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Record W4408967093 · doi:10.1001/jamacardio.2025.0848

Finerenone and Atrial Fibrillation in Heart Failure

2025· article· en· W4408967093 on OpenAlexaff
Shingo Matsumoto, Alasdair D Henderson, Pardeep S. Jhund, Johann Bauersachs, Ovidiu Chioncel, Brian Claggett, Josep Comín‐Colet, Akshay S. Desai, Carolyn S.P. Lam, Bertram Pitt, Markus F. Scheerer, James Lay‐Flurrie, Flaviana Amarante, Meike Brinker, Morten Schou, Michele Senni, Sanjiv J. Shah, Adriaan A. Voors, Faiez Zannad, Shelley Zieroth, Muthiah Vaduganathan, Scott D. Solomon, John J.V. McMurray

Bibliographic record

VenueJAMA Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of Manitoba
FundersNational Medical Research CouncilNational Institutes of HealthNorgineNovo NordiskServierUniversity of GlasgowAbiomedImpulse DynamicsAlnylam PharmaceuticalsMedical Research CouncilBayerEli Lilly and CompanyBristol-Myers SquibbAstraZenecaEuropean CommissionAmgenPfizerAmerican Heart Association
KeywordsMedicineAtrial fibrillationHeart failureCardiologyInternal medicineMEDLINE

Abstract

fetched live from OpenAlex

Importance: Heart failure (HF) with mildly reduced or preserved ejection fraction and atrial fibrillation (AF) are closely intertwined. Objective: To examine the efficacy and safety of the nonsteroidal mineralocorticoid receptor antagonist finerenone in patients with HF with mildly reduced or preserved ejection fraction according to the absence or presence of AF and the type of AF (paroxysmal vs persistent or permanent). Design, Setting, and Participants: Prespecified analyses were conducted in the Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients With Heart Failure (FINEARTS-HF) randomized clinical trial. The trial was conducted across 653 sites in 37 countries. Participants were adults aged 40 years and older with symptomatic HF and left ventricular ejection fraction of 40% or greater, randomized between September 2020 and January 2023. Data analysis was conducted from September 1 to October 1, 2024. Intervention: Finerenone (titrated to 20 mg or 40 mg) or placebo. Main Outcomes and Measures: The primary outcome was the composite of total HF events and cardiovascular death. New-onset AF or atrial flutter (AFL) was a prespecified exploratory outcome. Results: Among 5984 patients (mean [SD] age, 72.0 [9.6] years; 2724 [45.5%] female) with known AF status at baseline, 1384 (23.1%) had paroxysmal AF and 1886 (31.5%) had persistent or permanent AF. Patients with both types of AF were older and had worse HF status compared with those without AF (2714 patients [45.4%]). Both types of AF were associated with a higher unadjusted risk of the primary outcome compared with no AF (event rate per 100 person-years of follow-up, 20.3 [95% CI, 17.9-23.1] with paroxysmal AF, 19.8 [95% CI, 17.8-22.0] with persistent or permanent AF, and 11.9 [95% CI, 10.7-13.3] with no AF; rate ratio [RR], 1.62 [95% CI, 1.37-1.92] with paroxysmal AF and 1.66 [95% CI, 1.43-1.93] with persistent or permanent AF vs no AF); however, the associations were attenuated after adjustment for known prognostic variables. The benefit of finerenone on the primary outcome (overall RR, 0.84 [95% CI, 0.74-0.95]) was not modified by baseline AF status (RR, 0.80 [95% CI, 0.65-0.98] with no AF, 0.83 [95% CI, 0.65-1.06] with paroxysmal AF, and 0.85 [95% CI, 0.69-1.05] with persistent or permanent AF; P for interaction = .94). New-onset AF or AFL occurred in 6.5% of patients and was associated with a higher subsequent adjusted risk of the primary outcome (rate ratio, 3.65 [95% CI, 2.57-5.18]; P < .001). The subdistribution hazard ratio for new-onset AF or AFL among those receiving finerenone vs placebo was 0.77 (95% CI, 0.57-1.04; P = .09). Conclusions and Relevance: The efficacy of finerenone was consistent regardless of AF status. New-onset AF was associated with a substantially higher risk of subsequent outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT04435626.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.274
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2025
Admission routes1
Has abstractyes

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