Melatonin Use in the ICU: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Melatonin has wide-ranging effects on the body, including the regulation of circadian rhythm, and potentiation of cellular immune and antioxidant activities. In critically ill patients, endogenous melatonin has been shown to be markedly deranged and reduced. Therefore, the purpose of this systematic review and meta-analysis was to determine if exogenous supplementation of melatonin improves patient-centered outcomes. DATA SOURCES: We searched five electronic databases. STUDY SELECTION: Randomized clinical trials (RCTs) that compared melatonin to no melatonin in adults admitted to the ICU were identified. DATA EXTRACTION: We aggregated data as relative risks, mean differences (MDs), and standard mean differences (SMDs) using a random-effects model. Supporting evidence for each effect was evaluated for certainty using the Grading Recommendations, Assessment, Development, and Evaluations approach. DATA SYNTHESIS: In total, 32 RCTs ( n = 3895 patients) were included. We found that melatonin may reduce delirium (relative risk [RR] 0.72; 95% CI, 0.58-0.89; low certainty), may slightly reduce ICU length of stay (MD -0.57 d; 95% CI, -0.95 to -0.18 d; low certainty), and may improve reported sleep quality (SMD 0.54; 95% CI, 0.01-1.07; low certainty). Melatonin may result in a slight reduction in the frequency of adverse events (low certainty). Evidence was uncertain with regards to the frequency of sleep awakenings, anxiety level, agitation, and post-traumatic stress disorder incidence (all very low certainty), as well as to ICU mortality and post-ICU functional status (both low certainty). CONCLUSIONS: Our findings suggest that melatonin administration in the critically ill may improve perceived sleep and reduce delirium, without increasing adverse effects. Certainty of evidence was negatively affected by the risk of bias and inconsistency. Future RCTs should focus on identifying optimal dosing, administration timing, improving measurements of sleep outcomes, and target populations.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".