Management of Acute Alcohol Withdrawal Syndrome in Critically Ill Patients
Bibliographic record
Abstract
Approximately 16-31% of patients in the intensive care unit (ICU) have an alcohol use disorder and are at risk for developing alcohol withdrawal syndrome (AWS). Patients admitted to the ICU with AWS have an increased hospital and ICU length of stay, longer duration of mechanical ventilation, higher costs, and increased mortality compared with those admitted without an alcohol-related disorder. Despite the high prevalence of AWS among ICU patients, no guidelines for the recognition or management of AWS or delirium tremens in the critically ill currently exist, leading to tremendous variability in clinical practice. Goals of care should include immediate management of dehydration, nutritional deficits, and electrolyte derangements; relief of withdrawal symptoms; prevention of progression of symptoms; and treatment of comorbid illnesses. Symptom-triggered treatment of AWS with γ-aminobutyric acid receptor agonists is the cornerstone of therapy. Benzodiazepines (BZDs) are most studied and are often the preferred first-line agents due to their efficacy and safety profile. However, controversy still exists as to who should receive treatment, how to administer BZDs, and which BZD to use. Although most patients with AWS respond to usual doses of BZDs, ICU clinicians are challenged with managing BZD-resistant patients. Recent literature has shown that using an early multimodal approach to managing BZD-resistant patients appears beneficial in rapidly improving symptoms. This review highlights the results of recent promising studies published between 2011 and 2015 evaluating adjunctive therapies for BZD-resistant alcohol withdrawal such as antiepileptics, baclofen, dexmedetomidine, ethanol, ketamine, phenobarbital, propofol, and ketamine. We provide guidance on the places in therapy for select agents for management of critically ill patients in the presence of AWS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".