Incidence and Predictors of Postoperative Delirium in Patients Undergoing Elective Hip and Knee Arthroplasty: A Prospective Observational Study
Bibliographic record
Abstract
Background/Objectives: Postoperative delirium has not been well explored in patients undergoing elective hip and knee arthroplasty. This study assessed the incidence of delirium in these patients in the postanesthetic care unit (PACU) and throughout their hospital admission. Predictors of postoperative delirium and impact of delirium on length of stay were also analyzed. Methods: This prospective observational study recruited patients (n = 978) with normal cognitive function presenting for elective primary hip or knee arthroplasty at a single tertiary academic center. Delirium was assessed using the Nursing Delirium Scoring Scale (NuDESC) in the PACU, and twice daily after that on postoperative days 1, 2 and 3, or until discharge, whichever came first. Results: In total, 26 (2.7%) patients developed delirium postoperatively. Unadjusted logistic regression analyses revealed that age; history of cardiovascular, central nervous system, hematologic, endocrinologic, psychiatric disease; postoperative opioid use; and ASA level were associated with an increased risk of delirium, with odds ratios (95% confidence interval) of 1.7 (1.35 to 2.11), 3.6 (1.09 to 12.25), 3.5 (1.53 to 8.03), 2.7 (1.09 to 6.45), 2.3 (1.04 to 4.97), 4.7 (2.10 to 10.70), 0.4 (0.17 to 0.89), and 2.37 (1.05 to 5.33), respectively. A Mann–Whitney U test showed no difference in PACU or hospital length of stay between patients who did and did not have delirium in the PACU (within the first hour). Conclusions: Age, ASA > 3, a history of cardiovascular disease, central nervous system disease, hematologic disease, endocrinologic disease, psychiatric disease and postoperative opioid use are individually associated with postoperative delirium. A future study with an even larger sample size is needed to further evaluate these factors in an adjusted analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".