Perioperative approaches to prevent delayed neurocognitive recovery and postoperative neurocognitive disorder in older surgical patients: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Background and Aims: Delayed neurocognitive recovery (DNR) and postoperative neurocognitive disorder (P-NCD) are common postoperative complications affecting older patients. This review evaluates perioperative approaches for preventing DNR and P-NCD in older noncardiac surgical patients. Material and Methods: We searched databases for relevant articles from inception through June 2022 and updated in May 2023 (PROSPERO ID CRD42022359289). Randomized controlled trials (RCTs) utilizing intervention for DNR and/or P-NCD were included. Results: We included 39 RCTs involving anesthetic (25 RCTs, 7422 patients) and other pharmacological and nonpharmacological approaches (14 RCTs, 2210 patients). Seventeen trials investigating four interventions were included in the meta-analysis for DNR. Perioperative dexmedetomidine (relative risk [RR]: 0.59, 95% confidence interval [CI]: 0.35–0.97; P = 0.04) and propofol-based total intravenous anesthesia (TIVA) (RR: 0.81, 95% CI: 0.66–0.98; P = 0.03) significantly decreased the risk of DNR versus control. There was no significant decrease in the risk of DNR with regional anesthesia (RA) versus general anesthesia (GA) (RR: 0.89, 95% CI: 0.63–1.26) or bispectral index (BIS) monitoring (RR: 0.79, 95% CI: 0.60–1.04) versus the control groups. Evidence regarding the effects of interventions on P-NCD is limited. Although all included trials were at low risk of bias, the quality of meta-analysis pooled estimates was low. Conclusions: Our meta-analysis of RCTs showed that dexmedetomidine and TIVA decrease the risk of DNR in older patients undergoing noncardiac surgery by 41% and 20%, respectively, versus control. Further RCTs of adequate power and methodology on the effects of interventions on DNR and P-NCD are warranted.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| 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".