End-Tidal Hypocapnia Under Anesthesia Predicts Postoperative Delirium
Bibliographic record
Abstract
Background: Postoperative delirium (POD) might be associated with anesthetic management, but research has focused on choice or dosage of anesthetic drugs. We examined potential contributions of intraoperative ventilatory and hemodynamic management to POD. Methods: This was a sub-study of the ENGAGES-Canada trial (NCT02692300) involving non-cardiac surgery patients enrolled in Winnipeg, Canada. Patients received preoperative psychiatric and cognitive assessment, and intraoperatively underwent high-fidelity data collection of blood pressure, end-tidal respiratory gases and anesthetic agent concentration. POD was assessed by peak and mean POD scores using the Confusion Assessment Method-Severity (CAM-S) tool. Bivariate and multiple linear regression models were constructed controlling for age, psychiatric illness, and cognitive dysfunction in the examination of deviations in intraoperative end-tidal carbon dioxide (areas over (AOC) and under the curve (AUC)) on POD severity scores. Results: A total of 101 subjects (69 (6) years of age) were studied; 89 had comprehensive intraoperative hemodynamic and end-tidal gas measurements (data recorded at 1 Hz). The incidence of POD was 11.9%(12/101). Age, cognitive dysfunction, anxiety, depression, and intraoperative end-tidal CO2 (AUC) were significant correlates of POD severity. In the multiple regression model, cognitive dysfunction and AUC end-tidal CO2 (0.67 kPa below median intra-operative value) were the only independent significant predictors across both POD severity (mean and peak) scores. There was no association between cumulative anesthetic agent exposure and POD. Conclusions: POD was associated with intraoperative ventilatory management, reflected by low end-tidal CO2 concentrations, but not with cumulative anesthetic drug exposure. These findings suggest that maintenance of intraoperative normocapnia might benefit patients at risk of POD.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".