Antipsychotics in the Treatment of Delirium in Critically Ill Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials*
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic review and meta-analysis assessing whether the use of antipsychotic medications in critically ill adult patients with delirium impacts patient-important outcomes. DATA SOURCES: A medical librarian searched Ovid MEDLINE, EMBASE, APA PsycInfo, and Wiley's Cochrane Library as well as clinicaltrials.gov and the World Health Organization International Clinical Trials Registry Platform up to November 2023. STUDY SELECTION: Independently and in duplicate, reviewers screened abstracts and titles for eligibility, then full text of qualifying studies. We included parallel-group randomized controlled trials (RCTs) that included critically ill adult patients with delirium. The intervention group was required to receive antipsychotic medications at any dose, whereas the control group received usual care or placebo. DATA EXTRACTION: Reviewers extracted data independently and in duplicate using a piloted abstraction form. Statistical analyses were conducted using RevMan software (version 5.4). DATA SYNTHESIS: Five RCTs ( n = 1750) met eligibility criteria. The use of antipsychotic medications compared with placebo did not increase the number of delirium- or coma-free days (mean difference 0.90 d; 95% CI, -0.32 to 2.12; moderate certainty), nor did it result in a difference in mortality, duration of mechanical ventilation, ICU, or hospital length of stay. The use of antipsychotics did not result in an increased risk of adverse events (risk ratio 1.27; 95% CI, 0.71-2.30; high certainty). Subgroup analysis of typical versus atypical antipsychotics did not identify any subgroup effect for any outcome. CONCLUSIONS: In conclusion, our systematic review and meta-analysis demonstrated with moderate certainty that there is no difference in delirium- or coma-free days when delirious critically ill adults are treated with antipsychotic medications. Further studies in the subset of patients with hyperactive delirium may be of benefit.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.038 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".