Obstructive sleep apnea and its management in patients with atrial fibrillation: An International Collaboration of Sleep Apnea Cardiovascular Trialists (INCOSACT) global survey of practicing cardiologists
Bibliographic record
Abstract
Background: Among international cardiologists it is unclear whether equipoise exists regarding the benefit of diagnosing and managing obstructive sleep apnea (OSA) to improve atrial fibrillation (AF) outcomes and whether clinical practice and equipoise are linked. Methods: Between January 2019 and June 2020 we distributed a web-based 12-question survey regarding OSA and AF management to practicing cardiologists in 16 countries. Results: The United States, Japan, Sweden, and Turkey accounted for two-thirds of responses. 863 cardiologists responded; half were general cardiologists, a quarter electrophysiologists. Responses regarding treating OSA with CPAP to improve AF endpoints were mixed. 33% of respondents referred AF patients for OSA screening. OSA was diagnosed in 48% of referred patients and continuous positive airway pressure (CPAP) was prescribed for 59% of them. Nearly 70% of respondents believed randomized controlled trials (RCTs) of OSA treatment in AF patients were necessary and indicated willingness to contribute to such trials. Conclusions: There was no clinical equipoise among surveyed cardiologists; a majority expressed certainty that combined OSA and AF treatment is superior to AF treatment alone for improving AF outcomes. However, a minority of surveyed cardiologists referred AF patients for OSA testing, and while half of screened AF patients had OSA, CPAP was prescribed in little more than half of them, reflecting the view that better clinical trial evidence is needed to support this practice. Our results underscore the need for larger, multi-national prospective studies of OSA treatment and AF outcomes to inform more uniform society guideline recommendations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".