GPT-4 with Chain of Thought prompting demonstrated superior predictive performance for new-onset left bundle branch block post-TAVI (overall incidence 15.29%) compared to conventional ML and GPT-3.5.
Observational (n=469)
Can AI-based predictive models using pre-implantation clinical parameters accurately predict new-onset left bundle branch block post-TAVI?
GPT-4 with Chain of Thought prompting outperforms conventional machine learning models in predicting new-onset LBBB after TAVI using pre-implantation clinical parameters.
BACKGROUND AND AIMS: Transcatheter 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). METHODS: Of 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. RESULTS: New-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. CONCLUSIONS: The 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).
Cheilas et al. (Tue,) conducted a observational in Severe aortic stenosis undergoing TAVI (n=469). Machine learning and Large Language Models (GPT-3.5, GPT-4) vs. Conventional ML algorithms was evaluated on New-onset persistent left bundle branch block (LBBB). GPT-4 with Chain of Thought prompting demonstrated superior predictive performance for new-onset left bundle branch block post-TAVI (overall incidence 15.29%) compared to conventional ML and GPT-3.5.