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Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Prasanth Ganesan1, Maxime Pedron1, Ruibin Feng1

  • 1Division of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).

Circulation. Arrhythmia and Electrophysiology
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

Identifying patients for atrial fibrillation (AF) ablation success is challenging. Machine learning reveals distinct phenotypes for acute versus long-term outcomes, highlighting the need for better predictors of sustained arrhythmia freedom.

Keywords:
atrial fibrillationelectrocardiographyhearthumanslogistic models

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Predicting patient response to atrial fibrillation (AF) ablation is difficult.
  • AF ablation outcomes can be inconsistent, with some patients achieving long-term freedom despite initial procedural failure.

Purpose of the Study:

  • To use machine learning to identify distinct phenotypes for acute and long-term AF ablation success.
  • To differentiate the physiological factors influencing immediate versus sustained outcomes.

Main Methods:

  • Analysis of 72 features from 561 AF patients, including electrograms, ECG, cardiac structure, lifestyle, and clinical data.
  • Comparison of six machine learning models to predict acute and long-term ablation endpoints.
  • Shapley explainability analysis to define patient phenotypes and external validation in 77 AF patients.

Main Results:

  • The 1-year success rate was 69.5%, with a poor correlation between acute termination (49.6%) and long-term success.
  • Machine learning models showed higher predictive power for acute termination (AUC=0.86) than long-term outcomes (AUC=0.67).
  • Long-term success phenotypes were linked to clinical/lifestyle factors, while acute termination phenotypes reflected electrical features.

Conclusions:

  • Acute and long-term responses to AF ablation are driven by distinct clinical and electrical physiology.
  • The dissociation of phenotypes suggests potential roles for factors like attenuated AF progression in long-term success.
  • Developing reliable procedural predictors for long-term AF ablation success remains a critical unmet need.