一个基于常规血源指数的机器学习模型,用于在社区中早期预测动脉硬
Liuting Zheng1,2, Xiao Feng2,3, Keyun Wang2
1School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Lipids in health and disease
|February 24, 2026
概括
这项研究开发了一种机器学习模型,使用常规临床数据来预测动脉硬. 泰格腰与身高的比率 (TyG-WHtR) 是一个关键的预测指标,可以在社区中进行早期风险评估.
科学领域:
- 心血管健康 心血管健康
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 动脉硬是心血管疾病的重要危险因素.
- 早期发现动脉硬是预防疾病的关键.
- 基于社区的研究对于了解人口健康趋势至关重要.
研究的目的:
- 用机器学习选影响早期动脉硬的因素.
- 在社区人口中构建动脉硬的风险预测模型.
- 为了确定早期动脉硬的关键预测因素.
主要方法:
- 一项对2948名社区参与者的横截面研究.
- 使用最小绝对收缩和选择操作员 (LASSO) 回归来进行变量选择.
- 采用随机森林和夏普利添加式拓展 (SHAP) 分析用于预测建模.
- 开发了一个用于风险预测的名图,并评估了模型性能.
主要成果:
- 超过一半 (55.5%) 的参与者表现出早期增加的动脉硬.
- 确定了九个独立的风险因素,包括MHR,CRI-I,CRI-II,AI,TyG-WC,TyG-WHtR,NHR,PHR和UHR.
- 随机森林模型确定了年龄,血压,TyG-WHtR和葡萄糖代谢标志物作为顶级预测因素.
- 命名图显示出优异的分辨率 (AUC = 0.877) 和良好的校准.
结论:
- 开发并验证了一种包含七个常规临床指标的实际预测模型.
- 确定TyG-腰与身高比 (TyG-WHtR) 是动脉硬的关键独立预测因素.
- 该模型和视觉工具有助于早期,基于社区的查动脉硬.
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