Postoperative Outcomes in Elderly Patients Undergoing Cardiac Surgery With Preoperative Cognitive Impairment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Older patients with preoperative cognitive impairment are at risk for increased postoperative complications after noncardiac surgery. This systematic review and meta-analysis aimed to determine the association between preoperative cognitive impairment and dementia and postoperative outcomes in older surgical patients after cardiac surgery. METHODS: Eight electronic databases were searched from inception to January 4, 2022. Inclusion criteria were cardiac surgery patients ≥60 years of age; preoperative cognitive impairment; ≥1 postoperative complication reported; comparator group with no preoperative cognitive impairment; and written in English. Using a random-effects model, we calculated effect sizes as odds ratio (OR) and standardized mean differences (SMDs). Risk of random error was assessed by applying trial sequential analysis. RESULTS: Sixteen studies (62,179 patients) were included. Preoperative cognitive impairment was associated with increased risk of delirium in older patients after cardiac surgery (70.0% vs 20.5%; OR, 8.35; 95% confidence interval [CI], 4.25-16.38; I 2 , 0%; P < .00001). Cognitive impairment was associated with increased hospital length of stay (LOS; SMD, 0.36; 95% CI, 0.20-0.51; I 2 , 22%; P < .00001) and intensive care unit (ICU) LOS (SMD, 0.39; 95% CI, 0.09-0.68; I 2 , 70%; P = .01). No significant association was seen for 30-day mortality (1.7% vs 1.1%; OR, 2.58; 95% CI, 0.64-10.44; I 2 , 55%; P = .18). CONCLUSIONS: In older patients undergoing cardiac surgery, cognitive impairment was associated with an 8-fold increased risk of delirium, a 5% increase in absolute risk of major postoperative bleeding, and an increase in hospital and ICU LOS by approximately 0.4 days. Further research on the feasibility of implementing routine neurocognitive testing is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".