Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal: Journal of cardiovascular electrophysiology
PMID: 39716785

Abstract

INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis could improve patient selection for additional therapies and optimize treatment strategies.

Authors

  • Charbel Noujaim
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Han Feng
    Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong. Electronic address: hanfeng@cityu.edu.hk.
  • Ghassan Bidaoui
    Tulane Research Innovation for Arrhythmia Discovery, Tulane University School of Medicine, New Orleans, Louisiana.
  • Chao Huang
    University of North Carolina, Chapel Hill, NC, USA.
  • Hadi Younes
    Tulane Research Innovation for Arrhythmia Discovery, Tulane University School of Medicine, New Orleans, Louisiana.
  • Ala Assaf
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Mario Mekhael
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Nour Chouman
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Chanho Lim
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Eoin Donnellan
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Ghaith Shamaileh
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Abdel Hadi El Hajjar
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Daniel Nelson
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Aneesh Dhore
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.
  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.
  • Nassir Marrouche
    Tulane Research Innovation for Arrhythmia Discovery, Tulane University School of Medicine, New Orleans, Louisiana. Electronic address: nmarrouche@tulane.edu.
  • Omar Kreidieh
    Tulane University School of Medicine, Department of Cardiology, Tulane Research Innovation for Arrhythmia Discovery, New Orleans, Louisiana, USA.

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