2157 – Cortisol Levels And Neuropsychiatric Diagnosis As Markers Of Postoperative Delirium
Bibliographic record
Abstract
Introduction Delirium affects 11% to 47% of post-operative cardiac surgery patients and is associated with higher mortality and morbidity rates. However, the pathophysiology of this syndrome is largely unknown. Objectives To investigate whether increased concentration of pre- and postoperative plasma cortisol predicts the development of delirium after coronary artery bypass graft surgery (CABG). Secondly, to assess whether the association between cortisol and delirium is stress-related or mediated by other pathologies, such as major depressive disorder (MDD) or cognitive impairment. Methods The patients were examined one day preoperatively with Mini International Neuropsychiatric Interview and Montreal Cognitive Assessment to screen for depression and cognitive impairment, respectively. Blood samples for cortisol levels were collected both pre- and post-operatively. The Confusion Assessment Method was used within the first five days postoperatively to screen for a diagnosis of delirium. Results Postoperative delirium developed in 36% (41of 113) of participants. Multivariate logistic regression analysis revealed two groups independently associated with an increased risk of developing post-operative delirium: 1) Those with pre-operatively raised cortisol levels and 2) Those with a pre-operative diagnosis of MDD associated with raised levels of cortisol post-operatively. According to ROC analysis the most optimal cutoff values of the preoperative and postoperative cortisol concentration which predict the development of delirium were 353.55 nmol/L, and 994.10 nmol/L, respectively. Conclusion Raised peri-operative plasma cortisol concentrations are associated with delirium after CABG surgery. This may be an important pathophysiological consideration in the increased risk of post-operative delirium seen in patients with a preoperative diagnosis of MDD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".