根据分层集群分析,确定与中高风险肺栓塞患者的预后相关的疾病亚型,以分层集群分析为基础
Lihui Zhang1, Xincai Wang1, Xiaobin Pan1
1Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Affiliated Provincial Hospital of Fuzhou University, Fuzhou, China.
Journal of thoracic disease
|October 29, 2025
概括
机器学习在中高风险肺栓塞 (PE) 患者中发现了不同的预后亚型. 这种方法增强了风险分层,以改善患者管理和PE病例的结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 肺栓塞 (PE) 是心血管死亡的一个重要原因.
- 目前欧洲心脏病学会 (ESC) 的指导方针缺乏针对中高风险PE的量身定制管理.
- 现有的PE风险分层方法需要改进.
研究的目的:
- 改善中高风险PE患者的风险分层.
- 在中高风险PE中使用机器学习识别不同的患者子组.
- 分析已识别的子组的预后影响.
主要方法:
- 从两个中心的79名中高风险PE患者的回顾性分析.
- 使用平均方法进行无监督的分层集群分析.
- 使用了11个标准化的连续变量进行集群.
主要成果:
- 通过聚合层次的分类,通过聚合层次的分类确定了三个不同的集群.
- 在集群中观察到年龄,并发症和死亡率的统计学上显著差异.
- 集群3呈现出最差的预后,其特点是更长的ICU停留时间,更高的成本,以及1年和3年PE死亡率的增加.
结论:
- 在中高风险PE中,分层聚类成功识别了预后性疾病亚型.
- 基于机器学习的分类为完善PE风险分层提供了一个有前途的工具.
- 进一步的研究可以利用这些发现来定制患者管理策略.
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