Undiagnosed Obstructive Sleep Apnea and Postoperative Outcomes: A Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of undiagnosed obstructive sleep apnea (OSA) during preoperative evaluation and the best method to screen OSA and its association with postoperative complications remain unclear. OBJECTIVES: To determine the prevalence of undiagnosed OSA in preoperative Indian patients undergoing noncardiac surgery, to compare the diagnostic accuracy of the STOP-BANG questionnaire to a preoperative level III sleep study, and to assess the association of OSA with postoperative complications. METHODS: A prospective cohort of 245 consecutive adults with ≥2 risk factors for OSA who underwent noncardiac surgery between July 2011 and February 2013 were studied. The STOP-BANG questionnaire was administered to all patients, and 182/245 (74.2%) patients underwent a preoperative level III sleep study. Patients were followed for postoperative complications in hospital and contacted at 30 days after surgery. RESULTS: 70/182 (38.5%) obtained a new diagnosis of OSA, including 11/182 (6%) with moderate to severe OSA (apnea-hypopnea index ≥15/h). On logistic regression analyses, the presence of OSA was independently associated with postoperative oxygen desaturation (OR 5.96, 95% CI 2.35-15.1, p < 0.01), a postoperative complication within 7 days (OR 3.63, 95% CI 1.77-7.45, p < 0.01) and within 30 days (OR 3.5, 95% CI 1.74-7.1, p < 0.01). The STOP-BANG questionnaire did not identify 12/70 (17%) of the patients diagnosed with OSA and classified 28% of the cohort as OSA when the level III sleep study was negative. CONCLUSIONS: Unrecognized OSA is common in preoperative patients and is independently associated with postoperative complications. The STOP-BANG questionnaire had a lower performance in the diagnosis of OSA in a South Indian population than the level III sleep study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".