Table 2.
Performance of risk prediction models in the test cohort
| Algorithms | Overall | Discrimination | Calibration | Clinical Usefulness | |
|---|---|---|---|---|---|
| Brier | MCC | AUC (95%CI) | Hosmer–Lemeshow χ2 ( p value) | Net benefit at threshold of 5% | |
| PCE (White) | 0.045 | 0.186 | 0.777 (0.733–0.821) | 37.3 (p < 0.01) | 0.013 |
| China-PAR | 0.043 | 0.191 | 0.780 (0.737–0.822) | 67.6 (p < 0.001) | 0.016 |
| RePCE (White) | 0.057 | 0.194 | 0.779 (0.734–0.825) | 126.6 (p < 0.001) | 0.016 |
| ReChina-PAR | 0.043 | 0.193 | 0.780 (0.737–0.822) | 18.6 (p < 0.05) | 0.017 |
| ANN | 0.041 | 0.218 | 0.800 (0.759–0.838) | 9.1 (p = 0.33) | 0.017 |
| RF | 0.042 | 0.181 | 0.759 (0.713–0.804) | 12.6 (p = 0.13) | 0.011 |
| GBM | 0.042 | 0.193 | 0.774 (0.727–0.820) | 12.1 (p = 0.15) | 0.013 |
| KNN | 0.042 | 0.175 | 0.767 (0.723–0.811) | 34.7 (p < 0.01) | 0.015 |
| Adaboost | 0.051 | 0.163 | 0.727 (0.679–0.775) | 136.4 (p < 0.001) | 0.010 |
| SVM | 0.043 | 0.145 | 0.697 (0.642–0.752) | 4.0 (p = 0.86) | 0.009 |
| Catboost | 0.041 | 0.206 | 0.787 (0.745–0.830) | 10.2 (p = 0.25) | 0.015 |
Abbreviations: ASCVD atherosclerotic cardiovascular disease, CI confidence interval, MCC Matthews correlation coefficient, AUC area under the receiver operating characteristic curve, PCE Pooled Cohort Equations, China-PAR Prediction for ASCVD Risk in China, RePCE Recalibrated PCE, ReChina-PAR Recalibrated China-PAR, ANN Artificial Neural Network, RF Random Forest, GBM Gradient Boosting Machine, KNN K Nearest Neighbor, Adaboost Adaptive Boosting, SVM Support Vector Machine, Catboost Categorical Boosting