Recent advances in predicting, preventing, and managing postoperative delirium
Bibliographic record
Abstract
Postoperative delirium (POD) is a major public health problem associated with poor patient outcomes such as increased hospital lengths of stay, loss of functional independence, and higher mortality. Depending on the study, the reported incidence ranges from 5% to 65%, with the highest incidence in hip and cardiac surgery. Anesthesiologists should be familiar with the predisposing and precipitating factors of POD, particularly screening for preoperative cognitive impairment and frailty syndrome. Screening tools, for example, the Mini-Mental State Exam, Mini-Cog, 4 A's test for delirium screening, and Montreal Cognitive Assessment, can be used to assess for cognitive impairment and the Clinical Frailty Scale to assess for frailty syndrome. The Hospital Elder Life Program is the standard prevention protocol that is tried and tested in reducing the incidence of POD. Prehabilitation, lung protective strategies, pharmacologic agents such as ramelteon, a melatonin receptor agonist, glucocorticoids, dexmedetomidine, and non-pharmacologic agents, such as noise reduction strategies and the encouragement of nocturnal sleep, have all led to a decrease in the incidence of POD and are being studied for their efficacy. However, the data are inconclusive to date. Intraoperatively, preventing hypotension and blood pressure swings, ensuring adequate pain control and anesthetic depth, and using age-adjusted minimum alveolar concentration (MAC) titration reduce the incidence of POD. The incidence of POD using regional or general anesthesia is similar. In this narrative review, we will discuss the current understanding of the predictors, pathophysiology, prevention, and management of POD and identify areas of further research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".