Trial reportAlcoholism, clinical and experimental research2022
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.
Trial report in Alcoholism, clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Digital Cognitive Behavioral Therapy Reduces Heavy Drinking Among a Non-Treatment Sample of People With Alcohol Use Disorder: A Feasibility Trial With CBT4CBT.Alcohol, clinical & experimental research · 2026Trial
- Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders.Addiction (Abingdon, England) · 2023Trial
- Trends in Alcohol Use Before and During the COVID-19 Pandemic Among Women Living With and Without HIV in the United States (2017-2022).AIDS and behavior · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Empirical Derivation and Prediction of Treatment Trajectories in Harmonized AUD Clinical Trial Datasets.Addiction biology · 2025Article
- A Meta-Analysis of Task-Based fMRI Studies on Alcohol Use Disorder.Brain sciences · 2025Review
- Combination of Drugs in the Treatment of Alcohol Use Disorder: A Meta-Analysis and Meta-Regression Study.Brain sciences · 2025Review
- Perspective on Using Artificial Intelligence in Alcohol Research and Treatment: Opportunities and Ethical Considerations.Alcohol research : current reviews · 2025Review
- Analyzing Dropout in Alcohol Recovery Programs: A Machine Learning Approach.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundData 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.
methodsWe 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.
resultsThe 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.
conclusionsConsecutive 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.