The <scp>STOP</scp>‐<scp>BANG</scp> questionnaire shows an insufficient specificity for detecting obstructive sleep apnea in patients with atrial fibrillation
Bibliographic record
Abstract
Summary Obstructive sleep apnea (OSA) is a sleep disorder associated with significant cardiovascular comorbidities, including cardiac arrhythmia. The STOP‐BANG questionnaire is an eight‐item self‐report questionnaire designed to screen patients for OSA and was validated in preoperative surgical patients. The STOP items are snoring, daytime tiredness, observed apneas and high blood pressure. The BANG items are body mass index >35 kg/m2, age >50 years, neck circumference >40 cm and male gender. We aimed to determine the screening properties of the STOP‐BANG questionnaire in patients with arrhythmia. Non‐selected consecutive patients were recruited from arrhythmia clinics. Patients with previously diagnosed and/or treated OSA were excluded. The STOP‐BANG questionnaire was self‐administered. Patients underwent two consecutive nights of home sleep recording. OSA was defined as an apnea‐hypopnea index score of ≥5/hr of sleep. The screening properties of the STOP‐BANG questionnaire were analysed compared with the objective diagnosis of OSA by ambulatory testing. Ninety‐five patients were included in the final analysis. Eighty‐five percent were found to have OSA. The STOP‐BANG score of ≥3 was 89% sensitive and 36% specific for diagnosis of OSA. The STOP‐BANG questionnaire had fair performance, as indicated by an area under the curve of 0.74 (p = .004). In conclusion, the STOP‐BANG questionnaire is sensitive; however, it has a low specificity with a high false positive rate. Given that a large number of atrial fibrillation patients need testing for OSA, we recommend the use of a level II sleep study regardless of the results of the screening questionnaire. This approach accurately identifies OSA and may limit the cost of unnecessary level‐I sleep studies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".