Prevalence of postoperative neurocognitive disorders in older non‐cardiac surgical patients: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The growing number of older persons undergoing surgery are at a higher risk of neurocognitive disorder due to multimorbidity and age-related changes. The prevalence of postoperative neurocognitive disorder in this population requires further investigation. This systematic review and meta-analysis aims to estimate the pooled prevalence of perioperative neurocognitive disorder in older non-cardiac surgical patients. METHOD: A comprehensive search of multiple databases was conducted from inception to January 24, 2024. This review included studies of non-cardiac surgical inpatients aged ≥60 years old who underwent perioperative cognitive assessments. The primary outcome was the prevalence of postoperative neurocognitive disorder or cognitive dysfunction (POCD). Data were analyzed using a random-effects model to calculate pooled prevalence rates. Quality assessment employed the Newcastle-Ottawa Scale and MOOSE guidelines. Meta-regression was performed with Open Meta Analyst and RStudio 4.3.3. RESULT: Thirty-nine studies (n = 12,921) were included with mean age of 70.0 ± 8.9 years and 44.3% women. The overall prevalence of POCD was 23% (95% CI: 20%, 27%) at day 7, 16% (95% CI: 7%, 25%) at 1 month, 10% (95% CI: 8%, 13%) at 3 months and 3% (95% CI: 2%, 4%) at 1 year (Figure 1). Our meta-regression showed a higher prevalence of POCD in abdominal surgery at day 7 (β = 0.13, 95% CI: 0.03-0.22, p = 0.01) and 3 months (β = 0.49, 95% CI: 0.40-0.58, p < 0.001), versus orthopedic surgeries. CONCLUSION: The overall prevalence of POCD in older non-cardiac surgical populations was 23%, 16%, 10%, and 3% at day 7, 1 month, 3 months, and 1 year, respectively. Abdominal surgery had a higher prevalence of POCD than orthopedic surgery. The significant risk of POCD calls for cognitive screening, risk mitigation and intervention to provide better perioperative care and improve surgical outcomes.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".