开发和验证风险分层和共享决策工具的风险分层和共享决策工具,用于心力衰竭患者心房动的导管切除:一个多中心队列研究
Xiaodong Peng1, Liu He1, Jue Wang1
1Department of Cardiology, Beijing AnZhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Office of Beijing Cardiovascular Diseases Prevention, Beijing, China.
EClinicalMedicine
|July 11, 2025
概括
使用机器学习的新工具有助于决定心房动 (AF) 和心力衰竭 (HF) 患者是否应该接受射频导管切除 (RFCA). 它确定了最有可能从RFCA中受益的患者,改善了共享决策.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 心房动 (AF) 和心力衰竭 (HF) 经常并存,使风险评估和治疗选择复杂化.
- 准确的风险分层对于指导治疗决策至关重要,包括射频导管切除 (RFCA).
- 开发共享决策工具对于优化复杂心血管疾病的护理至关重要.
研究的目的:
- 为患有AF和HF的患者开发和验证共享决策工具.
- 帮助AF和HF患者的风险分层.
- 在这个患者群体中指导有关射频导管切除 (RFCA) 的决策.
主要方法:
- 一项多中心队列研究使用了来自中国-AF注册表的数据的无监督聚类和监督学习.
- 外部验证是使用国际CABANA试验数据进行的.
- 该研究分析了RFCA和诊断结果之间的关联,涉及到已识别的风险层.
主要成果:
- 开发了一个工具,根据25个临床特征确定了三个不同的风险集群.
- 综合事件 (心血管死亡和中风) 发生率在各个集群中存在显著差异.
- 与药物治疗相比,RFCA在风险最高的集群 (集群1) 中显示出明显更好的预后.
结论:
- 一个基于机器学习的工具有效地分层了AF和HF患者的风险.
- 该工具通过识别可能从RFCA中受益的患者来促进共享决策.
- 建议在干预试验和现实实践中进一步验证.
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