Melatonin for preventing postoperative delirium in elderly patients: A multicenter randomized placebo-controlled pilot study
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium (POD) in older adults is associated with high risk of morbidity and mortality. With limited treatment options, prevention is essential. Melatonin has been suggested to prevent delirium through regulating the sleep-wake cycle and serotonin metabolism, which has been shown to be disrupted in patients with POD. However, the evidence regarding the use of melatonin for POD prevention is limited and inconclusive. METHODS: Our multicenter, 2-arm, parallel-group, feasibility randomized controlled trial evaluated the effect of melatonin on POD incidence after noncardiac surgery in patients >65 years (n = 120). Patients were randomized to 3 mg oral melatonin or placebo once preoperatively and for 7 days postoperatively. Patients were assessed twice daily for delirium and followed at 3 months postoperatively. Feasibility outcomes were recruitment rate, medication adherence, and proportion completing 3-month follow-up. Clinical outcomes were delirium incidence, sleep quality, institutional discharge, and cognitive status at 3 months. RESULTS: Between September 2021 and June 2023, 85 patients were randomized (~1 patient/wk); of these, 92.9% adhered to study medications and 87.1% completed the 3-month follow-up. POD occurred in 9 patients with no statistical difference between the groups (melatonin group, n = 7; placebo group, n = 2; adjusted odds ratio: 1.12; 95% confidence interval: 0.006-150.1). There were no differences in any other clinical outcomes. Pandemic-related challenges, including interruption of surgeries and restrictions on research procedures impacted feasibility and the study was terminated early due to futility. CONCLUSIONS: Based on our observations, a sample size of >1000 patients is required for a definitive trial to evaluate the role of melatonin in reducing the incidence of POD. Design changes need to be considered to address feasibility challenges and ongoing post-pandemic modifications to patient care.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".