The prevalence of unrecognized cognitive impairment in geriatric surgical patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Pre‐existing cognitive impairment is emerging as a predictor of poor postoperative outcomes in older surgical patients (Guenther, 2020). Due to a substantial under‐diagnosis of cognitive impairment in the elderly, the prevalence of undiagnosed cognitive impairment has not been well defined in those undergoing surgery (Elman, 2018). The objective of this systematic review and meta‐analysis is to determine the pooled prevalence of unrecognized cognitive impairment in the elderly undergoing elective noncardiac surgery. Method An expert librarian conducted the literature search, which included MEDLINE (Ovid), PubMed (non‐MEDLINE records only), Embase, Cochrane Central, Cochrane Database of Systematic Reviews, PsycINFO, and Emcare Nursing for relevant articles from 1946 to April 2021. Inclusion criteria were (1) patients ≥ 60 years old undergoing elective non‐cardiac surgeries; (2) preoperative cognitive impairment assessed by validated cognitive screening tests; (3) published in English language. Descriptive analysis was conducted for the cognitive impairment group. Data were extracted from each study and prevalence of cognitive impairment with 95% confidence interval (CI) was calculated for each study. We used the random effect model to calculate the pooled prevalence value with 95% CI. Comprehensive meta‐analysis software was used for statistical analysis (Borenstein, 2013). Result Of 8,895 citations, 20 studies were included consisting of 4,159 patients undergoing a variety of elective non‐cardiac surgeries. All but one study were prospective cohort studies, with the exception being a cross‐sectional study. The mean age among patients with unrecognized cognitive impairment was 73.1 ± 7.2 years, and 43.6% were male. The pooled prevalence of cognitive impairment was 40.2% (95% CI: 31.0%, 50.1%; P=0.005; predictive interval: ‐1.47 to 2.27 (Table 1). The forest plot displays non‐overlapping CI's, indicating high heterogeneity within the data (I2: 97%). Influential analysis of any study showed that removal of each study did not significantly alter the pooled prevalence result. Meta‐regression analysis based on age, gender, and body mass index (BMI) did not change the final inference of our results. Conclusion The prevalence of unrecognized cognitive impairment is very high at 40.2% in geriatric patients undergoing elective non‐cardiac surgery.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".