从模式到预后:高级心力衰竭中的机器学习衍生集群
Murat Karaçam1, Barkın Kültürsay2, Deniz Mutlu3
1Department of Cardiology, Bitlis State Hospital, Bitlis, Türkiye.
Frontiers in cardiovascular medicine
|November 10, 2025
概括
机器学习确定了两个高级心力衰竭 (HF) 现型. 一组血液动力学和预后更好,而另一组由于双心脏功能障碍和运动能力低下而面临更高的死亡风险.
科学领域:
- 心脏病学 心脏病学
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 晚期心力衰竭 (HF) 呈现出复杂的异质性,挑战传统的预后和个性化治疗.
- 现有的分类系统可能无法完全捕捉到针对高级高频患者量身定制的患者护理所需的细微差别.
研究的目的:
- 采用无监督机器学习来识别高级HF患者中不同的临床子组.
- 评估这些已识别的表型对长期临床结果的预后影响.
主要方法:
- 对524名晚期HF患者的回顾性分析,具有全面的临床,心声学,血液动力学和运动数据.
- K-意味着对标准化的多维数据应用集群,以定义患者表型.
- 卡普兰-梅尔分析和考克斯回归用于评估结果 (死亡率,LVAD,移植) 按集群.
主要成果:
- 通过聚类确定了两个不同的表型.
- 集群1:患者血液动力学和功能状况保持良好,与良好的预后相关.
- 集群2:患有双心脏功能障碍,肺压升高和运动能力降低的老年患者,显着更高的不良事件发生率 (HR: 3.84; p < 0.001).
结论:
- 机器学习成功地划分了两个先进的HF表型,具有不同的临床特征和预后.
- 这种数据驱动的表型化方法为在先进的高频中改善风险分层提供了潜力.
- 这些发现可以指导为这种脆弱患者群体开发个性化治疗策略.
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