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心血管疾病风险分层:对集群分析和传统预测模型的比较分析
Diego Yacaman Mendez1,2,3, Minhao Zhou2,1, Boel Brynedal1,2
1Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
European journal of preventive cardiology
|January 15, 2025
概括
集群分析为心血管疾病 (CVD) 风险分层提供了可比的替代方案,识别了更多高风险个体. 虽然它显示出对心血管疾病事件的高灵敏度和负预测值,但需要进一步验证.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 预防心血管疾病 (CVD) 需要准确的风险分层.
- 现有的基于回归的模型可能缺乏对外部群体的概括性.
- 对心血管疾病风险评估的新方法对于有效的初级预防至关重要.
研究的目的:
- 评估集群分析作为心血管疾病 (CVD) 风险分层的新方法.
- 将集群分析衍生模型的性能与已建立的心血管疾病风险预测模型 (SCORE2,PCE,PREVENT) 进行比较.
主要方法:
- 在5.2年的时间里,分析了3416名以前没有心血管疾病的人群.
- 使用基于心血管疾病风险因素的集群分析开发了一个风险分层模型.
- 与SCORE2,PCE和PREVENT模型相比,模型性能使用灵敏度,特异性,PPV,NPV和C统计进行评估.
主要成果:
- 高风险集群显示59.0%的灵敏度和96.9%的CVD预测的NPV.
- 与SCORE2,PCE和PREVENT相比,集群分析模型具有更高的灵敏度和NPV,但具有较低的特异性和PPV.
- 在集群分析模型和现有模型之间没有观察到C统计的显著差异.
结论:
- 集群分析提供了与已建立的CVD风险模型相比较的性能.
- 集群分析方法确定了一个更大的高风险群体,捕获了更多患有心血管疾病的人,尽管错误阳性增加了.
- 建议在不同的队列中进行进一步的研究,以验证集群分析对心血管疾病风险分层的临床实用性.
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