Best perioperative practice in management of ambulatory patients with obstructive sleep apnea
Bibliographic record
Abstract
PURPOSE OF REVIEW: The perioperative outcome in obstructive sleep apnea (OSA) patients undergoing ambulatory surgery can be potentially impacted by the type of anesthetic technique, fluid management and choice of anesthetic agents. This review highlights the best perioperative practices in the management of OSA patients undergoing ambulatory surgical procedures. RECENT FINDINGS: A recent meta-analysis found that STOP-Bang might be used as a perioperative risk stratification tool. Patients with high-risk OSA (STOP-Bang ≥3) were found to be associated with an increased risk of postoperative complications and prolonged length of hospital stay compared with low-risk OSA (STOP-Bang 0-2) patients undergoing noncardiac surgical procedures. A bidirectional relationship exists between OSA and difficult airway. Both suspected or diagnosed OSA may be associated with either difficult intubation or difficult mask ventilation or both. A recent meta-analysis identified OSA as an important risk factor for opioid-induced respiratory depression. A dose-response relationship was shown between the morphine equivalent daily dose and death or near-death events in OSA patients undergoing surgery. Postoperative continuous monitoring is recommended for high-risk OSA patients receiving opioids. Minimising the dose of muscle relaxant, neuromuscular monitoring and ensuring complete reversal of neuromuscular blockade before extubation is essential in OSA patients to avoid postoperative complications. Whenever feasible, regional anesthesia with multimodal analgesia may be considered as a better alternative to general anesthesia in OSA patients. SUMMARY: Patients with OSA and associated comorbidities present a challenge to anesthesiologists as they are at a high risk of perioperative complications. It is important to identify patients with OSA, with the goal to raise awareness among providers, mitigate risk and improve outcomes.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".