动态HRV监测和机器学习预测NYHA急性心力衰竭患者的改善
Ying Shi1, Xiu Zhang2, Chenbin Ma3
1Chinese PLA Medical School, Beijing, China; Center for Artificial Intelligence, Chinese PLA General Hospital, Beijing, China.
Computers in biology and medicine
|March 9, 2025
概括
心率变化 (HRV) 的动态变化显示出对监测心力衰竭 (HF) 治疗的希望. 改进的HRV指标与更好的结果相关,有助于个性化HF管理和减少医院再入院.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 心力衰竭 (HF) 呈现出显著的发病率和死亡率,需要改善治疗疗效的评估.
- 目前纽约心脏协会 (NYHA) 的分类在反映治疗反应方面存在局限性.
- 心率变化 (HRV) 是自主功能的敏感指标,也是HF潜在的非侵入性预后工具.
研究的目的:
- 为了研究HRV参数 (ΔHRV) 从入院到出院的动态变化,作为HF急性到稳定阶段过渡的新生物标志物.
- 评估可穿戴设备和机器学习的实用性,以提高使用 ΔHRV 的 HF 治疗疗效的评估.
- 探索 ΔHRV 与 NYHA 分类改进之间的相关性.
主要方法:
- 在入院和出院时使用可穿戴设备监测40名高压患者的HRV.
- 应用统计分析和机器学习模型来评估 ΔHRV 和 NYHA 分类改进之间的关联.
- 专注于特定的HRV参数,包括SDNN和SD2,以及它们的变化.
主要成果:
- 观察到 ΔHRV (特别是 SDNN 和 SD2) 与 NYHA 分类的改善 (p < 0.001) 之间存在显著的相关性.
- 随机森林机器学习模型证明了治疗反应的高预测准确性,达到0.77.7的AUC.
- 动态HRV变化被证明是患者从急性到稳定的HF阶段过渡的敏感指标.
结论:
- 动态HRV参数 (ΔHRV),特别是SDNN和SD2,是敏感的,非侵入性的生物标志物,用于实时监测HF治疗反应.
- 将可穿戴HRV监控与机器学习相结合,便于个性化HF管理.
- 这种方法有助于识别高风险患者进行早期干预,可能降低HF再入院率.
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