469 Utilization of the STOP-Bang questionnaire for referral of obstructive sleep apnea in various geographical regions
Bibliographic record
Abstract
Abstract Introduction Obstructive sleep apnea (OSA) is a highly prevalent global health concern and is associated with many adverse outcomes for patients. Our objective is to determine the utility of the STOP-Bang questionnaire in the sleep clinic setting to screen for and stratify the risk of OSA among populations from different geographical regions. Methods The following electronic databases were systematically searched from 2008 to March 2020: MEDLINE, Medline-in-process, Embase, EmCare Nursing, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, PsycINFO, Journals @ Ovid, Web of Science, Scopus, and CINAHL. Inclusion criteria were: 1) assessment of the STOP-Bang questionnaire to screen for OSA in adult subjects (age ≥18 years); 2) patients referred to sleep clinic; 3) lab-polysomnography or home sleep apnea testing results confirmed the OSA diagnosis; and 4) apnea-hypopnea index (AHI) or respiratory disturbance index (RDI) was used to diagnose and grade the severity of OSA. Clinical and demographic data were extracted from each article independently by two reviewers (B.P., L.C.). Pooled predictive parameters were calculated using 2x2 contingency tables. Random effects meta-analyses and meta-regression with sensitivity analyses were performed. The Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines were followed. Results Forty-seven studies (n=26,547) studies met the criteria for systematic review (mean age: 49±14 years, mean body mass index: 32±8 kg/m2, 65% male). Studies were organized into different geographic regional groups – North America, South America, Europe, Middle East, East Asia, and South/Southeast Asia. The prevalence of all OSA, moderate-to-severe OSA, and severe OSA was 80%, 58%, and 39%, respectively. The area under the receiver operating curve of a STOP-Bang score ≥3 to detect moderate-to-severe OSA is high (>0.80) in all regions, except in East Asia (0.52). A STOP-Bang score ≥ 3 has excellent sensitivity (>90%) and high discriminative power to exclude moderate-to-severe, and severe OSA with negative predictive values of 77% and 91%, respectively. Conclusion The meta-regression analysis demonstrates that the STOP-Bang questionnaire can be utilized as an effective OSA screening tool among different geographical populations to assist in prioritizing patients with suspected OSA for assessment in sleep clinic. Support (if any):
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".