Si Fei, Liu Qian, Yu Jing
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
BMC Geriatr. 2025 Jan 11;25(1):27. doi: 10.1186/s12877-025-05679-1.
Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients. Model performance was comprehensively assessed using discrimination, calibration, and clinical decision curves.
After a 7-year follow-up of 934 older hypertensive patients, 243 individuals (26.03%) developed heart disease. Older hypertensive patients with baseline comorbid dyslipidemia, chronic pulmonary diseases, arthritis or rheumatic diseases faced a higher risk of future heart disease. Feature selection significantly improved predictive performance compared to the original variable set. The ROC-AUC for logistic regression, XGBoost, and DNN were 0.60 (95% CI: 0.53-0.68), 0.64 (95% CI: 0.57-0.71), and 0.67 (95% CI: 0.60-0.73), respectively, with logistic regression achieving optimal calibration. XGBoost demonstrated the most noticeable clinical benefit as the threshold increased.
Machine learning effectively identifies the risk of heart disease in older hypertensive patients based on data from the CHARLS cohort. The results suggest that older hypertensive patients with comorbid dyslipidemia, chronic pulmonary diseases, and arthritis or rheumatic diseases have a higher risk of developing heart disease. This information could facilitate early risk identification for future heart disease in older hypertensive patients.
构建老年高血压患者心脏病发生的预测模型，旨在实现早期风险识别。
纳入了来自中国健康与养老追踪调查（China Health and Retirement Longitudinal Study）的934名60岁及以上参与者，并进行了7年随访（2011 - 2018年）。采用机器学习方法（逻辑回归、XGBoost、深度神经网络）构建预测高血压患者心脏病风险的模型。使用区分度、校准度和临床决策曲线对模型性能进行综合评估。
对934名老年高血压患者进行7年随访后，243人（26.03%）患心脏病。基线合并血脂异常、慢性肺部疾病、关节炎或风湿性疾病的老年高血压患者未来患心脏病的风险更高。与原始变量集相比，特征选择显著提高了预测性能。逻辑回归、XGBoost和深度神经网络的受试者工作特征曲线下面积（ROC - AUC）分别为0.60（95%置信区间：0.53 - 0.68）、0.64（95%置信区间：0.57 - 0.71）和0.67（95%置信区间：0.60 - 0.73），其中逻辑回归实现了最佳校准。随着阈值增加，XGBoost显示出最显著的临床益处。
机器学习基于中国健康与养老追踪调查（CHARLS）队列的数据有效识别老年高血压患者的心脏病风险。结果表明，合并血脂异常、慢性肺部疾病以及关节炎或风湿性疾病的老年高血压患者患心脏病的风险更高。这些信息有助于早期识别老年高血压患者未来患心脏病的风险。
