Treatment of alcohol withdrawal syndrome in the emergency department: a rapid review
Bibliographic record
Abstract
Abstract Background Patients who experience harms from alcohol and other substance use often seek care in the emergency department (ED). Many ED patients with alcohol use disorder will require management of alcohol withdrawal. ED clinicians are responsible for risk-stratifying these patients under time and resource constraints and must reliably identify those who are safe for outpatient management versus those who require more intensive levels of care. Published guidelines for alcohol withdrawal are largely limited to the primary care and outpatient settings, and do not provide specific guidance for ED use. The purpose of this review was to synthesize published evidence on the treatment of alcohol withdrawal syndrome specifically in the emergency department setting. Methods We conducted a rapid review by searching MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (1980 to 2020). We also searched for grey literature on Google and hand-searched the conference abstracts of relevant addiction medicine and emergency medicine professional associations (2015 to 2020). We included interventional and observational studies that reported outcomes of clinical interventions aimed at treating alcohol withdrawal syndrome in adults in the ED. Results We identified 13 studies that met inclusion criteria for our review (7 randomized controlled trials and 6 observational studies). Most studies were at high/serious risk of bias. We divided studies based on intervention and summarized evidence narratively. Benzodiazepines decrease alcohol withdrawal seizure recurrence, but no clear evidence supports the use of one benzodiazepine over another. It is unclear if symptom-triggered benzodiazepine protocols are effective for use in the ED. More evidence is needed to determine if phenobarbital, with or without benzodiazepines, can be used safely and effectively to treat alcohol withdrawal in the ED. Phenytoin does not have evidence of effectiveness at preventing withdrawal seizures in the ED. Conclusions Few studies have evaluated the safety and efficacy of pharmacotherapies for alcohol withdrawal specifically in the ED setting. Benzodiazepines are the most evidence-based treatment for alcohol withdrawal in the ED. Pharmacotherapies that have demonstrated benefit for treatment of alcohol withdrawal in other inpatient and outpatient settings need to be evaluated in the ED setting before routine use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".