Development and Validation of a Risk Prediction Model for Atherosclerotic Cardiovascular Disease in Japanese Adults: The Hisayama Study

Abstract

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.

Keywords: Atherosclerosis; Cardiovascular disease; Japanese; Prediction model; Risk factors.

Supplemental Fig.1.

Flow chart of the study sample

Supplemental Fig.2. Selection rate of the candidate predictors in 200 bootstrapping samples

The solid black bar indicates variables that selected for ＞40% (i.e., ＞80 times) of the bootstrapping resampling procedure. Abbreviations: HDL cholesterol, high-density lipoprotein cholesterol; LDL cholesterol, low-density lipoprotein cholesterol.

Fig.1. Simplified point-based scoring system for atherosclerotic cardiovascular disease

The predicted probability was determined using the following formula: $\hat{\mathrm{P}}$ =1−0.9696 ^{exp([total score＋points for age]×0.144−2.4767)} , where the points of 0, 5, 11, 16, and 20 for age were assigned to the age ranges of 40-49, 50-59, 60-69, 70-79, and ≥ 80 years, respectively. Probabilities are presented in green (low risk: ＜2.0% of the 10-year atherosclerotic cardiovascular disease risk, corresponding to the lowest 35% of the distribution in the population), yellow (middle risk: 2.0%–10.0%), and red (high risk: ≥ 10%, corresponding to the highest 20% of the distribution in the population). ^＊ In the alternative simplified score that included serum non-HDL cholesterol instead of serum LDL cholesterol, the points for the predefined categories of serum non-HDL cholesterol (＜150, 150–169, 170–189, and ≥ 190 mg/dL) were 0, 1, 2, and 3, respectively.

Supplemental Fig.3. Histogram of the 10-year ASCVD probabilities predicted by the simplified risk score

Bars were color-coded as green (low-risk: ＜2.0% of 10-year atherosclerotic cardiovascular disease risk, corresponding to the lowest 35% of distribution in the population), yellow (middle-risk: 2.0%–10.0%), and red (high-risk: ≥ 10%, corresponding to the highest 20% of distribution in the population)

Fig.2. Calibration plots of the predicted 10-year probability of atherosclerotic cardiovascular disease predicted by the multivariable model (A) and the simplified scoring system (B)

The dotted lines indicate the case of perfect calibration, corresponding to an intercept of zero and a slope of one for the calibration plot. The solid curves indicate the calibration curve fit by the loess smoother. Bars indicate the 95% confidence intervals of the observed probability in each group of the predicted probability. Abbreviation: GND, Greenwood-Nam-D’Agostino.

Supplemental Fig.4.

Agreement between the final multivariable model using serum LDL cholesterol and the alternative model using serum non-HDL cholesterol as a predictor

Supplemental Fig.5.

Agreement between the simplified risk score using serum LDL cholesterol and the alternative simplified score using serum non-HDL cholesterol as a predictor