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针对当代中国人的初级预防心血管疾病风险预测模型 (1°P-CARDIAC):使用混合统计和机器学习方法的模型导出和验证
Yekai Zhou1,2, Celia Jiaxi Lin3, Qiuyan Yu2,4
1School of Computing and Data Science, The University of Hong Kong, Hong Kong Special Administration Region, China.
PloS one
|July 28, 2025
概括
一个新的心血管疾病 (CVD) 风险预测模型,1°P-CARDIAC被开发用于中国人群的初级预防. 该模型显示了与现有工具相比更好的性能,有助于对高风险个体的早期干预.
科学领域:
- 心脏病学 心脏病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 是全球和中国的主要死亡原因.
- 缺少专门针对中国人群的验证的初级预防模型.
- 早期识别高风险个体对于预防心血管疾病至关重要.
研究的目的:
- 为中国个人创建和验证一次性预防风险预测模型.
- 为了确定早期心血管疾病干预的高风险个体.
- 将新型模型的性能与现有的风险预测工具进行比较.
主要方法:
- 开发并验证了中国 (1°P-CARDIAC) 模型的个性化心血管疾病风险评估,使用香港的导出和验证队列.
- 采用XGBoost考克斯模型和多变量归算与链式方程 (MICE) 用于模型导出和处理缺失的数据.
- 将1°P-CARDIAC与PREDICT,聚合队列方程 (PCE),中国-PAR和Framingham (亚洲) 模型使用引导验证进行比较.
主要成果:
- 这项研究包括了近130万名来自衍生和验证队列的患者.
- 1°P-CARDIAC完整模型表现出强的性能 (C统计:0.87) 和校准 (斜率:0.94).
- 基本的1°P-CARDIAC模型也表现良好 (C-统计:0.75,斜率:0.91),表现优于现有的模型 (C-统计:0.680.72).
结论:
- 1°P-CARDIAC是一种经过验证的CVD风险预测模型,用于中国人群的初级预防,采用混合机器学习方法.
- 该模型与常用的风险模型相比,显示出更高的性能.
- 1°P-CARDIAC提供了有效性和多功能性,有可能改善心血管疾病初级预防中的公共卫生结果.
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