ArticleFuture cardiology2025
AI-based prediction of left bundle branch block risk post-TAVI using pre-implantation clinical parameters.
Article in Future cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Applications of Artificial Intelligence in Transcatheter Aortic Valve Replacement: A Review of the Literature.Life (Basel, Switzerland) · 2025Review
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4 authors.
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Abstract
BACKGROUND AND
aimsTranscatheter Aortic Valve Implantation (TAVI) has revolutionized the treatment of severe aortic stenosis. Although its clinical efficacy is well established, the development of new-onset left bundle branch block (LBBB) following TAVI remains a frequent and concerning complication. This study aims to develop pre-implantation predictive models for new-onset LBBB after TAVI using both conventional machine learning (ML) algorithms and Large Language Models (LLMs).
methodsOf the 1113 patients who underwent TAVI over a 15-year period, 469 were included after excluding those with preexisting LBBB, pacing rhythm, or missing relevant data. Pre-procedural clinical parameters - such as valve type, valve size, patient demographics, and comorbidities - were analyzed. The dataset was split into training and testing sets. Several ML algorithms were employed, and performance was evaluated using accuracy, precision, and F1 score. Additionally, LLMs (GPT-3.5 and GPT-4) were assessed using Few-Shot and Chain of Thought (CoT) prompting.
resultsNew-onset persistent LBBB occurred in 15.29% of patients. Among ML models, XGBoost performed best. GPT-4 with CoT prompting demonstrated superior predictive performance compared to both conventional ML and GPT-3.5.
conclusionsThe current study establishes a predictive model leveraging pre-implantation parameters to anticipate the occurrence of new-onset left bundle branch block (LBBB) post-Transcatheter Aortic Valve Implantation (TAVI).
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