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开发和可视化代谢综合征风险预测模型:基于中国健康检查数据的纵向队列研究

Wenxi Liu¹, Xiao Tang¹, Tongcheng Cui¹

¹Department of Health Statistics, School of Public Health, Dalian Medical University, Dalian, China.

Frontiers in nutrition

|December 11, 2023

概括

这项研究使用例行体检数据开发了一种可靠的代谢综合征 (MetS) 风险预测模型. 随机生存森林 (RSF) 模型在预测男性和女性的MetS风险方面表现出卓越的性能.

科学领域:

心血管健康心血管健康
预测分析是一种预测分析.
代谢健康代谢健康

背景情况:

代谢综合征 (MetS) 构成了重大的公共卫生挑战,需要有效的风险预测策略.
纵向健康数据为了解疾病进展和开发预测模型提供了宝贵的资源.
目前对MetS的预测模型可能无法完全捕捉随时间推移的生理变化的累积影响.

研究的目的:

为代谢综合征 (MetS) 构建一个实际的风险预测模型.
将身体检查指标的基线和累积暴露纳入.
为 MetS 健康管理提供理论基础.

主要方法:

使用纵向健康检查数据,为男性和女性队列开发了单独的预测模型.
采用了多阶段的变量选择过程,包括单变量考克斯,拉索-考克斯,RSF和逐步的考克斯回归.
使用考克斯比例危险回归和随机生存森林 (RSF) 算法构建和比较预测模型.
在内部和外部使用时间依赖ROC曲线 (tdROC) 和一致性索引 (C-索引) 验证模型.

主要成果:

该RSF模型的表现始终优于考克斯模型,显示出较低的预测误差和较小的集成Brier分数 (IBS).
最终的RSF模型实现了高预测准确性,1年,3年和5年的AUC从0.959到0.991不等.
内部和外部验证证实了开发的RSF模型的良好预测能力和稳定性,C指数高于0.7.

结论:

基于RSF的MetS风险预测模型比传统的Cox模型更稳定和可靠.
该模型有效地利用例行体检指标,在各种数据集上表现出强的表现.
开发的模型具有在MetS健康管理中临床应用的巨大潜力.

关键词:

纵向数据纵向数据纵向数据代谢综合征代谢综合征随机生存森林随机生存森林风险预测模型的风险预测模型这就是Cox比例危险回归.

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