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.
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 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 | 0.019 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.082 | 0.015 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".