Screening for Obstructive Sleep Apnea in an Atrial Fibrillation Population: What’s the Best Test?
Bibliographic record
Abstract
Background Among individuals with nonvalvular atrial fibrillation (AF), the prevalence of obstructive sleep apnea (OSA) can be as high as 85%. Continuous positive airway pressure treatment for moderate or severe OSA might improve AF outcomes and quality of life, so early identification of OSA might be of value. However, screening questionnaires for OSA are suboptimal because they are weighted toward tiredness and loud snoring, which might be absent in AF patients. NoSAS ( N eck, O besity, S noring, A ge, S ex) is a new OSA questionnaire that excludes these parameters. Acoustic pharyngometry (AP) is a potential novel screening technique that measures pharyngeal cross-sectional area, which is reduced in patients with OSA. Methods We prospectively compared the accuracy of the NoSAS, the STOP-BANG questionnaire ( S noring, T iredness, O bserved apnea, blood P ressure, B ody mass index, A ge, N eck circumference and G ender), and AP with home sleep apnea testing (HSAT) in consecutive patients with nonvalvular AF. Results Of 188 participants, 86% had OSA and 49% had moderate or severe OSA. Mean Epworth Sleepiness Scale scores were low; 5.9 (SD, 3.9), indicating that most participants were not sleepy. Receiver operating characteristic curves for comparisons of screening tests with HSAT showed suboptimal accuracy. For moderate plus severe and severe only groups respectively, the area under the curve was 0.50 (95% confidence interval [CI], 0.42-0.58) and 0.42 (95% CI, 0.34-0.52) for AP, 0.65 (95% CI, 0.58-0.73) and 0.63 (95% CI, 0.52-0.74) for the STOP-BANG questionnaire, and 0.68 (95% CI, 0.60-0.75) and 0.69 (95% CI, 0.59-0.80) for the NoSAS. Conclusions AP and NoSAS are not sufficiently accurate for screening AF patients for OSA. Because of the high rates of OSA in this cohort, the potential benefits of OSA treatment, and the suboptimal accuracy of current screening questionnaires, cardiologists should consider HSAT for AF patients.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".