Regional anesthesia techniques and postoperative delirium: systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Postoperative delirium is a frequent occurrence in the elderly surgical population. As a comprehensive list of predictive factors remains unknown, an opioid-sparing approach incorporating regional anesthesia techniques has been suggested to decrease its incidence. Due to the lack of conclusive evidence on the topic, we conducted a systematic review and meta-analysis to investigate the potential impact of regional anesthesia and analgesia on postoperative delirium. EVIDENCE ACQUISITION: PubMed, Embase, and the Cochrane central register of Controlled trials (CENTRAL) databases were searched for randomized trials comparing regional anesthesia or analgesia to systemic treatments in patients having any type of surgery. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We pooled the results separately for each of these two applications by random effects modelling. Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was used to evaluate the certainty of evidence and strength of conclusions. EVIDENCE SYNTHESIS: Eighteen trials (3361 subjects) were included. Using regional techniques for surgical anesthesia failed to reduce the risk of postoperative delirium, with a relative risk (RR) of 1.21 (95% CI: 0.79 to 1.85); P=0.3800. In contrast, regional analgesia reduced the relative risk of perioperative delirium by a RR of 0.53 (95% CI: 0.42 to 0.68; P<0.0001), when compared to systemic analgesia. Post-hoc subgroup analysis for hip fracture surgery yielded similar findings. CONCLUSIONS: These results show that postoperative delirium may be decreased when regional techniques are used in the postoperative period as an analgesic strategy. Intraoperative regional anesthesia alone may not decrease postoperative delirium since there are other factors that may influence this outcome.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".