A newly developed risk prediction model for 10-year ASCVD risk in Japanese adults demonstrated good discrimination (Harrell's C statistic 0.786) and calibration.
Cohort (n=2,454)
Can a newly developed risk prediction model accurately predict the 10-year risk of atherosclerotic cardiovascular disease in Japanese adults without a history of CVD?
A newly developed risk prediction model demonstrated good discrimination and calibration for predicting the 10-year risk of ASCVD in Japanese adults.
Effect estimate: C-statistic 0.786
AIM: To develop and validate a new risk prediction model for predicting the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) in Japanese adults. METHODS: A total of 2,454 participants aged 40-84 years without a history of cardiovascular disease (CVD) were prospectively followed up for 24 years. An incident ASCVD event was defined as the first occurrence of coronary heart disease or atherothrombotic brain infarction. A Cox proportional hazards regression model was used to construct the prediction model. In addition, a simplified scoring system was translated from the developed prediction model. The model performance was evaluated using Harrell's C statistics, a calibration plot with the Greenwood-Nam-D'Agostino test, and a bootstrap validation procedure. RESULTS: During a median of a 24-year follow-up, 270 participants experienced the first ASCVD event. The predictors of the ASCVD events in the multivariable Cox model included age, sex, systolic blood pressure, diabetes, serum high-density lipoprotein cholesterol, serum low-density lipoprotein cholesterol, proteinuria, smoking habits, and regular exercise. The developed models exhibited good discrimination with negligible evidence of overfitting (Harrell's C statistics: 0.786 for the multivariable model and 0.789 for the simplified score) and good calibrations (the Greenwood-Nam-D'Agostino test: P=0.29 for the multivariable model, 0.52 for the simplified score). CONCLUSION: We constructed a risk prediction model for the development of ASCVD in Japanese adults. This prediction model exhibits great potential as a tool for predicting the risk of ASCVD in clinical practice by enabling the identification of specific risk factors for ASCVD in individual patients.
Honda et al. (Thu,) conducted a cohort in Atherosclerotic cardiovascular disease (n=2,454). Risk prediction model was evaluated on Incident ASCVD event (first occurrence of coronary heart disease or atherothrombotic brain infarction) (C-statistic 0.786). A newly developed risk prediction model for 10-year ASCVD risk in Japanese adults demonstrated good discrimination (Harrell's C statistic 0.786) and calibration.