A Systematic Review and Meta-analysis Examining the Impact of Incident Postoperative Delirium on Mortality
Bibliographic record
Abstract
BACKGROUND: Delirium is an acute and reversible geriatric syndrome that represents a decompensation of cerebral function. Delirium is associated with adverse postoperative outcomes, but controversy exists regarding whether delirium is an independent predictor of mortality. Thus, we assessed the association between incident postoperative delirium and mortality in adult noncardiac surgery patients. METHODS: A systematic search was conducted using Cochrane, MEDLINE/PubMed, Cumulative Index to Nursing and Allied Health Literature, and Embase. Screening and data extraction were conducted by two independent reviewers. Pooled-effect estimates calculated with a random-effects model were expressed as odds ratios with 95% CIs. Risk of bias was assessed using the Cochrane Risk of Bias Tool for Non-Randomized Studies. RESULTS: A total of 34 of 4,968 screened citations met inclusion criteria. Risk of bias ranged from moderate to critical. Pooled analysis of unadjusted event rates (5,545 patients) suggested that delirium was associated with a four-fold increase in the odds of death (odds ratio = 4.12 [95% CI, 3.29 to 5.17]; I = 24.9%). A formal pooled analysis of adjusted outcomes was not possible due to heterogeneity of effect measures reported. However, in studies that controlled for prespecified confounders, none found a statistically significant association between incident postoperative delirium and mortality (two studies in hip fractures; n = 729) after an average follow-up of 21 months. Overall, as study risk of bias decreased, the association between delirium and mortality decreased. CONCLUSIONS: Few high-quality studies are available to estimate the impact of incident postoperative delirium on mortality. Studies that controlled for prespecified confounders did not demonstrate significant independent associations of delirium with mortality.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".