使用人工智能从12导电图预测心力衰竭:一个HeartShare/AMP-HF聚合队列分析
Akshay S Desai1, Ambarish Pandey2, Rohit Suratekar3
1Heart and Vascular Institute, MassGeneral Brigham and Harvard Medical School, Boston, Massachusetts, USA.
Journal of the American College of Cardiology
|January 6, 2026
概括
应用于心电图 (ECG-AI) 的人工智能在与PREVENT-HF方程相结合时,显著改善了心力衰竭 (HF) 的预测. 这种ECG-AI工具增强了针对性HF预防策略的风险分层.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 预防性心脏病学 预防性心脏病学
背景情况:
- 应用于心电图 (ECG-AI) 的人工智能提供了一种可扩展的方法来识别心力衰竭 (HF) 风险的个体.
- 通过检测潜在的心脏功能障碍,ECG-AI可以指导预防性干预.
研究的目的:
- 为了评估ECG-AI是否检测到缩和腹功能障碍,改善了发生的HF预测,超出了PREVENT-HF临床风险方程.
- 评估使用ECG-AI与已确定的临床风险因素相结合的高频风险预测的增强.
主要方法:
- 从Framingham心脏研究,MESA和CHS参与者的基线临床和心电图数据汇总.
- 使用了经过验证的ECG-AI算法 (静脉缩的ECG-AI LEF,静脉缩功能障碍的ECG-AI DD) 和PREVENT-HF方程.
- 使用哈雷尔的C统计和净重新分类改进评估风险歧视和重新分类.
主要成果:
- 呈阳性复合ECG-AI屏幕的参与者显示,患高频发作的风险增加了10至20倍.
- 将ECG-AI添加到PREVENT-HF中显著改善了1,3,5年和10年的HF风险的净重新分类.
- 净重新分类改进在0.0860.125 (PREVENT-HF 10%的门) 和0.3270.403 (PREVENT-HF 20%的门) 之间.
结论:
- 通过将ECG-AI与PREVENT-HF集成,可以更好地分辨近期心力衰竭风险.
- 电脑心电图-人工智能有可能对人口水平的高频风险分层进行分层.
- 这种方法可以促进针对心力衰竭的有针对性的预防策略的实施.
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