通过集群分析和外部验证来确定心房的表型
Yuki Saito1, Yuto Omae2, Koichi Nagashima3
1Division of Cardiology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan saito.yuki@nihon-u.ac.jp.
Heart (British Cardiac Society)
|June 1, 2023
概括
机器学习确定了五种不同的心房动 (AF) 现型. 这些由临床特征定义的患者群体显示出死亡率和心血管事件的不同风险,有助于识别高风险患者.
科学领域:
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 临床研究 临床研究
背景情况:
- 心房动 (AF) 是一种复杂的疾病,患者的个人资料不同.
- 了解AF异质性对于准确的预后和风险分层至关重要.
研究的目的:
- 应用集群分析来识别大型AF队列中的不同患者表型.
- 评估这些已识别的AF表型对临床结果的预后影响.
主要方法:
- 利用K-原型集群分析对来自两个大型,前性观察性AF注册表 (SAKURA和RAFFINE) 的14个临床变量.
- 根据患者的特征,包括年龄,性别,并发病症和病史,衍生出五个不同的AF集群.
- 评估所有原因死亡率和复合心血管事件的终点.
主要成果:
- 确定了五个AF集群,其特点是独特的临床概况 (例如,年轻人/低并发病率,高血压主导,老年人/心力衰竭,老年人/糖尿病/缺血性心脏病).
- 在所有集群中观察到所有原因死亡率和复合心血管事件风险的显著梯度 (逻辑等级p<0.001).
- 结果在独立的队列中得到了验证,证实了鉴定出来的表型的稳定性.
结论:
- 机器学习驱动的集群分析成功地划分了五种不同的AF表型.
- 这些表型具有独特的临床特征,并与不良结果的差异性风险有关.
- 已识别的AF表型为识别和管理患有心房的高风险个体提供了宝贵的工具.
关键词:
心房动是心房动的一种.
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