Obstructive Sleep Apnea and Risk of Postoperative Complications after Non-Cardiac Surgery
Bibliographic record
Abstract
Obstructive sleep apnea (OSA), a common sleep disorder, poses significant challenges in perioperative management due to its complexity and multifactorial nature. With a global prevalence of approximately 22.6%, OSA often remains undiagnosed, and increases the risk of cardiac and respiratory postoperative complications. Preoperative screening has become essential in many institutions to identify patients at increased risk, and experts recommend proceeding with surgery in the absence of severe symptoms, albeit with heightened postoperative monitoring. Anesthetic and sedative agents exacerbate upper airway collapsibility and depress central respiratory activity, complicating intraoperative management, especially with neuromuscular blockade use. Additionally, OSA patients are particularly prone to opioid-induced respiratory depression, given their increased sensitivity to opioids and heightened pain perception. Thus, regional anesthesia and multimodal analgesia are strongly advocated to reduce perioperative complication risks. Postoperative care for OSA patients necessitates vigilant monitoring and tailored management strategies, such as supplemental oxygen and Positive Airway Pressure therapy, to minimize cardiorespiratory complications. Health care institutions are increasingly focusing on enhanced monitoring and resource allocation for patient safety. However, the rising prevalence of OSA, heterogeneity in disease severity, and lack of evidence for the efficacy of costly perioperative measures pose challenges. The development of effective screening and monitoring algorithms, alongside reliable risk predictors, is crucial for identifying OSA patients needing extended postoperative care. This review emphasizes a multidimensional approach in managing OSA patients throughout the perioperative period, aiming to optimize patient outcomes and minimize adverse 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".