早发冠心病危险因素分析及列线图模型构建-现代医学

早发冠心病危险因素分析及列线图模型构建

单位：1. 广州中医药大学第八临床医学院, 广东佛山 528000;
2. 佛山市中医院心血管内科, 广东佛山 528000

分类号：R541.4

出版年·卷·期（页码）：2025·53·第四期（522-529）

摘要：

目的： 探讨早发冠心病(PCAD)的危险因素并构建相关的列线图模型。方法： 收集2023年1月至2024年7月佛山市中医院收治的111例早发冠心病患者(男63例，女48例)作为PCAD组，并按照性别进行频数匹配，纳入同期在该院诊疗的222例非冠心病患者(男126例，女96例)作为非PCAD组。对比两组患者的一般资料、实验室指标及甘油三酯葡萄糖指数(TyG指数)。采用Lasso回归筛选变量，并将筛选出的变量纳入Logistic回归模型。绘制列线图模型，并通过受试者工作特征(ROC)曲线及其曲线下面积(AUC)和C指数评估模型的区分度。进行Hosmer-Lemeshow (H-L)拟合优度检验并绘制校准曲线以评价校准度，同时绘制临床决策曲线以分析模型的临床适用性。结果： 心电图异常、心脏彩超异常、高血压病病史、低密度脂蛋白胆固醇(LDL-C)、空腹血糖(FBG)是预测PCAD的危险因素(P<0.05)。基于危险因素成功构建预测PCAD的列线图模型。训练集的C指数为0.926(95%CI 0.888~0.965)，验证集为0.940(95%CI 0.893~0.987)。ROC曲线分析显示，组合模型预测PCAD具有优秀区分度，训练集AUC为0.926(95%CI 0.888~0.964)；验证集AUC为0.949(95%CI 0.906~0.992)。H-L检验结果显示良好，训练集结果： χ²=3.104 1，P=0.928；验证集结果： χ²=2.020 1，P=0.980。训练集和验证集的实际曲线和校正曲线的轨迹均具有较强一致性。决策曲线分析显示，组合模型具有良好的临床适用性，训练集在0.00~0.90概率内,组合模型净收益>0；验证集在0.00~0.94概率内，组合模型净收益>0。结论： 心电图异常、心脏彩超异常、高血压病病史、LDL-C、FBG为早发冠心病的危险因素，基于上述因素建立的列线图模型具有良好的预测价值。

Objective: To investigate the risk factors for premature coronary artery disease(PCAD) and develop a nomogram model. Methods: A total of 111 PCAD patients(63 males, 48 females) admitted to Foshan Hospital of Traditional Chinese Medicine between January 2023 and July 2024 were enrolled as the PCAD group. A control group(non PCAD group) of 222 non-coronary heart disease patients(126 males, 96 females) was frequency-matched by sex. General characteristics, laboratory indices, and the TyG index were compared between the two groups. Variables were screened using LASSO regression, and a Logistic regression model was constructed to develop a nomogram model. Discriminative ability was evaluated via receiver operating characteristic(ROC) curves, area under the curve(AUC), and the concordance index(C-index). Calibration was assessed using the Hosmer-Lemeshow test and calibration curves. Clinical utility was analyzed through decision curve analysis(DCA). Results: Independent risk factors for PCAD included electrocardiogram(ECG) abnormalities, echocardiographic abnormalities, hypertension history, LDL-C, and fasting blood glucose(FBG)(P<0.05). The nomogram demonstrated excellent discrimination in both the training set(C-index: 0.926, 95% CI 0.888-0.965; AUC: 0.926, 95% CI 0.888-0.964) and validation set(C-index: 0.940, 95% CI 0.893-0.987; AUC: 0.949, 95%CI 0.906-0.992). The Hosmer-Lemeshow test indicated strong calibration(training set: χ²=3.104 1, P=0.928; validation set: χ²=2.020 1, P=0.980), with calibration curves showing close alignment between predicted and observed probabilities. DCA confirmed clinical applicability, with net benefits>0 across risk thresholds of 0.00-0.90(training set) and 0.00-0.94(validation set). Conclusion: ECG abnormalities, echocardiographic abnormalities, hypertension history, LDL-C, and FBG are significant risk factors for PCAD. The nomogram model based on these factors exhibits robust predictive performance and clinical utility.

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