机器学习在患有大动脉反的患者中识别出临床上明显的表型
Brototo Deb1, Christopher G Scott2, Hector I Michelena3
1Department of Medicine, Georgetown University, Washington, District of Columbia; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
概括
无监督集群确定了三组不同的中度至重度大动脉反 (AR) 患者群体,揭示了生存率的显著差异. 这些发现有助于完善AR患者的风险分层.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 数据科学数据科学数据科学
背景情况:
- 大动脉反 (AR) 是一种常见的门疾病,具有长期无症状的阶段.
- 心肌过载的新型标记物正在出现,用于评估AR中脱补偿的发作.
- 了解患者分层对于管理AR进展至关重要.
研究的目的:
- 采用无监督的集群分析来识别慢性中度至重度AR的不同临床和心声图患者集群.
- 评估这些已识别的集群与全因死亡率之间的关联.
- 评估聚类在完善AR患者风险分层中的实用性.
主要方法:
- 对1100名中度至重度慢性AR患者的回顾性分析.
- 利用统一的多重近似和投影 (UMAP) 与k-means用于集群临床和心声数据.
- 采用多重归算缺少的数据和存活率分析的日志等级测试.
主要成果:
- 出现了三个不同的患者群体,显示出明显不同的生存结果 (日志等级P < .001).
- 集群1 (年轻的男性,严重的AR,较少的症状) 的生存率最好 (75.6%).
- 集群3 (严重的症状性AR) 在5年内表现出最低的存活率 (45.3%).
结论:
- 在慢性中度至重度的AR中,存在具有不同心声学特征和死亡风险的不同患者群体.
- 识别这些集群可以改善个体风险分层和临床决策.
- 需要进一步的前性研究来验证这些发现.
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