表现优于CHARGE-AF,C2HEST和HATCH;一个组合模型的AUC为0.906.
结论:
- 在ESUS患者中,DLA准确地从SR ECG中识别了神经发作性AF.
- 与传统的AF预测模型相比,DLA表现出卓越的性能.
- 将DLA与已确定的风险因素相结合,为ESUS中 Paroxysmal AF检测提供了一个有前途的工具.
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