基于机器学习的表型化和对心力衰竭治疗反应的评估,其中包括保存的喷射分数
Rui Li1,2, Yijun Liu3, Zhen Zhao3
1Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Peking University, Beijing, China.
EClinicalMedicine
|November 3, 2025
概括
机器学习确定了三种心力衰竭与保存的喷射分数 (HFpEF) 现型. 像SGLT2i和ARNI这样的特定治疗方法在某些HFpEF群体中显示出好处,这表明了个性化医疗方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 精准医学是一门精准的医学.
背景情况:
- 常见的心力衰竭与保存的喷射分数 (HFpEF),但由于其异质性,缺乏有效的治疗方法.
- 超过一半的心力衰竭病例是HFpEF,强调需要更好的治疗策略.
- 识别不同的HFpEF表型对于了解疾病机制和量身定制治疗至关重要.
研究的目的:
- 使用机器学习算法识别不同的HFpEF表型.
- 为了比较识别的HFpEF现象组的治疗反应.
- 调查现象分组在指导HFpEF管理中的临床实用性.
主要方法:
- 使用DeepCluster模型分析了来自2147名住院HFpEF患者的人口统计和临床数据.
- 考克斯的比例危险模型评估了已识别的现象组内的预后和治疗反应.
- 该模型经过了内部验证和外部验证,使用TOPCAT试验和密歇根大学队列的数据.
主要成果:
- 确定了三个不同的HFpEF现象组,具有独特的临床特征和风险概况.
- 现象组1 (代谢并发症,高) 显示死亡风险最高.
- -葡萄糖携带载体2抑制剂 (SGLT2i) 和血管激素受体-尼普利辛抑制剂 (RNAI) 在特定的现象组中显示出潜在的生存益处,ARNI对死亡率的影响在不同组之间显著不同.
- 通道阻塞剂在表观组2 (心房动的女性) 中显示有好处.
结论:
- 机器学习可以有效地将HFpEF患者分为不同的现象群,具有差异性的治疗反应.
- SGLT2i和RNAI可能有利于具有代谢并发症和混合缩/腹功能障碍的患者.
- 通道阻塞剂可能对心房动的HFpEF患者有益.
- 这些发现支持个性化的HFpEF治疗方法,需要进一步的前性验证.
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