Qiu Yahui, Guo Qifeng, Feng Xuejuan, Xiao Weiqiang, Liang Shisen, Wei Mei
Department of Heart Center, The First Hospital of Hebei Medicical University, 89Donggang Road, Yuhua District, Shijiazhuang, 050000, Hebei, China.
Graduate School of Hebei Medicical University, 361 Zhongshan East Road, Shijiazhuang, 050000, Hebei, China.
Sci Rep. 2025 Aug 11;15(1):29410. doi: 10.1038/s41598-025-14589-6.
Due to the cardioprotective effects of estrogen, premenopausal women have a relatively lower risk of developing coronary artery disease (CAD). However, the incidence of CAD in premenopausal women has been increasing in recent years. Therefore, the aim of this study is to develop a clinical prediction model to estimate the risk of CAD in premenopausal women. This study included premenopausal women who underwent coronary angiography at the First Hospital of Hebei Medical University from September 2018 to December 2021. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to identify the optimal variables for predicting the risk of CAD in premenopausal women. A nomogram was then constructed using multivariate logistic regression analysis. Finally, the predictive performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUROC), its calibration performance was assessed using calibration curves, and clinical net benefit was evaluated using Decision Curve Analysis (DCA). A total of 222 premenopausal women were ultimately included for analysis, of whom 86 were diagnosed with CAD. Through LASSO and multivariate logistic regression, five predictive variables were finally selected: age, diabetes mellitus (DM), aspartate transaminase (AST), alkaline phosphatase (ALP), and lipoprotein (a) (Lp(a)). These five variables were used to construct a prediction model, which was presented in the form of a nomogram. The calibration curves of the nomogram showed good fit. The area under the receiver operating characteristic curve (AUROC) for the nomogram was 0.819 (95%CI: 0.760-0.878). Additionally, decision curve analysis (DCA) indicated that the nomogram can achieve good net benefit in clinical applications.
由于雌激素具有心脏保护作用，绝经前女性患冠状动脉疾病（CAD）的风险相对较低。然而，近年来绝经前女性CAD的发病率一直在上升。因此，本研究的目的是建立一个临床预测模型，以估计绝经前女性患CAD的风险。本研究纳入了2018年9月至2021年12月在河北医科大学第一医院接受冠状动脉造影的绝经前女性。采用最小绝对收缩和选择算子（LASSO）回归方法来确定预测绝经前女性CAD风险的最佳变量。然后使用多因素逻辑回归分析构建列线图。最后，使用受试者操作特征曲线下面积（AUROC）评估列线图的预测性能，使用校准曲线评估其校准性能，并使用决策曲线分析（DCA）评估临床净效益。最终共纳入222例绝经前女性进行分析，其中86例被诊断为CAD。通过LASSO和多因素逻辑回归，最终选择了五个预测变量：年龄、糖尿病（DM）、天冬氨酸转氨酶（AST）、碱性磷酸酶（ALP）和脂蛋白（a）[Lp（a）]。这五个变量用于构建预测模型，并以列线图的形式呈现。列线图的校准曲线显示拟合良好。列线图的受试者操作特征曲线下面积（AUROC）为0.819（95%CI：0.760 - 0.878）。此外，决策曲线分析（DCA）表明，列线图在临床应用中可实现良好的净效益。
