Use of Dual Orexin Receptor Antagonists for the Prevention of Delirium: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Delirium and dementia are leading causes of cognitive impairment in older adults and have a complex and interconnected relationship. Delirium can exacerbate cognitive decline in dementia, but it is also an independent risk factor for the development of long-term cognitive impairment and new-onset dementia. Although many medications have been evaluated, studies have not shown any medication to be effective for the prevention or treatment of delirium. Their use also carry a high risk of adverse effects where the balance of benefits and harms is unclear in older adults and those with dementia. Dual orexin receptor antagonists (DORAs) have emerged as a new, potentially safer class of medications for the management of delirium. We conducted a systematic review to assess the effects of DORAs on the prevention and treatment of delirium METHODS: We searched MEDLINE, EMBASE, and CENTRAL for primary studies evaluating the use of DORAs for delirium prevention or treatment compared to non-users. Citation screening, data abstraction, risk of bias, and quality of evidence assessments were conducted in duplicate. Random-effects model meta-analyses were conducted. RESULTS: Three randomized controlled trials (345 participants) and 6 observational studies (1,943 participants) met inclusion criteria. In pooled meta-analysis, DORA use led to a clinically and statistically significant decrease in the overall incidence of delirium (OR 0.28, 95% CI 0.18-0.42). DORAs decreased the odds of delirium in both post-operative patients (OR 0.24, 95% CI 0.07-0.83) and intensive care unit patients (OR 0.28, 95% CI 0.18-0.45). DISCUSSION: The preventative effect of DORAs on delirium was consistently observed in different study designs (RCTs vs observational), different clinical settings (post-operative vs. ICU) and different DORA agents. The magnitude of effect appears to be both statistically and clinically significant and may represent an important intervention to reduce the impact of delirium on long-term cognitive impairment and decline. However, data on their use in older adults, patients living with dementia and the treatment of delirium is limited. CONCLUSION: DORAs appear to reduce the incidence of delirium in hospitalized patients, but additional research is required to evaluate their effects in older adults and those living with dementia.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".