May 1, 2025SHILAP Revista de lepidopterología10 citationsOpen Access

Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study

Structured PICO

Does an ML-based multimodal model predict 12-month major adverse cardiovascular and cerebrovascular events risk in individuals with symptomatic aortic stenosis and HFpEF following TAVR?

Population

Individuals with symptomatic aortic stenosis (AS) and heart failure with preserved ejection fraction (HFpEF) following transcatheter aortic valve replacement (TAVR)

Intervention

Machine learning-based multimodal model incorporating 8 readily accessible predictors

Outcome

12 months of major adverse cardiovascular and cerebrovascular events (MACCE) riskcomposite

An ML-based multimodal model using 8 predictors can effectively identify high-risk individuals with symptomatic AS and HFpEF after TAVR, potentially aiding in personalized risk stratification.

Abstract

Our ML-based multimodal model, incorporating 8 readily accessible predictors, demonstrated robust predictive capability for 12 months of major adverse cardiovascular and cerebrovascular events risk. This model can be used to identify high-risk individuals with AS and HFpEF following TAVR, potentially aiding in risk stratification and personalized treatment strategies.

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