Obstructive sleep apnea in the patient with atrial fibrillation: current knowledge and remaining uncertainties
Bibliographic record
Abstract
PURPOSE OF REVIEW: Obstructive sleep apnea (OSA) is highly prevalent in patients with atrial fibrillation and plays a causal role for OSA in the pathogenesis of atrial fibrillation. The presence of OSA in atrial fibrillation is associated with increased symptom burden and increased risk of hospitalizations. Furthermore, untreated OSA is associated with an increased risk of atrial fibrillation recurrence post ablation or cardioversion, and observational studies suggest that continuous positive airway pressure (CPAP) therapy can attenuate this risk. This review describes our current understanding of the relationship between OSA and atrial fibrillation with an emphasis on emerging evidence. RECENT FINDINGS: Recent studies have identified novel screening questionnaires, which may be superior to traditional questionnaires in identifying OSA in atrial fibrillation populations. Significant night-to-night variability in OSA severity has been shown in atrial fibrillation patients, which has implications for diagnostic testing. While several small, randomized control trials (RCTs) have not shown CPAP therapy to be effective in reducing atrial fibrillation burden, one RCT did show CPAP can attenuate the atrial substrate with implications for long-term outcomes. SUMMARY: Further RCTs, appropriately powered, and focused on well defined cohorts, are required to guide management decisions regarding screening and treatment of OSA in atrial fibrillation populations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".