Preoperative hypoxic biomarkers and postoperative delirium in patients with obstructive sleep apnea
Bibliographic record
Abstract
PURPOSE: Postoperative delirium (POD) in patients with obstructive sleep apnea (OSA) is associated with increased mortality and healthcare costs. In this study, we investigated the association of OSA risk, serum biomarkers for central nervous ischemia (S100B and NSE), and POD. METHODS: After research ethics approval, patients completed the STOP BANG assessment before undergoing elective surgery. Blood was drawn for S100B and NSE measurement, and cognitive performance was tested using the Montreal Cognitive Assessment (MoCA) at study admission and postoperatively at discharge. Delirium assessment was performed using the Nursing Delirium Screening Scale (NuDESC) and the Confusion Assessment Method (CAM). RESULTS: One hundred twenty-four enrolled patients were separated into three OSA-risk groups based on STOP BANG score testing (low risk, n = 22; intermediate risk, n = 67; high risk, n = 35). Preoperative NSE values increased with OSA risk (NSE in ng/ml; mean [range]; low risk: 15.6 [9.2-44.3]; intermediate risk: 21.8 [7.6-114.1]; high risk: 29.2 [10.1-151]; p = 0.039). Postoperative MoCA and NuDESC assessments were not different between the OSA-risk groups. We found a decreasing incidence for POD with increasing OSA risk (positive CAM: low risk: 18.1%, intermediate risk: 12.0%; high risk: 11.5%, p = 0.043). However, this was no longer detectable in a complete case analysis. In patients with POD, postoperative ischemic biomarker values were not different between OSA-risk groups. CONCLUSION: We found a trend of decreasing POD incidence with increasing OSA risk, which was not robust in a complete case analysis. Our results possibly support the phenomenon of hypoxic preconditioning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".