Development of a machine-learning-based model for predicting major adverse cardiovascular events in patients with chronic total occlusion undergoing percutaneous coronary intervention

AIM  To develop a clinical prediction model using machine learning to identify risk factors for major adverse cardiovascular event (MACE) after chronic total occlusion percutaneous coronary intervention (CTO-PCI), so as to enhance the clinical decision-making capabilities.

METHODS We included 843 CTO patients who underwent PCI treatment at Xijing Hospital from 2018 to 2021. Patient data included basic information, medical history, in-hospital MACE events, preoperative examinations and intervention details. All patients were followed up for one year to record MACE events. Patients were divided into training (80%) and validation (20%) datasets. Variables were selected using LASSO regression and a total of 10 machine learning models were trained and evaluated using 5-fold cross-validation. The Random Forest method was ultimately chosen for the prediction model. Model performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves and Brier scores. Feature importance analysis and SHapley Additive exPlanations (SHAP) value dependence plots were used to detail the contribution and impact of key features on the model’s predictions.

RESULTS Based on the results of the LASSO regression analysis, a total of 16 predictor variables were included in the model construction. Among all the models built, the random forest model performed the best, with an AUC of 0.759 and a Brier score of 0.120. Feature importance analysis revealed that RBC (red blood cell count), D-Dimer, PLT (platelet count), operation time and age are the key features in predicting the occurrence of MACE within one year after CTO-PCI. SHAP value dependence plots further illustrated the specific impacts of these features on the model’s prediction results.

CONCLUSION This study has successfully constructed a random forest model to predict the occurrence of MACE within one year after CTO-PCI. The prognostic model effectively identifies high-risk patients post-CTO-PCI, which aids in clinical decision-making and reducing the risk of MACE.