Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.