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Atrial fibrillation and exercise capacity in patients with and without heart failure during exercise-based cardiac rehabilitation

2025· article· en· W7127691343 on OpenAlexaff
J Riess, E Alba Schmidt, E Di Carluccio, Gloria Petrasch, M Albus, Sonja Sichler, P Sartori, Greta Hametner, H Brito Da Silva, S J Foulkes, J Vontobel, M J Haykowsky, D Niederseer

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEjection fractionHeart failureAtrial fibrillationRehabilitationHemodynamicsRetrospective cohort studyCohortCardiac function curveCardiac output

Abstract

fetched live from OpenAlex

Abstract Background/Introduction Atrial fibrillation (AF) impairs exercise tolerance and exacerbates hemodynamic limitations in cardiac patients. The coexistence of AF and heart failure (HF) further compromises hemodynamic function during exercise. The impact of AF on changes in peak exercise aerobic power (VO2peak) and aerobic endurance (6 minute walk distance test, 6MWT) after completing an exercise-based cardiac rehabilitation (EBCR) remains unclear. Purpose This study examined the association between AF and VO2peak in patients with and without AF, both overall and within an HF subgroup. Methods This retrospective single-center cohort study included patients who underwent EBCR at a specialized in-patient rehabilitation center between December 2022 and June 2024. Patients were eligible if they completed a 6MWT at both admission and discharge. Estimated VO₂peak (ml/min/kg) was derived from maximal power output and METs obtained during exercise stress test and adjusted using spiroergometry results in a subset of patients. The corresponding regression equation VO₂/kg_est_cor = (0.0446 * 6MWT) – 1.8981 was applied for further analyses. HF was defined as a left ventricular ejection fraction (LVEF) <50% at admission. To analyze the association between AF and VO2, we performed linear regression adjusted for age and sex. Results A total of 744 patients were analyzed, including 172 (23%) with a history of AF. The overall median age was 65 years (57–72 years), 78% male, and the median length of stay was 20 days (20–27 days). Median left ventricular ejection fraction (LV-EF) was 55.0% (47–60%) in patients without AF and 52.0% (45–59) with AF. Among 203 HF patients, 53/203 (26%) had AF. At admission, estimated VO₂peak tended to be lower among patients with AF (14.2 ml/kg/min [10-17]) compared to those without AF (15.6 ml/min/kg [12–20]) (Figure 1), and a similar trend was seen in HF patients with AF (14.2 ml/kg/min [9-17]) vs those without AF (16.2 ml/min/kg [11–20]). All groups improved VO2peak to a similar extent over the course of CBCR, such that at discharge, estimated VO₂peak in patients without HF and AF was 21.9 ml/min/kg (18–25; median difference: 5.9 ml/kg/min [4–8]) and 21.4 ml/min/kg (18–25; median difference: 7.0 ml/kg/min [5–10]) in AF patients. In HF patients, discharge VO₂ was 21.9 ml/min/kg (17–25; median difference: 5.5 ml/kg/min [3–8]) and 19.5 ml/min/kg (16–25; median difference: 6.3 ml/kg/min [4–9]) in patients with HF and AF. AF was associated with a significantly lower VO₂ at admission (-0.97 ml/kg/min, p=0.039), showed no significant effect at discharge (p=0.499) and was linked to a greater VO₂ improvement during rehabilitation (+1.25 ml/kg/min, p<0.001). Conclusion Patients with AF tend to have lower estimated VO₂peak at admission, both in the overall cohort and in HF patients. All groups showed clinically relevant improvements in exercise capacity over the course of EBCR, with slightly greater progress in AF patients.Estimated VO₂peak violin plots

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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
Has abstractyes

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