Prevalence, management and impact of chronic obstructive pulmonary disease in atrial fibrillation: a systematic review and meta-analysis of 4,200,000 patients
Bibliographic record
Abstract
AIM: Prevalence of chronic obstructive pulmonary disease (COPD) in atrial fibrillation (AF) patients is unclear, and its association with adverse outcomes is often overlooked. Our aim was to estimate the prevalence of COPD, its impact on clinical management and outcomes in patients with AF, and the impact of beta-blockers (BBs) on outcomes in patients with COPD. METHODS AND RESULTS: A systematic review and meta-analysis was conducted according to international guidelines. All studies reporting the prevalence of COPD in AF patients were included. Data on comorbidities, BBs and oral anticoagulant prescription, and outcomes (all-cause death, cardiovascular (CV) death, ischaemic stroke, major bleeding) were compared according to COPD and BB status. Among 46 studies, pooled prevalence of COPD was 13% [95% confidence intervals (CI) 10-16%, 95% prediction interval 2-47%]. COPD was associated with higher prevalence of comorbidities, higher CHA2DS2-VASc score and lower BB prescription [odds ratio (OR) 0.77, 95% CI 0.61-0.98]. COPD was associated with higher risk of all-cause death (OR 2.22, 95% CI 1.93-2.55), CV death (OR 1.84, 95% CI 1.39-2.43), and major bleeding (OR 1.45, 95% CI 1.17-1.80); no significant differences in outcomes were observed according to BB use in AF patients with COPD. CONCLUSION: COPD is common in AF, being found in 13% of patients, and is associated with increased burden of comorbidities, differential management, and worse outcomes, with more than a two-fold higher risk of all-cause death and increased risk of CV death and major bleeding. Therapy with BBs does not increase the risk of adverse outcomes in patients with AF and COPD.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".