Is obstructive sleep apnea associated with difficult airway? Evidence from a systematic review and meta-analysis of prospective and retrospective cohort studies
Bibliographic record
Abstract
BACKGROUND: Difficult airway management and obstructive sleep apnea may contribute to increased risk of perioperative morbidity and mortality. The objective of this systematic review and meta-analysis (SRMA) is to evaluate the evidence of a difficult airway being associated with obstructive sleep apnea (OSA) patients undergoing surgery. METHODS: The standard databases were searched from 1946 to April 2017 to identify the eligible articles. The studies which included adult surgical patients with either suspected or diagnosed obstructive sleep apnea must report at least one difficult airway event [either difficult intubation (DI), difficult mask ventilation (DMV), failed supraglottic airway insertion or difficult surgical airway] in sleep apnea and non-sleep apnea patients were included. RESULTS: Overall, DI was 3.46-fold higher in the sleep apnea vs non-sleep apnea patients (OSA vs. non-OSA: 13.5% vs 2.5%; OR 3.46; 95% CI: 2.32-5.16, p <0.00001). DMV was 3.39-fold higher in the sleep apnea vs non-sleep apnea patients (OSA vs. non-OSA: 4.4% vs 1.1%; OR 3.39; 95% CI: 2.74-4.18, p <0.00001). Combined DI and DMV was 4.12-fold higher in the OSA vs. non-OSA patients (OSA vs. non-OSA: 1.1% vs 0.3%; OR 4.12; 95% CI: 2.93-5.79, p <0.00001). There was no significant difference in the supraglottic airway failure rates in the sleep apnea vs non-sleep apnea patients (OR: 1.34; 95% CI: 0.70-2.59; p = 0.38). Meta-regression to adjust for various subgroups and baseline confounding factors did not impact the final inference of our results. CONCLUSION: This SRMA found that patients with obstructive sleep apnea had a three to four-fold higher risk of difficult intubation or mask ventilation or both, when compared to non-sleep apnea patients.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".