Prognostic Value of Machine‐Learning‐Based PRAISE Score for Ischemic and Bleeding Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Abstract

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.