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Development of a Cardiovascular Disease Risk Prediction Model Using the Suita Study, a Population-Based Prospective
Michikazu Nakai1, Makoto Watanabe2, Yoshihiro Kokubo2
1Center for Cerebral and Cardiovascular Disease Information, National Cerebral and Cardiovascular Center.
Aim:
To construct a risk prediction model for cardiovascular disease (CVD) based on the Suita study, an urban Japanese cohort study, and compare its accuracy against the Framingham CVD risk score (FRS) model.
Methods:
After excluding participants with missing data or those who lost to follow-up, this study consisted of 3,080 men and 3,470 women participants aged 30-79 years without CVD at baseline in 1989-1999. The main outcome of this study was incidence of CVD, defined as the incidence of stroke or coronary heart disease. Multivariable Cox proportional hazards models with stepwise selection were used to develop the prediction model. To assess model performance, concordance statistics (C-statistics) and their 95% confidence intervals (CIs) were calculated using a bootstrap procedure. A calibration test was also conducted.
Results:
During a median follow-up period of 16.9 years, 351 men and 241 women developed CVD. We formulated risk models with and without electrocardiogram (ECG) data that included age, sex, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, diabetes mellitus, smoking, and urinary protein as risk factors. The C-statistics of the Suita CVD risk models with ECG data (0.782; 95% CI, 0.766-0.799) and without ECG data (0.781; 95% CI, 0.765-0.797) were significantly higher than that of the FRS model (0.768; 95% CI, 0.750-0.785).
Conclusions:
The Suita CVD risk model is feasible to use and improves predictability of the incidence of CVD relative to the FRS model in Japan.
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