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Record W4410482517 · doi:10.1093/eurjpc/zwaf236.056

Exercise capacity and hemodynamics in heart failure with preserved ejection fraction patients with and without atrial fibrillation

2025· article· en· W4410482517 on OpenAlexaff
Stephen Foulkes, Sara Ferreira, Maurício Milani, Youri Bekhuis, Maarten Falter, Sarah Stroobants, S Joghani, Sibel Altintas, Rūta Jasaitytė, Jan Stassen, Lieven Herbots, Guido Claessen, Mark J. Haykowsky, Jan Verwerft

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAtrial fibrillationCardiologyEjection fractionInternal medicineHeart failureHemodynamicsHeart failure with preserved ejection fractionStroke volume

Abstract

fetched live from OpenAlex

Abstract Background Atrial fibrillation (AF) is a common comorbidity in individuals with heart failure with preserved ejection fraction (HFpEF) that contributes to increased morbidity and mortality. However, the impact of AF on key HFpEF physiologic features, including exercise tolerance (peak oxygen uptake, VO2peak) hemodynamic responses, and peripheral oxygen extraction remains unclear. Purpose To compare VO2peak, peak exercise hemodynamics and arterio-venous oxygen difference (a-vO2diff) in patients with HFpEF with or without AF. Methods The study cohort included patients referred to a multi-disciplinary unexplained dyspnea clinic with an established HFpEF diagnosis after detailed clinical and hemodynamic evaluation. Patients were sub-grouped based on whether they were in AF (HFpEF-AF; n=88) or sinus rhythm (HFpEF-SR; n=625) at the time of the evaluation. Peak VO2, exercise hemodynamics and a-vO2diff were assessed from maximal cardiopulmonary exercise testing with simultaneous echocardiography (CPETecho). AF and SR sub-groups were compared using ANCOVA with adjustment for age and sex. Results The HFpEF-AF group was slightly older (76±6yrs vs 73±8yrs, P<0.001), with similar (P>0.2 for all) body mass index (28.2±4.8kg/m2 vs 29.6±11.2kg/m2), resting left-ventricular (LV) ejection fraction (61±8 vs 62±8) and LV mass index (92±36g/m2 vs 93±25g/m2) but higher indexed left-atrial volume (47±15mL/m2 vs 32±13mL/m2, P<0.001) and median [interquartile range] NT-proBNP levels (1200ng/L [805, 1750] vs 290ng/L [140, 530], P<0.001). The HFpEF-AF group also had a higher proportion of males (42% vs 55%, P=0.023), but similar prevalence of hypertension (75% vs 79%, P=0.43) and diabetes (22% vs 22%, P=0.91). Both groups showed comparable peak effort during CPETecho (peak respiratory exchange ratio: HFpEF-AF=1.06±0.11 vs HFpEF-SR=1.07±0.12, P=0.25). Peak VO2 was 9% lower in HFpEF-AF (P=0.027), accompanied by a 10% reduction in peak cardiac output (P=0.007), despite a 12% higher heart rate (P<0.001). Stroke volume was 19% lower in HFpEF-AF, related to smaller left ventricular end-diastolic volume (12% lower, P=0.001), which decreased further during exercise (5% decrease vs. 3% increase in HFpEF-SR, interaction P<0.001). In contrast, the a-vO2diff was not different between groups (P=0.94). HFpEF-AF and HFpEF-SR also showed similar resting (17.8±4.1mmHg vs 17.2±4.1 mmHg, P=0.29) and peak exercise mean pulmonary artery pressures (mPAP; 32.2±6.6mmHg vs 32.7±7.0mmHg, P=0.48), but the mPAP-CO slope tended to be 14% higher in HFpEF-AF (Fig. 1, P=0.086). Conclusions Patients with HFpEF in AF have a lower peak oxygen uptake and reduced end-diastolic volume, stroke volume and cardiac output reserve compared to patients with HFpEF in sinus rhythm. These findings highlight differences in HFpEF exercise physiology associated with AF, suggesting a potential need for tailored therapeutic approaches to optimize their functional outcomes.Fig. 1Impact of AF on exercise responsesFig. 2Impact of AF on LV responses

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)0.001
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 score0.009
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.229
Teacher spread0.221 · 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".

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Citations0
Published2025
Admission routes1
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