Interventions to prevent and treat delirium: An umbrella review of randomized controlled trials
Bibliographic record
Abstract
Delirium is a common condition across different settings and populations. The interventions for preventing and managing this condition are still poorly known. The aim of this umbrella review is to synthesize and grade all preventative and therapeutic interventions for delirium. We searched five databases from database inception up to March 15th, 2023 and we included meta-analyses of randomized controlled trials (RCTs) to decrease the risk of/the severity of delirium. From 1959 records after deduplication, we included 59 systematic reviews with meta-analyses, providing 110 meta-analytic estimates across populations, interventions, outcomes, settings, and age groups (485 unique RCTs, 172,045 participants). In surgery setting, for preventing delirium, high GRADE evidence supported dexmedetomidine (RR=0.53; 95%CI: 0.46-0.67, k=13, N=3988) and comprehensive geriatric assessment (OR=0.46; 95%CI=0.32-0.67, k=3, N=496) in older adults, dexmedetomidine in adults (RR=0.33, 95%CI=0.24-0.45, k=7, N=1974), A2-adrenergic agonists after induction of anesthesia (OR= 0.28, 95%CI= 0.19-0.40, k=10, N=669) in children. High certainty evidence did not support melatonergic agents in older adults for delirium prevention. Moderate certainty supported the effect of dexmedetomidine in adults and children (k=4), various non-pharmacological interventions in adults and older people (k=4), second-generation antipsychotics in adults and mixed age groups (k=3), EEG-guided anesthesia in adults (k=2), mixed pharmacological interventions (k=1), five other specific pharmacological interventions in children (k=1 each). In conclusion, our work indicates that effective treatments to prevent delirium differ across populations, settings, and age groups. Results inform future guidelines to prevent or treat delirium, accounting for safety and costs of interventions. More research is needed in non-surgical settings.
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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 | 0.071 | 0.211 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.020 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".