Risk factors of postoperative delirium in the knee and hip replacement patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The risk factors of postoperative delirium (POD), a serious while preventable complication, developed by patients undergoing knee and replacement surgery are still under investigation. In this systematic review and meta-analysis, we identified risk factors associated with POD in knee and hip replacement. Methods PubMed, Ovid MEDLINE, and Ovid EMBASE were used to identify original researches. The studies evaluating the risk factors of POD after knee and hip replacement were reviewed, and the qualities of the included studies were assessed with Newcastle–Ottawa Scale. Data were extracted, pooled, and a meta-analysis was completed Result Twenty-two studies were finally included with a total of 11934 patients who underwent knee or hip replacement and 1841 developed POD with an incidence of 17.6% (95% confidential interval (CI) 13.2–22.0%). Eighteen significant risk factors were identified including advanced age (odds ratio (OR) 1.15 95% CI 1.08–1.22), cognitive impairment (OR 6.84, 95% CI 3.27–14.33), history of cerebrovascular events (OR 2.51, 95% CI 1.28–4.91), knee replacement (OR 1.42, 95% CI 1.00–2.02), blood loss (standardized mean difference (SMD) 0.30, 95% CI 0.15–0.44), dementia (OR 3.09, 95% CI 2.10–4.56), neurologic disorders (OR 2.26, 95% CI 1.23–4.15), psychiatric illness (OR 2.74, 95% CI 1.34–5.62), and obstructive sleep apnea (OR 4.17, 95% CI 1.72–10.09) along with several comorbidity evaluation scores and laboratory markers. Conclusion We identified risk factors consistently associated with the incidence of POD in knee and hip replacement. Strategies and interventions should be implemented to the patients receiving knee or hip replacement with potential risk factors identified in this meta-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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".