基于人工智能的心电图模型用于预测心力衰竭与保存的喷射分数:一个单中心研究研究.
David Hong1, Sung-Hee Song2, Heayoung Shin1
1Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.
European heart journal. Digital health
|September 23, 2025
概括
人工智能 (AI) 的心电图 (ECG) 模型可以预测心力衰竭与保存的喷射分数 (HFpEF),并识别患有心脏不良事件风险较高的患者. 这个AI-ECG工具有助于简化HFpEF诊断和预后.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 诊断心力衰竭与保存的喷射分数 (HFpEF) 是具有挑战性的,因为没有一个明确的标志物,需要多个复杂的测试.
- 目前HFpEF的诊断途径通常涉及心声回声和生物标志物测量,这可能是耗时和资源密集的.
研究的目的:
- 开发和验证一个支持人工智能 (AI) 的心电图 (ECG) 模型来预测HFpEF.
- 评估AI-ECG模型的预后能力,根据患者的不良心血管结果风险对患者进行分层.
主要方法:
- 一项回顾性队列研究,使用了来自13,081名患者的数据,使用HFA-PEFF得分进行分类 (HFpEF ≥5,对照 <5).
- 在ECG数据上训练了一个卷积神经网络来预测HFpEF,其性能由AUROC评估.
- 患者被分为训练,验证和测试组,比例为7:1:2.
主要成果:
- AI-ECG模型在HFpEF预测方面取得了良好的区分性表现,AUROC为0.81 (95%CI为0.79-0.82).
- 通过HFpEF风险因素分层的子组观察到一致的模型性能.
- 阳性AI-ECG分类与5年内心脏病死亡 (HR 9.56) 和心力衰竭住院治疗 (HR 5.91) 的风险显著增加有关.
结论:
- AI-ECG模型是预测HFpEF的可靠工具,根据HFA-PEFF评分的定义.
- 该模型有效地根据患者的预后对患者进行分层,识别那些具有较高不良事件风险的人.
- 这种AI-ECG模型的临床整合可以简化诊断过程并改善HFpEF患者的治疗.
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