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<p>Atrial fibrillation (AF) is classically categorised by arrhythmia duration, but these subtypes have limitations in capturing mechanistic and prognostic diversity. A variational autoencoder, trained on &gt;1.1M ECGs, extracted representative features, filtered for an AF cohort of 20,291 unique patients. These features were input into an unsupervised tree-based clustering method to map AF heterogeneity as a tree structure and identify phenogroups. Five phenogroups stratified by future disease risk were identified: (1) higher-risk AF; (2) highest-risk AF with heart failure (HF); (3) average paroxysmal AF; (4) lower-risk paroxysmal AF; and (5) higher-risk paroxysmal AF. The tree trajectory positioned individuals based on shared traits, emphasising explainability. Paroxysmal phenogroups 4 and 5 differed in risk and ventricular structure, with phenogroup 5 exhibiting more adverse features. Mixed AF phenogroup 2 reflected advanced AF with greater HF burden and mortality risk. This AI-ECG framework augments AF subtypes with a risk-based dimension, supporting personalised care.</p>

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Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Mehak Gurnani,
  • Konstantinos Patlatzoglou,
  • Joseph Barker,
  • Libor Pastika,
  • Boroumand Zeidaabadi,
  • Ibrahim Antoun,
  • Riyaz Somani,
  • G. Andre Ng,
  • Paolo Inglese,
  • Lara Curran,
  • Declan O’Regan,
  • Nicholas S. Peters,
  • Daniel B. Kramer,
  • Jonathan W. Waks,
  • Arunashis Sau,
  • Fu Siong Ng

摘要

Atrial fibrillation (AF) is classically categorised by arrhythmia duration, but these subtypes have limitations in capturing mechanistic and prognostic diversity. A variational autoencoder, trained on >1.1M ECGs, extracted representative features, filtered for an AF cohort of 20,291 unique patients. These features were input into an unsupervised tree-based clustering method to map AF heterogeneity as a tree structure and identify phenogroups. Five phenogroups stratified by future disease risk were identified: (1) higher-risk AF; (2) highest-risk AF with heart failure (HF); (3) average paroxysmal AF; (4) lower-risk paroxysmal AF; and (5) higher-risk paroxysmal AF. The tree trajectory positioned individuals based on shared traits, emphasising explainability. Paroxysmal phenogroups 4 and 5 differed in risk and ventricular structure, with phenogroup 5 exhibiting more adverse features. Mixed AF phenogroup 2 reflected advanced AF with greater HF burden and mortality risk. This AI-ECG framework augments AF subtypes with a risk-based dimension, supporting personalised care.