Predictors of abstinence, no heavy drinking days, and a 2‐level reduction in World Health Organization drinking levels during treatment for alcohol use disorder in the COMBINE study. (13th June 2022)

• Record Type: Journal Article
• Authors: Wallach, Joshua D.; Gueorguieva, Ralitza; Phan, Huong; Witkiewitz, Katie; Wu, Ran; O'Malley, Stephanie S.
• Is Part Of: Alcoholism. Volume 46:Number 7(2022)
• Page Start: 1331
• Page End: 1339
• Publication Date: 2022-06-13
• Subjects: alcohol use disorder -- clinical trials -- pharmacotherapy; Alcoholism -- Periodicals; Alcoholism -- Periodicals; Alcoolisme; Electronic journals; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.861005
• DOI: 10.1111/acer.14877
• Languages: English
• ISSNs: 0145-6008

Abstract

Abstract: Background: Data from trials of medications for alcohol use disorder (AUD) can be used to identify predictors of drinking outcomes regardless of treatment, which can inform the design of future trials with heterogeneous populations. Here, we identified predictors of abstinence, no heavy drinking days, and a 2‐level reduction in World Health Organization (WHO) drinking levels during treatment for AUD in the Combined Pharmacotherapies and Behavioral Interventions (COMBINE) Study. Methods: We utilized data from the COMBINE Study, a randomized placebo‐controlled trial evaluating the efficacy of naltrexone and acamprosate, both alone and in combination, for AUD ( n = 1168). A tree‐based machine learning algorithm was used to construct classification trees predicting abstinence, no heavy drinking days, and a 2‐level reduction in WHO drinking levels in the last 4 weeks of treatment, based on 89 baseline variables. Results: The final tree for predicting abstinence had one split based on consecutive days abstinent prior to randomization, with a higher proportion of subjects achieving abstinence among those classified as abstinent for >2 versus ≤2 consecutive weeks prior to randomization (66% vs. 29%). The final tree for predicting no heavy drinking days in the last 4 weeks of treatment had three splits based on consecutive days abstinent, age, and total Alcohol Dependence Scale score at baseline. Seventy‐three percent of the subjects classified as abstinent for >2 consecutive weeks prior to randomization had no heavy drinking days in the last 4 weeks of treatment. Among those classified as abstinent ≤2 consecutive weeks prior, three additional splits showed that younger subjects (age ≤44 years; 37%), and older subjects (age >44) with a total Alcohol Dependence Scale score >13 and complete abstinence (56%) or other drinking goals (35%), were less likely to have no heavy drinking days than older subjects with a total Alcohol Dependence Scale score ≤13 (67%). The final tree for predicting a 2‐level reduction in WHO levels had no splits. Conclusions: Consecutive days abstinent prior to randomization may predict abstinence and no heavy drinking days and total Alcohol Dependence Scale score and age may predict no heavy drinking days. The 2‐level reduction in WHO levels outcome may be less likely to discriminate based on multiple patient characteristics. Abstract : In this secondary evaluation of the COMBINE Study, a trial of pharmacotherapy for alcohol use disorder, a tree‐based machine learning algorithm identified consecutive days abstinent prior to randomization as a potential predictor of abstinence and no heavy drinking days. Alcohol Dependence Scale score and age may predict no heavy drinking days. The final classification tree for predicting a 2‐level reduction in WHO levels had no splits. Opportunities exist to use the results from classification trees to identify/recruit subjects into future trials.