Obstructive Sleep Apnea Screening in Patients With Atrial Fibrillation: Missed Opportunities for Early Diagnosis
Bibliographic record
Abstract
BACKGROUND: "There is a high prevalence of obstructive sleep apnea (OSA) among patients with atrial fibrillation (AF). There is also strong evidence that proper OSA management can reduce AF recurrence." Polysomnography is the gold standard for OSA diagnosis, but screening tests, such as STOP-BANG, have been successful in identifying patients at risk for OSA. Our study assesses screening rates for OSA in patients with persistent AF, and willingness of patients at increased risk for OSA towards further diagnostic evaluation. METHODS: A total of 254 persistent AF patients were surveyed regarding prior screening for OSA, and if previously unscreened, assessed with STOP-BANG. Prior cardioversions and willingness to undergo further workup was also recorded. Patients at risk for OSA were given educational brochures. Subjects with diagnosis of OSA were asked about their compliance with positive airway pressure therapy. RESULTS: Sixty-six percent of AF patients were never screened for OSA; 75% unscreened participants (95% CI: 68-81%) were at high risk for OSA. Patients with previous hospitalizations or electrical cardioversions were more frequently screened for OSA (P = 0.02, P = 0.03, respectively). Forty-three percent of high-risk individuals had a BMI < 30. Among patients at risk for OSA (score ≥ 3), the majority (n = 99, 79%) were interested in follow-up with a sleep study (n = 93, 74%). CONCLUSIONS: Although there is a strong OSA-associated risk for AF, which is amenable to intervention, most patients with persistent AF are not assessed for OSA. Simple to use screening questionnaires are sensitive and can reliably identify patients at high risk for OSA, reserving costlier and somewhat inconvenient nocturnal polysomnography to only those at risk. We hope our study will help to push the AF and OSA connection into the spotlight in the primary care of patients with AF.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".