Evidence map›Paper›PMID 40074560›Full record

ArticleESC heart failure2025

Atrial fibrillation development in the heart failure population from nationwide British linked electronic health records.

Hiroyuki Yoshimura, Nikhil Paliwal, Arturo Gonzalez-Izquierdo, Chris Finan, Amand Floriaan Schmidt, Gregory Y H Lip, Rui Providencia

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in ESC heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. 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

7 authors.

Hiroyuki YoshimuraInstitute of Health Informatics Research, University College London, London, UK.ORCID https://orcid.org/0009-0009-1991-5001
Nikhil PaliwalInstitute of Cardiovascular Science, University College London, London, UK.
Arturo Gonzalez-IzquierdoInstitute of Health Informatics Research, University College London, London, UK.
Chris FinanInstitute of Cardiovascular Science, University College London, London, UK.
Amand Floriaan SchmidtInstitute of Cardiovascular Science, University College London, London, UK.
Gregory Y H LipLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Rui ProvidenciaInstitute of Health Informatics Research, University College London, London, UK.

Funding

British Heart Foundation PG/18/5033837British Heart Foundation PG/22/10989Horizon 2020 899871National Institute of Health Research NIHR129463University College London British Heart Foundation Research Accelerator AA/18/6/34223
6 · The paper itself

Abstract

aimsAtrial fibrillation (AF) is a frequent comorbidity in heart failure (HF). We analysed factors associated with new-onset atrial fibrillation in patients with heart failure using linked real-world UK data from primary and secondary care, along with findings from genome-wide association studies. METHODS AND

resultsAmong 163 174 participants with a diagnosis of HF (January 1998 to May 2016) from Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES), 111 595 participants had no previous history of AF (mean age 76.3 ± 12.6; 50.3% women; 95.8% white ethnicity). Multivariate weighted Cox regression was used to identify predictors for new-onset AF. Linkage disequilibrium score regression was performed to assess the strength of the genetic correlation between AF and identified predictors. During follow-up (median 1.33 years, IQR 0.15-4.18), the incidence rate for AF was 2.8% at 30 days, 9.9% at 1 year, 18.0% at 3 years, and 24.9% at 5 years after HF diagnosis after HF diagnosis. Female sex (HR = 0.79, 95% CI 0.71-0.88), age (HR = 1.04, 95% CI 1.04-1.04), white ethnicity (HR = 1.30, 95% CI 1.06-1.59), social deprivation (HR = 1.20, 95% CI 1.01-1.42), BMI (HR = 1.01, 95% CI 1.00-1.02), gentle physical activity (HR = 0.84, 95% CI 0.72-0.97), hypertension (HR = 1.15, 95% CI 1.03-1.29), chronic kidney disease (HR = 1.15, 95% CI 1.06-1.24), chronic obstructive pulmonary disease (COPD) (HR = 1.10, 95% CI 1.01-1.19) and valvular heart disease (HR = 1.17, 95% CI 1.06-1.29) were associated with new-onset AF. Angiotensin-converting enzyme inhibitors were associated with lower AF incidence (HR = 0.88, 95% CI 0.80-0.96), and the magnitude of effect was dependent on the duration of administration. Linkage disequilibrium score regression showed important genetic correlation between AF and HF (rg = 0.57, P = 2.30 × 10

conclusionsIncident AF in the HF population is high, with good genetic correlation for the two conditions. Identified predictors for new-onset AF might be helpful to improve management of HF patients and AF prevention.

Indexed as

Atrial FibrillationElectronic Health RecordsHeart FailureAgedAged, 80 and overComorbidityFemaleFollow-Up StudiesHumansIncidenceMaleRetrospective StudiesRisk FactorsUnited KingdomAtrial fibrillationElectronic health recordGenome‐wide association studyHeart failureRisk factors

Identifiers

PMID40074560
PMCPMC12287870

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.