Evidence map›Paper›PMID 41345458›Full record

ArticleNPJ digital medicine2025

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

Mehak Gurnani, Konstantinos Patlatzoglou, Joseph Barker, Libor Pastika, Boroumand Zeidaabadi, Ibrahim Antoun, Riyaz Somani, G Andre Ng, Paolo Inglese, Lara Curran and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Mehak GurnaniNational Heart and Lung Institute, Imperial College London, London, UK.
Konstantinos PatlatzoglouNational Heart and Lung Institute, Imperial College London, London, UK.
Joseph BarkerNational Heart and Lung Institute, Imperial College London, London, UK.
Libor PastikaNational Heart and Lung Institute, Imperial College London, London, UK.
Boroumand ZeidaabadiNational Heart and Lung Institute, Imperial College London, London, UK.
Ibrahim AntounDepartment of Cardiovascular Sciences, Leicester British Heart Foundation Centre of Research Excellence, Leicester National Institute for Health and Care Research Biomedical Research Centre, University of Leicester, Leicester, UK.
Riyaz SomaniDepartment of Cardiovascular Sciences, Leicester British Heart Foundation Centre of Research Excellence, Leicester National Institute for Health and Care Research Biomedical Research Centre, University of Leicester, Leicester, UK.
G Andre NgDepartment of Cardiovascular Sciences, Leicester British Heart Foundation Centre of Research Excellence, Leicester National Institute for Health and Care Research Biomedical Research Centre, University of Leicester, Leicester, UK.
Paolo IngleseMRC Laboratory of Medical Sciences, Imperial College London, London, UK.
Lara CurranMRC Laboratory of Medical Sciences, Imperial College London, London, UK.
Declan O'ReganMRC Laboratory of Medical Sciences, Imperial College London, London, UK.
Nicholas S PetersNational Heart and Lung Institute, Imperial College London, London, UK.
Daniel B KramerRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Jonathan W WaksHarvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Arunashis SauNational Heart and Lung Institute, Imperial College London, London, UK. arunashis.sau09@imperial.ac.uk.
Fu Siong NgNational Heart and Lung Institute, Imperial College London, London, UK. f.ng@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID41345458
PMCPMC12722375

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.