Background The PRAISE (Prediction of Adverse Events Following an Acute Coronary Syndrome) score is a machine-learning-based model for predicting 1-year all-cause death, myocardial infarction, and Bleeding Academic Research Consortium (BARC) type 3/5 bleeding. Its utility in an unselected Asian population undergoing percutaneous coronary intervention for acute coronary syndrome remains unknown. We aimed to validate the PRAISE score in a real-world Asian population. Methods and Results A total of 6412 consecutive patients undergoing percutaneous coronary intervention for acute coronary syndrome were prospectively included. The PRAISE scores were compared with established scoring systems (GRACE [Global Registry of Acute Coronary Events] 2.0, PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy), and PARIS [Patterns of Non-Adherence to Anti-Platelet Regimen in Stented Patients]) to evaluate their discrimination, calibration, and reclassification. The risk of all-cause mortality (hazard ratio [HR], 12.24 [95% CI, 5.32-28.15]) and recurrent acute myocardial infarction (HR, 3.92 [95% CI, 1.76-8.73]) was greater in the high-risk group than in the low-risk group. The C-statistics for death, myocardial infarction, and major bleeding were 0.75 (0.67-0.83), 0.61 (0.52-0.69), and 0.62 (0.46-0.77), respectively. The observed to expected ratio of death, myocardial infarction, and major bleeding was 0.427, 0.260, and 0.106, respectively. Based on the decision curve analysis, the PRAISE score displayed a slightly greater net benefit for the 1-year risk of death (5%-10%) than the GRACE score did. Conclusions The PRAISE score showed limited potential for risk prediction in our validation cohort with acute coronary syndrome. As a result, new prediction models or model refitting are required with improved discrimination and accuracy in risk prediction.
Keywords: PRAISE score; acute coronary syndrome; machine learning; risk stratification.