AIMS: This study aimed to develop a cardiovascular disease (CVD) risk model using data from a large occupational cohort. METHODS: A risk prediction model was developed using the routine health checkup data of 96,117 Japanese employees (84.0% men) who were 30-64 years of age and had no CVD at baseline. Cox proportional hazards regression models were employed to develop a risk model for assessing the 10-year CVD risk. Measures of discrimination and calibration were used to assess the predictive performance of the model and internal validation was used to examine potential overfitting. RESULTS: During a mean follow-up period of 6.7 years (range, 0.1-11.0 years), 422 cases of incident CVD were confirmed. The final model, which included predictor variables of age, smoking, diabetes, systolic blood pressure, and low- and high-density lipoprotein cholesterol levels, demonstrated a good predictive ability (Harrell's C-statistic, 0.796
95% confidence interval, 0.775-0.817) with excellent calibration between observed and predicted values. Internal validation revealed minimal overfitting. CONCLUSIONS: The developed model can accurately predict the 10-year CVD risk. Because it is based on routine health checkup data, the prediction model can be easily implemented in the workplace. Further studies are required to assess the external validity and transferability of the proposed CVD risk model.