Evidence map›Paper›PMID 35616436›Full record

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.

Joshua D Wallach, Ralitza Gueorguieva, Huong Phan, Katie Witkiewitz, Ran Wu, Stephanie S O'Malley

Open access · greenAbstract readRandomized Controlled Trial
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 21% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Joshua D WallachDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0002-2816-6905
Ralitza GueorguievaDepartment of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Huong PhanDepartment of Statistics, University of Washington, Washington, USA.
Katie WitkiewitzDepartment of Psychology, Center on Alcohol, Substance Use, and Addictions, Albuquerque, New Mexico, USA.ORCID 0000-0002-1086-3067
Ran WuDepartment of Psychiatry, Yale Medical School, New Haven, Connecticut, USA.
Stephanie S O'MalleyDepartment of Psychiatry, Yale Medical School, New Haven, Connecticut, USA.ORCID 0000-0002-1976-2369
Yale University · USCanadian Centre on Substance Use and Addiction · CAUniversity of Washington · USYale New Haven Health System · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Identifying Patient Subgroups That Are Most Likely To Benefit From Medications Used To Treat Alcohol Use DisorderK01AA028258 · NIAAA · YALE UNIVERSITY · PI WALLACH, JOSHUA DAVID · 2021 to 2025
$892k
NCATS NIH HHS UL1 TR001863NIAAA NIH HHS K01 AA028258NIAAA NIH HHS K01AA028258
6 · The paper itself

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

AlcoholismAdultAlcohol AbstinenceAlcohol DrinkingHumansNaltrexoneTreatment OutcomeWorld Health OrganizationNaltrexonealcohol use disorderclinical trialspharmacotherapy

Identifiers

PMID35616436
PMCPMC9887652
OpenAlexW4281564615

What OpenQuestion holds

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Registered trials

None linked

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.