Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Abstract

Aims: Death due to cardiovascular diseases (CVD) increased significantly in China. One way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those at risk of CVD and provide targeted intervention. Methods and Results: We conducted a retrospective cohort study of 2331 Chinese males without prior CVD to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008-2016) and one revisits record by September 2019. With the full cohort, we used single factor cox regression to examine each candidate predictor adjusted for age. 16 sequential prediction models were built on significant predictors. CVDMCM was selected based on the Akaike information criterion, the area under the ROC curve, and the percentage of variation in outcome values explained by the model (R2). This model, the Framingham CVD risk model, and Wu simplified model were all validated by bootstrapping with 1000 repetitions. CVDMCM C statistics (0.779, 95% CI: 0.733-0.825), D statistic (4.738, 95% CI: 3.270-6.864), and calibration plot demonstrated that CVDMCM outperformed the other two models. Conclusions: We developed and validated CVDMCM, which predicted 4-year CVD risk for Chinese males with better performance than the Framingham CVD model and Wu simplified model. In addition, we developed a web calculator for physicians to conveniently generate CVD risk scores and identify those with a higher risk of CVD. We believe CVDMCM had great potential for clinical usage.