Evidence map›Paper›PMID 41314690›Full record

ArticleOpen heart2025

Cardiac magnetic resonance imaging-derived atrial fibrosis in patients with pre-atrial fibrillation.

Ali Wahab, Ramesh Nadarajah, Raluca Tomoaia, Wasim Javed, Catherine Reynolds, Sheena Bennet, Asad Bhatty, Gregory Y H Lip, John Camm, Jianhua Wu and 3 more

Abstract read
In one paragraph

Article in Open heart, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Ali WahabLeeds Institute of Data Analytics, University of Leeds, Leeds, England, UK A.Wahab@leeds.ac.uk.ORCID http://orcid.org/0009-0001-6245-6260
Ramesh NadarajahLeeds Institute of Data Analytics, University of Leeds, Leeds, England, UK.
Raluca TomoaiaLeeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Wasim JavedLeeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Catherine ReynoldsLeeds Institute of Data Analytics, University of Leeds, Leeds, England, UK.
Sheena BennetDepartment of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Asad BhattyLeeds Institute of Data Analytics, University of Leeds, Leeds, England, UK.
Gregory Y H LipLiverpool Heart & Chest Hospital, Liverpool, UK.
John CammCity St George's University of London, London, UK.
Jianhua WuQueen Mary University of London Barts and The London School of Medicine and Dentistry, London, UK.ORCID http://orcid.org/0000-0001-6093-599X
Sven PleinLeeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Peter SwobodaLeeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Chris P GaleLeeds Institute of Data Analytics, University of Leeds, Leeds, England, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAtrial fibrosis identified on cardiac magnetic resonance (CMR) imaging has been proposed as a preprocedural imaging biomarker for patient selection for rhythm control interventions in patients with atrial fibrillation (AF). Whether atrial fibrosis is present in patients considered as 'pre-AF' is unknown. METHODS AND

resultsWe prospectively recruited 12 participants with pre-AF as defined by the Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF machine learning algorithm, without AF diagnosed during AF screening, and compared them to 25 participants with confirmed AF. All participants underwent CMR using a 3T system with left atrial fibrosis quantification and ADAS-3D left atrial image postprocessing software. Participants with pre-AF had smaller left atrial end-systolic (33.6±9.8 vs 43.0±17.0, p=0.003) and end-diastolic (16.5±8.7 vs 28.2±14.4, p=0.007) volumes, and higher left atrial ejection fraction (59.6±14.6 vs 40.7±17.5, p=0.005) than participants with AF. The extent of atrial fibrosis was not different between those with pre-AF and AF (borderzone (%) 5.2±5.0 vs 2.9±6.9, p=0.772; borderzone fibrosis (cm) 6.2±5.8 vs 6.8±10.7, p=0.927).

conclusionCMR identifies atrial fibrosis before manifest AF in patients with pre-AF as defined by a machine learning algorithm.

Indexed as

Atrial FibrillationAtrial Function, LeftAtrial RemodelingHeart AtriaMagnetic Resonance Imaging, CineAgedFemaleFibrosisHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesAtrial FibrillationElectronic Health RecordsMagnetic Resonance Imaging

Identifiers

PMID41314690
PMCPMC12666032

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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.