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

Hayoung Ahn¹, Sungwoo Hur¹, Cheol Hyun Lee²

¹Graduate School of AI, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.

Diagnostics (Basel, Switzerland)

|May 27, 2026

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This summary is machine-generated.

Machine learning models can predict left bundle branch block (LBBB) after transcatheter aortic valve replacement (TAVR) by analyzing complex patient data. This approach aids in identifying key risk factors for better procedural planning.

Area of Science:

Cardiology
Medical Informatics
Artificial Intelligence in Medicine

Background:

Left bundle branch block (LBBB) is a frequent complication post-transcatheter aortic valve replacement (TAVR), linked to poorer patient outcomes.
Predicting LBBB is difficult due to intricate anatomical, procedural, and clinical factors.

Purpose of the Study:

To create a machine learning (ML) framework for predicting LBBB after TAVR.
To identify crucial features contributing to LBBB development.

Main Methods:

A multicenter retrospective analysis of 242 patients undergoing TAVR.
Development of an ML framework using transformer-based feature selection and classifiers.
Model performance assessed via accuracy, precision, recall, F1-score, and AUC with bootstrap validation.

Keywords:

left bundle branch block machine-learning risk stratification transcatheter aortic valve replacement

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Main Results:

A gradient boosting model achieved 78.05% accuracy and a 50.46% F1-score (AUC 0.61).
ML identified key predictors like coronary height, LVOT/annulus ratio, and valve size.
The model highlighted features not typically emphasized in conventional analyses.

Conclusions:

ML-based feature selection effectively captures complex interactions for LBBB risk stratification post-TAVR.
While performance was modest, ML shows promise for personalized TAVR planning.
Further external validation in larger cohorts is recommended.