Sleep Study and Oximetry Parameters for Predicting Postoperative Complications in Patients With OSA
Bibliographic record
Abstract
In the surgical setting, OSA is associated with an increased risk of postoperative complications. At present, risk stratification using OSA-associated parameters derived from polysomnography (PSG) or overnight oximetry to predict postoperative complications has not been established. The objective of this narrative review is to evaluate the literature to determine the association between parameters extracted from in-laboratory PSG, portable PSG, or overnight oximetry and postoperative adverse events. We obtained pertinent articles from Ovid MEDLINE, Ovid MEDLINE In-Process & Other Non-Indexed Citations, and Embase (2008 to December 2017). The search included studies with adult patients undergoing surgery who had OSA diagnosed with portable PSG, in-laboratory PSG, or overnight oximetry that reported on specific sleep parameters and at least one adverse outcome. The search was restricted to English-language articles. The search yielded 1,810 articles, of which 21 were included in the review. Preoperative apnea-hypopnea index (AHI) and measurements of nocturnal hypoxemia such as oxygen desaturation index (ODI), cumulative sleep time percentage with oxyhemoglobin saturation (Spo 2 ) < 90% (CT90), minimum Spo 2 , mean Spo 2 , and longest apnea duration were associated with postoperative complications. OSA is associated with postoperative complications in the population undergoing surgery. Clinically and statistically significant associations between AHI and postoperative adverse events exists. Complications may be more likely to occur in the category of moderate to severe OSA (AHI ≥ 15). Other parameters from PSG or overnight oximetry such as ODI, CT90, mean and minimal Spo 2 , and longest apnea duration can be associated with postoperative complications and may provide additional value in risk stratification and minimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 |
| 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".