Incidence and predictors of postoperative delirium in the older acute care surgery population: a prospective study
Bibliographic record
Abstract
Background: Among older inpatients, the highest incidence of delirium is within the surgical population. Limited data are available regarding postoperative delirium risk in the acute care surgical population. The purpose of our study was to establish the incidence of and risk factors for delirium in an older acute care surgery population. Methods: Patients aged 65 years or more who had undergone acute care surgery between April 2014 and September 2015 at 2 university-affiliated hospitals in Alberta were followed prospectively and screened for delirium by means of a validated chart review method. Delirium duration was recorded. We used separate multivariable logistic regression models to identify independent predictors for overall delirium and longer episodes of delirium (duration ≥ 48 h). Results: Of the 322 patients included, 73 (22.7%) were identified as having experienced delirium, with 49 (15.2%) experiencing longer episodes of delirium. Postoperative delirium risk factors included Foley catheter use, intestinal surgery, gallbladder surgery, appendix surgery, intensive care unit (ICU) admission and mild to moderate frailty. Risk factors for prolonged postoperative delirium included Foley catheter use and mild to moderate frailty. Surgical approach (open v. laparoscopic) and overall operative time were not found to be significant. Conclusion: In keeping with the literature, our study identified Foley catheter use, frailty and ICU admission as risk factors for delirium in older acute care surgical patients. We also identified an association between delirium risk and the specific surgical procedure performed. Understanding these risk factors can assist in prevention and directed interventions for this high-risk population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".