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Updated: May 2, 2026
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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
Published on: July 29, 2011
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Non-Invasive Prediction of Atrial Fibrosis Using a Regression Tree Model of Mean Left Atrial Voltage
Javier Ibero1,2, Ignacio García-Bolao1, Gabriel Ballesteros3
1Cardiology and Cardiac Surgery Department, Clínica Universidad de Navarra, Avenida Pio XII 36, 31008 Pamplona, Spain.
Biomedicines
|August 28, 2025
Summary
Machine learning models can now predict atrial fibrosis non-invasively. Key predictors include left atrial volume and emptying fraction, offering a new way to assess atrial cardiomyopathy.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Atrial fibrosis contributes to atrial cardiomyopathy.
- Invasive assessment of atrial fibrosis via mean left atrial voltage (MLAV) limits clinical use.
- Machine learning (ML) offers potential for non-invasive MLAV prediction.
Purpose of the Study:
- To develop and validate a non-invasive ML model for predicting MLAV.
- To identify key clinical and echocardiographic predictors of MLAV.
- To improve characterization of atrial fibrosis as a continuum.
Main Methods:
- Prospective study of 113 atrial fibrillation (AF) patients undergoing pulmonary vein isolation (PVI).
- Ultra-high-density voltage mapping (uHDvM) for MLAV estimation.
- Regression tree model (CART algorithm) using clinical and echocardiographic data.
Main Results:
- ML regression tree model showed moderate predictive accuracy (R² = 0.63).
- Indexed minimum left atrial (LA) volume and passive emptying fraction were most influential predictors.
- No significant difference in AF recurrence-free survival based on MLAV or model groups.
Conclusions:
- A novel ML-based regression tree model non-invasively predicts MLAV.
- Minimum LA volume and passive emptying fraction are key predictors.
- This model provides an accessible tool for characterizing atrial cardiomyopathy and guiding therapy.
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