对心血管结果预测心肺运动测试的综合解释:一种机器学习方法
Nicholas Cauwenberghs1, Josephine Sente1, Hanne Van Criekinge1
1Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Sciences, University of Leuven, 3000 Leuven, Belgium.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
心肺运动测试 (CPET) 现象组有效地确定了男性的心血管风险. 这种方法可以提高不同患者群体的风险分层和风险管理.
科学领域:
- 心脏病学 心脏病学
- 运动生理学 运动生理学
- 预防医学 预防医学
背景情况:
- 心血管 (CV) 风险评估可以通过整合心肺运动测试 (CPET) 数据来改进.
- 来自CPET指标的患者现象组可能为CV事件提供了增强的预测价值.
研究的目的:
- 使用CPET总结指标识别患者现象组.
- 评估这些现象组对发生的CV事件的预测价值.
主要方法:
- 无监督集群 (高斯混合模型) 应用于2280名接受最大CPET的患者.
- 根据10个关键CPET指数定义了四个男性和四个女性现象组.
- 事件CV事件被跟踪在5.3年的中位数随访.
主要成果:
- 现象组在临床和螺旋测量特征上有显著差异.
- 在男性中,特定的现象组 (3和4) 显示出显著更高的CV事件风险 (HR 1.51-2.19).
- 在女性中,在同变量调整后,心血管风险的表组差异并不显著.
结论:
- 基于CPET的综合性表观分组在男性患者中有效地分层了心血管风险.
- CPET 现象映射可以帮助全面解释 CPET 结果,并指导 CV 风险管理.
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