The Effects of Continuous Positive Airway Pressure on Postoperative Outcomes in Obstructive Sleep Apnea Patients Undergoing Surgery
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a commonly encountered comorbid condition in patients undergoing surgery and is associated with a greater risk of postoperative adverse events. Our objective in this review was to investigate the effectiveness of continuous positive airway pressure (CPAP) in reducing the risk of postoperative adverse events in patients with OSA undergoing surgery, the perioperative Apnea-Hypopnea Index (AHI), and the hospital length of stay (LOS). METHODS: We performed a systematic search of the literature databases. We reviewed the studies that included the following: (1) adult surgical patients (>18 years old) with information available on OSA; (2) patients using either preoperative and/or postoperative CPAP or no-CPAP; (3) available reports on postoperative adverse events, preoperative and postoperative AHI, and LOS; and (4) all published studies in English including case series. RESULTS: Six studies that included 904 patients were eligible for the meta-analysis. The meta-analysis for postoperative adverse events was performed in 904 patients (CPAP: n = 471 vs no-CPAP: n = 433; adverse events: 134 vs 133; P = 0.19). There was no significant difference in the postoperative adverse events between the 2 groups. The preoperative baseline AHI without CPAP was reduced significantly with postoperative use of CPAP (preoperative AHI versus postoperative AHI, 37 ± 19 vs 12 ± 16 events per hour, P < 0.001). LOS showed a trend toward significance in the CPAP group versus the no-CPAP group (4.0 ± 4 vs 4.4 ± 8 days, P = 0.05). CONCLUSIONS: Our review suggests that there was no significant difference in the postoperative adverse events between CPAP and no-CPAP treatment. Patients using CPAP had significantly lower postoperative AHI and a trend toward shorter LOS. There may be potential benefits in the use of CPAP during the perioperative period.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".