Validation of machine learning-based risk stratification scores for patients with acute coronary syndrome treated with percutaneous coronary intervention

Abstract

Aims: This study aimed to validate the machine learning-based Global Registry of Acute Coronary Events (GRACE) 3.0 score and PRAISE (Prediction of Adverse Events following an Acute Coronary Syndrome) in patients with acute coronary syndrome (ACS) treated with percutaneous coronary intervention (PCI) for predicting mortality. Methods and results: Data of consecutive patients with ACS treated with PCI in a tertiary centre in the Netherlands between 2014 and 2021 were used for external validation. The GRACE 3.0 score for predicting in-hospital mortality was evaluated in 2759 patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) treated with PCI. The PRAISE score for predicting one-year mortality was evaluated in 4347 patients with ACS treated with PCI. Both risk scores were compared with the GRACE 2.0 score. The GRACE 3.0 score showed excellent discrimination [c-statistic 0.90 (95% CI 0.84, 0.94)] for predicting in-hospital mortality, with well-calibrated predictions (calibration-in-the large [CIL] -0.19 [95% CI -0.45, 0.07]). The PRAISE score demonstrated moderate discrimination [c-statistic 0.75 (95% CI 0.70, 0.80)] and overestimated the one-year risk of mortality [CIL -0.56 (95% CI -0.73, -0.39)]. Decision curve analysis demonstrated that the GRACE 3.0 score offered improved risk prediction compared with the GRACE 2.0 score, while the PRAISE score did not. Conclusion: This study in ACS patients treated with PCI provides suggestive evidence that the GRACE 3.0 score effectively predicts in-hospital mortality beyond the GRACE 2.0 score. The PRAISE score demonstrated limited potential for predicting one-year mortality risk. Further external validation studies in larger cohorts including patients without PCI are warranted.