Characteristics of Patients with Unrecognized Sleep Apnea Requiring Postoperative Oxygen Therapy
Bibliographic record
Abstract
Surgical patients with obstructive sleep apnea (OSA) have increased risk of perioperative complications. The primary objective is to determine the characteristics of surgical patients with unrecognized OSA requiring oxygen therapy for postoperative hypoxemia. The secondary objective is to investigate the characteristics of patients who were responsive to oxygen therapy. This was a post-hoc multicenter study involving patients with cardiovascular risk factors undergoing major non-cardiac surgery. Patients ≥45 years old underwent Type 3 sleep apnea testing and nocturnal oximetry preoperatively. Responders to oxygen therapy were defined as individuals with ≥50% reduction in oxygen desaturation index (ODI) on postoperative night 1 versus preoperative ODI. In total, 624 out of 823 patients with unrecognized OSA required oxygen therapy. These were mostly males, had larger neck circumferences, higher Revised Cardiac Risk Indices, higher STOP-Bang scores, and higher ASA physical status, undergoing intraperitoneal or vascular surgery. Multivariable regression analysis showed that the preoperative longer cumulative time SpO2 < 90% or CT90% (adjusted p = 0.03), and lower average overnight SpO2 (adjusted p < 0.001), were independently associated with patients requiring oxygen therapy. Seventy percent of patients were responders to oxygen therapy with ≥50% ODI reduction. Preoperative ODI (19.0 ± 12.9 vs. 14.1 ± 11.4 events/h, p < 0.001), CT90% (42.3 ± 66.2 vs. 31.1 ± 57.0 min, p = 0.038), and CT80% (7.1 ± 22.6 vs. 3.6 ± 8.7 min, p = 0.007) were significantly higher in the responder than the non-responder. Patients with unrecognized OSA requiring postoperative oxygen therapy were males with larger neck circumferences and higher STOP-Bang scores. Those responding to oxygen therapy were likely to have severe OSA and worse preoperative nocturnal hypoxemia. Preoperative overnight oximetry parameters may help in stratifying patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".