A machine learning model for predicting ischemic and bleeding risk after percutaneous coronary intervention: Development and external validation

Fig 1. Study design and analytical workflow.

Fig 2. Receiver operating characteristic (ROC) curves comparing discrimination performance of the weighted LightGBM model, logistic regression, PRECISE-DAPT score, and DAPT score in the external validation cohort. (A) Prediction of composite ischemic events. (B) Prediction of major bleeding events. Curves depict sensitivity versus 1-specificity across all classification thresholds. The diagonal reference line represents no discrimination. AUROC values are shown for each model.

Fig 3. Explainability of the primary AI model using SHAP-based feature attribution. (A) displays SHAP summary plots for prediction of ischemic events. (B) displays SHAP summary plots for prediction of bleeding events. Features are ranked by their mean absolute contribution to model predictions, with each point representing an individual patient instance. Positive SHAP values indicate increased predicted risk, while negative values indicate reduced predicted risk. The direction and magnitude of feature effects demonstrate clinical plausibility and consistency with established cardiovascular risk factors.

Fig 4. Calibration and clinical utility of the primary AI model. (A) shows calibration of the weighted LightGBM model, comparing predicted risk with observed event rates across deciles of predicted probability. The dashed line represents ideal calibration. (B) presents decision curve analysis comparing the net clinical benefit of the AI model with the PRECISE-DAPT and DAPT scores, as well as treat-all and treat-none strategies, across clinically relevant risk thresholds. Results demonstrate good calibration and consistent net benefit of the AI-based approach in external validation.

Fig 5. Time-to-event analysis and subgroup consistency of AI-guided DAPT personalization. (A) Displays Kaplan-Meier curves comparing event-free survival between AI-guided and guideline-based DAPT strategies. Kaplan-Meier curves comparing projected event-free survival under AI-guided versus guideline-based strategies. (B) Forest plot showing projected hazard ratios across predefined clinical subgroups derived from model-based simulations.