Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41345458.
- Also identified by DOI 10.1038/s41746-025-02159-z and PMC identifier 12722375.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.