Predictors of Postoperative Delirium in Elderly Patients Following Total Hip and Knee Arthroplasty: A Systematic Review and Meta‑Analysis
Bibliographic record
Abstract
Abstract Background: Postoperative delirium (POD) is widely reported as a very common postoperative complication following total joint arthroplasty (TJA) of the hip and knee in elderly patients, leading to a variety of adverse effects. We sought to investigate predictors of delirium after TJA.Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were searched up to May 2020 for studies examining POD following TJA in elderly patients. Pooled odds ratio (OR) and mean difference (MD) of those who experienced delirium compared to those who did not were calculated for each variable. The Newcastle-Ottawa Scale (NOS) was used for the study quality evaluation. Results: Fifteen studies with 31 potential factors were included in the meta-analysis. In the primary analysis, 9 factors were associated with POD, comprising advanced age (MD 3.81; 95% confidence interval (CI) 1.80–5.83), dementia (OR 24.85; 95% CI 7.26–85.02), hypertension (OR 2.26; 95% CI 1.31–3.89), diabetes (OR 2.02; 95% CI 1.15–3.55), stroke (OR 14.61; 95% CI 5.26–40.55), psychiatric illness (OR 2.72; 95% CI 1.45–5.08), use of sedative-hypnotics (OR 6.42; 95% CI 2.53–16.27), lower preoperative levels of hemoglobin (MD −0.56; 95% CI −0.89–−0.22), and lower preoperative mini-mental state examination score (MD −0.40; 95% CI −0.69–−0.12). Twelve studies were included in the systematic review, of which 24 factors were additionally correlated with POD using single studies.Conclusions: We identified predictors of POD in elderly patients following TJA. These findings may be used to identify patients with a high risk of delirium who are most likely to benefit from postoperative recovery.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".