A Transformer-Based Machine Learning Framework for Risk Stratification of Left Bundle Branch Block After...

Background/Objectives: Left bundle branch block (LBBB) remains a common complication after transcatheter aortic valve replacement (TAVR) and is associated with adverse clinical outcomes. However, accurate prediction of LBBB remains challenging due to the complex interactions among the anatomical, procedural, and clinical factors. This study aimed to develop a machine learning (ML)-based framework to predict LBBB and identify relevant contributing features. Methods: In this multicenter retrospective study, we analyzed 242 patients undergoing TAVR across three institutions. A machine learning framework incorporating transformer-based feature selection and conventional classifiers was developed. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Internal validation was performed using bootstrap resampling. Results: The gradient boosting model using ML-derived features demonstrated the most balanced performance, achieving an accuracy of 78.05% and an F1-score of 50.46%, with modest discrimination (AUC 0.61). The ML-based approach identified clinically relevant features, including coronary height, left ventricular outflow tract/annulus ratio, and prosthetic valve size, as well as additional variables not emphasized in conventional analyses. Conclusions: ML-based feature selection can capture complex feature interactions beyond traditional statistical approaches and provide clinically meaningful insights into risk stratification for LBBB after TAVR. Although predictive performance was modest, this approach highlights the potential of ML for improved risk stratification and individualized procedural planning. Further large-scale external validation is warranted.