CatLet Score as a Predictor of Cardiac Death in Patients With Acute Myocardial Infarction: Insights From Interpretable Machine Learning Models

Background: Predicting cardiac death in patients with acute myocardial infarction (AMI) remains a major challenge. The Coronary Artery Tree description and lesion evaluation (CatLet) angiographic scoring system can describe the variability in coronary artery anatomy, the degree of stenosis of the affected coronary artery, and the subtended myocardial territory. Therefore, this study aimed to establish an effective and interpretable machine learning (ML) model to explore the relationship between the CatLet score and cardiac death in patients with AMI. Methods: The CatLet score was calculated in 767 consecutively enrolled patients with AMI. Cox regression analysis, Kaplan–Meier survival analysis, and restricted cubic spline analysis were used to explore the association between the CatLet score and cardiac death in patients with AMI. Six ML methods were used to build predictive models. The Shapley Additive Explanations (SHAP) analysis was used to visualize model features and individual case predictions. Results: Compared to the lowest CatLet score of tertile 1, patients with the highest CatLet score (tertile 3) had a higher risk of cardiac death (hazard ratio (HR) = 3.71; 95% confidence interval (CI) = 1.36–10.08; p = 0.010). Restricted cubic spline analysis indicated a linear association between the CatLet score and cardiac death. The ML results showed that the adaptive boosting (Adaboost) model had the most reliable performance with an area under the curve (AUC) of 0.927, a sensitivity of 0.902, and a specificity of 0.796. The SHAP analysis showed that the CatLet score was a significant contributor to the cardiac death outcome. Conclusions: The Catlet score positively correlates with the risk of cardiac death in patients with AMI, while the use of ML modeling can effectively predict the risk of cardiac death.