对ST段升高心肌梗塞患者的临床,血管学和实验室参数进行集群分析
Oğuzhan Birdal1, Emrah İpek2, Mehmet Saygı3
1Department of Cardiology, Ataturk University, Erzurum, 25240, Turkey. droguzhanbirdal@gmail.com.
Lipids in health and disease
|June 4, 2024
概括
层次聚合集群 (HAC) 在ST段升高心肌梗塞 (STEMI) 中确定了两个不同的患者群. 集群2显示死亡风险明显高,这表明HAC的死亡风险明显高.
科学领域:
- 心脏病学 心脏病学
- 医疗保健中的机器学习
- 生物统计学 生物统计学
背景情况:
- ST段升高心肌梗塞 (STEMI) 是冠状动脉疾病的严重表现.
- 准确的风险分层对于指导STEMI治疗和出院计划至关重要.
- 层次聚合集群 (HAC) 为患者分类提供了一种新的机器学习方法.
研究的目的:
- 研究HAC在将STEMI患者分为不同的表型集群中的实用性.
- 为了比较确定的患者群体之间的临床结果,特别是死亡率.
主要方法:
- 分析了2015年至2023年间诊断的3205名STEMI患者的队列.
- 应用了等级聚合集群 (HAC) 来将患者分为两个不同的集群.
- 通过使用统计分析 (chi-square,log-rank,Cox回归) 将结果,包括死亡率,与分组进行了比较.
主要成果:
- STEMI患者被分为第1组 (2731名患者) 和第2组 (474名患者).
- 与集群1 (5.4%) 相比,集群2的死亡率显著高 (23%) (P < 0.01).
- 生存分析和考克斯回归证实,即使根据年龄和性别进行调整,在集群2中死亡风险大幅增加 (HR = 3.51,P < 0.001).
结论:
- 层次聚合集群 (HAC) 有效地将STEMI患者分类为具有不同预后的组.
- HAC方法显示出作为STEMI患者一个月死亡率的预测工具的潜力.
- 这种机器学习方法可以帮助完善STEMI的风险评估和治疗策略.
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