| Title: | Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach |
| Journal: | npj Digital Medicine |
| Published: | 4 Dec 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41345458/ |
| DOI: | https://doi.org/10.1038/s41746-025-02159-z |
| URL: | https://www.nature.com/articles/s41746-025-02159-z_reference.pdf |
Publication 16747
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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.</p>
16 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 48666 | Electrophysiological remodelling in patients at elevated risk of ventricular arrhythmia and sudden cardiac death |
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