机器学习识别大动脉反中患者团的机器学习
Maan Malahfji1, Xin Tan2, Yodying Kaolawanich3
1Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
JACC. Cardiovascular imaging
|March 27, 2025
概括
机器学习确定了主动脉反 (AR) 的不同患者群体,揭示了不同的预后. 这种精准医学方法改善了风险分层,突出了需要进一步关注的高死亡率女性AR表型.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 大动脉吐 (AR) 患者群体通常被视为同质,尽管观察到疾病进展和结果的变化.
- 当前的治疗模式可能无法充分解决AR患者队列中的异质性.
研究的目的:
- 利用无监督的机器学习来识别AR群体内的不同患者现象集群.
- 评估这些已识别的现象集群的预后相关性.
主要方法:
- 使用23个临床和心脏磁共振 (CMR) 变量 (Partition Around Medoids) 的无监督集群管道.
- 从4个美国中心的972名患者的数据使用,独立于结果的患者集群,并进行了验证.
- 所有原因的死亡是主要的结局措施.
主要成果:
- 确定了四种不同的AR类现象群,死亡率各不相同 (1%至22%).
- 现象群1:年轻的雄性,双门,高LV重塑 (1%的死亡率).
- 现象群2:年龄较大的雄性,三门,中间结果 (10%的死亡率).
- 现象群3:年龄较大的男性,高并发症, LV功能障碍/痕 (22%的死亡率).
- 现象群4:主要是女性,死亡率高,症状负担较高 (20%的死亡率).
- 集群算法独立预测了生存率 (C统计值为0.77与0.75,P=0.009在验证中).
结论:
- 机器学习成功地使用全面的CMR和临床数据识别了独特的AR类现象集群.
- 这种表型化方法为精准医学提供了潜力,并提高了AR患者的风险分层.
- 一个独特的,高死亡率的女性AR表型值得进一步调查和专注关注.
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