Zhu Wenqing, Tan Shuoyuan, Zhou Zhitong, Zhao Miaomiao, Wang Yingquan, Li Qi, Zheng Yang, Shi Jianwei
Tongji University School of Medicine, Tongji University, Shanghai, People's Republic of China.
School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, People's Republic of China.
Clin Interv Aging. 2025 Jun 12;20:825-836. doi: 10.2147/CIA.S519546. eCollection 2025.
Cardiovascular diseases (CVD) represents a leading cause of morbidity and mortality worldwide, including China. Accurate prediction of CVD risk and implementation of preventive measures are critical. This study aimed to develop a short-term risk prediction model for CVD events among individuals aged ≥60 years in Shanghai, China.
Stratified random sampling recruited elderly individuals. Retrospective data (2016-2022) were analyzed using Lasso-Cox regression, followed by a multivariable Cox regression model. The risk scoring was visualized through a nomogram, and the model performance was assessed using calibration plots and receiver operating characteristic curves.
A total of 9,636 individuals aged ≥60 years were included. The Lasso-Cox regression analysis showed male gender (HR=1.482), older age (HR=1.035), higher body mass index (HR=1.015), lower high-density lipoprotein cholesterol (HR=0.992), higher systolic blood pressure (HR=1.009), lower diastolic blood pressure (HR=0.982), higher fasting plasma glucose (HR=1.068), hypertension (HR=1.904), diabetes (HR=1.128), and lipid-lowering medication (HR=1.384) were related to higher CVD risk. The C-index in the training and validation data was 0.642 and 0.623, respectively. Calibration plots indicated good agreement between predicted and actual probabilities.
This short-term predictive model for CVD events among the elderly population exhibits good accuracy but moderate discriminative ability. More studies are warranted to investigate predictors (gender, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, hypertension, and lipid-lowering medication) of CVD incidence for the development of preventive measures.
心血管疾病（CVD）是包括中国在内的全球发病和死亡的主要原因。准确预测心血管疾病风险并实施预防措施至关重要。本研究旨在建立中国上海60岁及以上人群心血管疾病事件的短期风险预测模型。
采用分层随机抽样招募老年人。使用Lasso-Cox回归分析回顾性数据（2016 - 2022年），随后建立多变量Cox回归模型。通过列线图直观显示风险评分，并使用校准图和受试者工作特征曲线评估模型性能。
共纳入9636名60岁及以上个体。Lasso-Cox回归分析显示，男性（HR = 1.482）、年龄较大（HR = 1.035）、体重指数较高（HR = 1.015）、高密度脂蛋白胆固醇较低（HR = 0.992）、收缩压较高（HR = 1.009）、舒张压较低（HR = 0.982）、空腹血糖较高（HR = 1.068）、高血压（HR = 1.904）、糖尿病（HR = 1.128）以及降脂药物治疗（HR = 1.384）与心血管疾病风险较高相关。训练数据和验证数据的C指数分别为0.642和0.623。校准图表明预测概率与实际概率之间具有良好的一致性。
该老年人群心血管疾病事件短期预测模型具有良好的准确性，但判别能力中等。需要更多研究来调查心血管疾病发病率的预测因素（性别、高密度脂蛋白胆固醇、收缩压、舒张压、高血压和降脂药物治疗），以制定预防措施。
