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Related Concept Videos

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

898
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
898

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Related Experiment Video

Updated: Jun 4, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of

Charbel Noujaim1, Han Feng1, Ghassan Bidaoui1

  • 1Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.

Journal of Cardiovascular Electrophysiology
|December 24, 2024
PubMed
Summary

Machine learning identified a high-risk group for catheter ablation failure in persistent atrial fibrillation. This group has older age, higher fibrosis, elevated BMI, and larger left atrial volume, indicating a need for personalized treatment strategies.

Keywords:
atrial fibrillationcatheter ablationclinical

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Area of Science:

  • Cardiology
  • Medical Informatics

Background:

  • Persistent atrial fibrillation (AF) catheter ablation success rates are limited by patient heterogeneity.
  • Identifying distinct patient phenotypes post-ablation can optimize treatment and patient selection.

Purpose of the Study:

  • To identify distinct patient phenotypes in persistent AF undergoing catheter ablation.
  • To improve patient selection for therapies and optimize treatment strategies.

Main Methods:

  • Utilized Gradient Boosting Method and K-medoids cluster analysis on DECAAF II trial data.
  • Identified key features for arrhythmia recurrence prediction: LA volume, BMI, baseline fibrosis, and age.
  • Validated findings using a randomly selected subset of centers.

Main Results:

  • Two patient clusters were identified based on key features.
  • Cluster 1 (high-risk) exhibited higher rates of atrial arrhythmia recurrence (51.7% vs. 35.0%) compared to Cluster 2.
  • Key differentiating factors for high-risk cluster: older age, high LA fibrosis, elevated BMI, and increased LA volume.

Conclusions:

  • Machine learning successfully identified a high-risk cluster for persistent AF catheter ablation failure.
  • This high-risk phenotype is characterized by specific demographic and cardiac structural factors.
  • Findings support tailored treatment approaches for persistent AF patients undergoing ablation.