The impact of atrial fibrillation on oxygen uptake and haemodynamics in patients with heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Aims Atrial fibrillation (AF) may exacerbate exercise intolerance and haemodynamic limitations in individuals with heart failure (HF). Therefore, we performed a systematic search and meta-analysis to quantify the impact of AF on exercise tolerance (peak oxygen uptake, VO2peak; primary outcome) and exercise haemodynamics (secondary outcomes) in patients with HF. Methods and results PubMed, Scopus, and Web of Science databases were systematically searched for articles from inception to June 2024. Studies were included if they: (i) examined participants with HF; (ii) compared participants with AF to those not in AF (i.e. sinus rhythm); (iii) measured VO2peak from expired gas analysis. A fixed effects meta-analysis was performed, with groups compared using the weighted average effect size, represented as the weighted mean difference (WMD) with 95% confidence intervals (95% CI). Of 573 identified studies, 16 met the full inclusion comparing VO2peak in HF-patients in AF [HF-AF; n = 1,271, 68% male, 67 years, left ventricular ejection fraction (LVEF): 41%], and HF in sinus rhythm (HF-SR; n = 4910; 62% male, 62 years, LVEF: 41%). VO2peak was significantly lower in HF-AF (WMD: −1.55mL/kg/min, 95%-CI: −1.81 to −1.28, n = 6471). This coincided with a slightly lower peak heart rate (WMD: −2.94 b/min, 95%-CI: −4.76 to −1.13 b/min, n = 5115), decreased O2pulse (WMD: −1.58 mL/beat, 95% CI: −1.90 to −1.26, n = 3049), and lower systolic blood pressure (WMD: −11.11 mmHg, 95% CI: −14.01 to −8.21, n = 2409). Conclusion In patients with HF, AF is associated with greater VO2peak impairment, potentially due to reduced stroke volume and/or arterio-venous oxygen difference. This highlights the importance of combined strategies to identify and manage AF in individuals with HF.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".