Incidence and relative risk of delirium after major surgery for patients with pre‐operative depression: a systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Background Delirium is a common and potentially serious complication after major surgery. A previous history of depression is a known risk factor for experiencing delirium in patients admitted to the hospital, but the generalised risk has not been estimated in surgical patients. Methods We conducted a systematic review and meta‐analysis of studies reporting the incidence or relative risk (or relative odds) of delirium in the immediate postoperative period for adults with pre‐operative depression. We included studies that defined depression as either a formal pre‐existing diagnosis or having clinically important depressive symptoms measured using a patient‐reported instrument before surgery. Multilevel random effects meta‐analyses were used to estimate the pooled incidences and pooled relative risks. We also conducted subgroup analyses by various study‐level characteristics to identify important moderators of pooled estimates. Results Forty‐two studies (n = 4,664,051) from five continents were included. The pooled incidence of postoperative delirium for patients with pre‐operative depression was 29% (95%CI 17–43%, I 2 = 99.0%), compared with 15% (95%CI 6–28%, I 2 = 99.8%) in patients without pre‐operative depression and 21% (95% CI 11–33%, I 2 = 99.8%) in the cohorts overall. For patients with pre‐operative depression, the risk of delirium was 1.91 times greater (95%CI 1.68–2.17, I 2 = 42.0%) compared with patients without pre‐operative depression. Conclusions Patients with a previous diagnosis of depression or clinically important depressive symptoms before surgery have substantially greater risk of experiencing delirium after surgery. Clinicians and patients should be informed of these increased risks. Robust screening and other risk mitigation strategies for postoperative delirium are warranted, especially for patients with pre‐operative depression.
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 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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".