Association of STOP-Bang Questionnaire as a Screening Tool for Sleep Apnea and Postoperative Complications: A Systematic Review and Bayesian Meta-analysis of Prospective and Retrospective Cohort Studies
Bibliographic record
Abstract
BACKGROUND: The risk of postoperative complications increases with undiagnosed obstructive sleep apnea (OSA). The high-risk OSA (HR-OSA) patients can be easily identified using the STOP-Bang screening tool. The aim of this systematic review and meta-analysis is to determine the association of postoperative complications in patients screened as HR-OSA versus low-risk OSA (LR-OSA). METHODS: The following data bases were searched from January 1, 2008, to October 31, 2016, to identify the eligible articles: Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, Cochrane Databases of Systematic Reviews, Medline-in-Process & other nonindexed citations, Google Scholar, Embase, Web of Sciences and Scopus. The search included studies with adult surgical patients screened for OSA with STOP-Bang questionnaire that reported at least 1 cardiopulmonary or any other complication requiring intensive care unit admission as diagnosis of outcome. We used a Bayesian random-effects analysis to evaluate the existing evidence of STOP-Bang in relation to OSA and to assess the association of postoperative complications with the identified HR-OSA patients by study design and methodologies. RESULTS: This systematic review and meta-analysis was conducted using 10 cohort studies: 23,609 patients (HR-OSA, 7877; LR-OSA, 15,732). The pooled odds of perioperative complications were higher in the HR-OSA versus LR-OSA patients (odds ratio 3.93, 95% credible interval, 1.85-7.77, P= .003; 6.86% vs 4.62%). The length of hospital stay was longer in HR-OSA by 2 days when compared with LR-OSA (5.0 ± 4.2 vs 3.4 ± 2.8 days; mean difference 2.01; 95% credible interval, 0.77-3.24; P= .005). Meta-regression to adjust for baseline confounding factors and subgroup analysis did not materially change the results. CONCLUSIONS: This systematic review and meta-analysis suggests that HR-OSA is related with higher risk of postoperative adverse events and longer length of hospital stay when compared with LR-OSA patients. Our findings support the implementation of the STOP-Bang screening tool for perioperative risk stratification.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".