Risk factors for postoperative delirium in orthopedic surgery patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium is a common and serious complication in orthopedic surgery patients, particularly in the elderly. This study aimed to systematically review and meta-analyze the risk factors associated with postoperative delirium in orthopedic surgery patients. METHODS: A comprehensive literature search was conducted across PubMed, Cochrane Library, SpringerLink, Elsevier Science Direct, and CNKI databases from inception to October 2024. Studies reporting risk factors for postoperative delirium in orthopedic surgery patients were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and meta-analyses were performed using random-effects models to calculate pooled risk ratios (RR) and weighted mean differences (WMD) with 95% confidence intervals (CI). RESULTS: A total of 19 studies involving 4,410 patients were included. Significant risk factors for postoperative delirium included advanced age (WMD: 3.30 years, 95% CI: 0.59-6.01), male sex (RR: 1.12, 95% CI: 1.00-1.26), diabetes (RR: 1.76, 95% CI: 1.20-2.58), and preoperative cognitive dysfunction (RR: 1.98, 95% CI: 1.76-2.22). BMI was not significantly associated with delirium risk (WMD: -0.19, 95% CI: -0.84-0.46). The quality of the included studies was generally good, with NOS scores ranging from 6 to 8. CONCLUSION: This meta-analysis identified several significant risk factors for postoperative delirium in orthopedic surgery patients, including advanced age, male sex, diabetes, and preoperative cognitive dysfunction. These findings highlight the multifactorial nature of postoperative delirium and underscore the importance of comprehensive preoperative assessment to identify high-risk patients. Future research should focus on developing comprehensive risk prediction models that integrate both modifiable and non-modifiable risk factors to improve outcomes for patients undergoing orthopedic surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".