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Related Experiment Video

Updated: Jun 13, 2026

Upper-extremity Approach for Secondary Access in Transfemoral Transcatheter Aortic Valve Implantation
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Upper-extremity Approach for Secondary Access in Transfemoral Transcatheter Aortic Valve Implantation

Published on: August 8, 2025

Machine Learning-Driven Probability of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.

Marcel Abras1,2, Daniela Bursacovschi3, Ecaterina Pasat2

  • 1Department of Cardiology, Nicolae Testemitanu State University of Medicine and Pharmacy, MD-2004 Chisinau, Moldova.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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Machine learning accurately predicts permanent pacemaker implantation (PPI) risk after transcatheter aortic valve replacement (TAVR). Anatomical factors, not clinical history, are key predictors, aiding procedural planning.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Permanent pacemaker implantation (PPI) is a frequent complication after transcatheter aortic valve replacement (TAVR).
  • Predicting PPI risk is crucial for optimizing procedural planning and patient management.
  • Accurate risk stratification can improve outcomes for patients undergoing TAVR.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting PPI risk post-TAVR.
  • To identify key predictors of PPI using explainable AI (XAI) methods.
  • To enhance clinical decision-making for TAVR procedures.

Main Methods:

  • Prospective study of 179 patients undergoing TAVR (2019-2025).
  • Development of an ML model using clinical, echocardiographic, and imaging data.
Keywords:
TAVRpermanent pacemaker implantationprediction model

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Last Updated: Jun 13, 2026

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  • Evaluation of model performance using accuracy, F1-score, and confusion matrix.
  • Application of SHapley Additive exPlanations (SHAP) for feature importance analysis.

    • Main Results:

    • The ML model achieved high predictive performance (accuracy=0.944, F1-score=0.947).
    • Anatomical variables (valve size, sinus diameter, annulus diameter) were primary predictors of PPI.
    • Baseline clinical factors (e.g., LVEF, MI history) had a lower predictive impact.

    Conclusions:

    • Machine learning models can reliably predict PPI risk following TAVR.
    • Aortic root anatomy and prosthesis characteristics are critical determinants of PPI.
    • Explainable AI enhances risk assessment and supports procedural planning for TAVR.