Occurrence and risk factors for post-stroke delirium: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Delirium is a significant health concern in acute stroke patients. We aim to systematically summarize existing evidence to conduct a meta-analysis to quantify the occurrence and risk factors for delirium after acute stroke. METHOD: PubMed, EMBASE and MEDLINE were searched from inception to Feb. 2023 for prospective observational studies that reported the incidence or prevalence of post-stroke delirium and/or evaluated potential risk factors. The search strategy was created using controlled vocabulary terms and text words for stroke and delirium. We performed a meta-analysis of the estimates for occurrence and risk factors using random-effects models. Meta-regression and subgroup meta-analyses were conducted to explore the sources of heterogeneity. Study quality and quality of evidence were assessed using the customized Newcastle-Ottawa Scale and GRADE, respectively. RESULTS: =96.2 %). The pooled occurrence of hyperactive, hypoactive, and mixed delirium was 8.5 %, 5.7 % and 5.0 %, respectively. Study location, delirium assessment method and stroke type independently affected the heterogeneity of the pooled estimate of delirium. Statistically significant risk factors were older age, low education level, cigarette smoking, alcohol drinking, atrial fibrillation, lower ADL level, higher pre-stroke mRS score, premorbid cognitive impairment or dementia, aphasia, total anterior circulation impairment, higher National Institute of Health Stroke Scale score and infection. CONCLUSIONS: Delirium affected 1 in 4 acute stroke patients, although reported rates may depend on assessment method and stroke type. Timely prevention, recognition and intervention require prioritizing patients with dominant risk factors.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.052 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".