Postoperative outcomes in surgical patients with obstructive sleep apnoea diagnosed by sleep studies: a meta‐analysis and trial sequential analysis
Bibliographic record
Abstract
Identifying surgical patients with obstructive sleep apnoea may assist with anaesthetic management to minimise postoperative complications. Using trial sequential analysis, we evaluated the impact of obstructive sleep apnoea diagnosed by polysomnography or home sleep apnoea testing on postoperative outcomes in surgical patients. Multiple databases were systematically searched. Outcomes included: total postoperative complications, systemic complications (cardiovascular, respiratory, neurological, renal, infectious) and specific complications (atrial fibrillation, myocardial infarction, combined hospital and intensive care unit re-admission, mortality). The pooled odds ratios of postoperative complications were evaluated by the Mantel-Haenszel method random-effects model. Meta-analysis and meta-regression were conducted, and the GRADE approach was used to evaluate the certainty of evidence. Twenty prospective cohort studies with 3756 patients (2127 obstructive sleep apnoea and 1629 non-obstructive sleep apnoea) were included (9 in non-cardiac surgery and 11 in cardiac surgery). Postoperative complications were almost two-fold higher with obstructive sleep apnoea, OR (95%CI) 1.92 (1.52-2.42), p < 0.001; certainty of evidence, moderate. Obstructive sleep apnoea was associated with a 1.5 times increased risk of postoperative cardiovascular complications, OR (95%CI) 1.56 (1.20-2.02), p = 0.001; certainty of evidence, moderate; an almost two-fold increase in respiratory complications, OR (95%CI) 1.91 (1.39-2.62), p < 0.001; certainty of evidence, moderate; and hospital and ICU re-admission, OR (95%CI) 2.25 (1.21-4.19), p = 0.01; certainty of evidence, low. Trial sequential analysis showed adequate information size for postoperative complications. Baseline confounding factors were adjusted by meta-regression, and the sub-group analysis did not materially change our results. This increased risk occurred especially in patients in whom obstructive sleep apnoea had been newly diagnosed, emphasising the importance of pre-operative screening.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".