一种基于心电图的新型深度学习算法,用于预测早发性心室复合症患者的心肌病
Joshua Lampert1, Akhil Vaid2, William Whang1
1Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
JACC. Clinical electrophysiology
|July 22, 2023
概括
一个深度学习模型可以预测使用12导电心电图 (ECG) 预先预发性心室综合体 (PVCs) 的患者的左心室喷射率 (LVEF) 减少. 这种算法准确地识别心肌病风险,帮助PVC患者的早期干预.
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:
- 早发性心室综合体 (PVCs) 是常见的心律失常症,可能导致PVC诱导的心肌病.
- 早期识别患有左心室喷射分数 (LVEF) 减少风险的患者对于及时干预至关重要.
- 目前用于预测PVC患者心肌病的方法可能无法充分利用现有的诊断数据.
研究的目的:
- 开发和验证一种深度学习算法,用于预测PVC患者的LVEF减少.
- 通过使用大型多中心数据集来评估深度学习模型的准确性.
- 为了确定电心电图 (ECG) 的主要特征,这些特征有助于模型的预测.
主要方法:
- 利用了来自5家医院的383,514张心电图的数据集,其中14241名患者被诊断患有PVC.
- 开发了一个内部训练和测试的深度学习模型,在多个机构进行外部验证.
- 主要结局是6个月内诊断LVEF≤40%;使用接收器操作曲线 (AUC) 下的面积和可解释性图分析.
主要成果:
- 深度学习模型的AUC为0.79 (95%CI:0.77-0.81),预测LVEF在6个月内降低至≤40%.
- 可解释性分析强调QRS复合体和鼻腔节律中的ST段是关键预测因素,独立于PVC负担.
- 在接受PVC切除术的患者中,89%的患者显示LVEF改善和心肌病解决.
结论:
- 通过对12导心电图的深度学习分析,可以准确地预测PVC患者的新发性心肌病,无论PVC负担如何.
- 模型的表现在不同的人口群体 (性别,种族) 中是一致的.
- 该算法依赖于鼻腔节律心电图特征 (QRS复合体/ST段),而不是PVC形态,用于预测.
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