Phenobarbital Dosing for the Treatment of Alcohol Withdrawal Syndrome: A Review of the Literature.
Lindsay BrooksJustin P ReinertPublished in: The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians (2024)
Objective: To determine the most appropriate phenobarbital dosing regimen by evaluating the safety and efficacy of the drug when specifically used in alcohol withdrawal syndrome (AWS). Data sources: A comprehensive literary search was conducted using PubMed and bibliographic mining in October 2023. Study selection and data extraction: An established monotherapy phenobarbital regimen needed to be established within the article to be included in analysis. Location of implementation was not a deterrent to evaluation, nor was the route of phenobarbital administration. Data synthesis: Six publications were evaluated in this review, and two main phenobarbital dosing regimens emerged. While fix-based dosing strategies and weight-based dosing strategies resulted, the dosing within the regimens resulted in the same or relatively similar doses employed, respectively. Each of the studies had a statistically significant decrease in their primary outcome being studied, and the use of phenobarbital as monotherapy was proven to improve AWS symptoms, significantly decrease intensive care unit and hospital length of stay, decrease the use of adjunctive medications, decrease the use of a ventilator, and prevent seizures. Conclusions: Despite benzodiazepines having been the clinical first-line therapy for AWS, research shows that the pharmacokinetic stability and clinical benefits of phenobarbital are in support creation of phenobarbital protocols, as monotherapy, in hospitals or institutions for patients with AWS.
Keyphrases
- intensive care unit
- combination therapy
- healthcare
- electronic health record
- big data
- open label
- primary care
- mechanical ventilation
- physical activity
- emergency department
- clinical trial
- randomized controlled trial
- weight loss
- depressive symptoms
- data analysis
- acute respiratory distress syndrome
- deep learning
- drug induced