Background and aims: Predicting cardiovascular events is of practical benefit for disease prevention. The aim of this study was to develop and evaluate an updated risk prediction model for cardiovascular diseases and its subtypes. Methods: A total of 2462 community residents aged 40–84 years were followed up for 24 years. A Cox proportional hazards regression model was used to develop risk prediction models for cardiovascular diseases, and separately for stroke and coronary heart diseases. The risk assessment ability of the developed model was evaluated, and a bootstrapping method was used for internal validation. The predicted risk was translated into a simplified scoring system. A decision curve analysis was used to evaluate clinical usefulness. Results: The multivariable model for cardiovascular diseases included age, sex, systolic blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking habits, and regular exercise as predictors. The models for stroke and coronary heart diseases incorporated both shared and unique variables. The developed models showed good discrimination with little evidence of overfitting (optimism-corrected Harrell's C statistics 0.726–0.777) and calibrations (Hosmer-Lemeshow test, p = 0.44–0.90). The decision curve analysis revealed that the predicted risk-based decision-making would have higher net benefit than either a CVD intervention strategy for all individuals or no individuals. Conclusions: The developed risk prediction models showed a good performance and satisfactory internal validity, which may help understand individual risk and setting personalized goals, and promote risk stratification in public health strategies for CVD prevention.
|Number of pages||7|
|Publication status||Published - Dec 2018|
All Science Journal Classification (ASJC) codes
- Cardiology and Cardiovascular Medicine