4.5 Article

An analysis of moderators in the COMBINE study: Identifying subgroups of patients who benefit from acamprosate

Journal

EUROPEAN NEUROPSYCHOPHARMACOLOGY
Volume 25, Issue 10, Pages 1586-1599

Publisher

ELSEVIER
DOI: 10.1016/j.euroneuro.2015.06.006

Keywords

Alcohol dependence; Moderator effects; Classification and regression trees; Subgroups with enhanced treatment effect; Clinical trials

Funding

  1. Abott Laboratories
  2. Eli Lilly & Company Lundbeck
  3. Pfizer
  4. Ethypharma

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The goal of the current study was to use tree-based methods to identify moderators of acamprosate effect on abstinence from heavy drinking in COMBINE, the largest study of pharmacotherapy for alcoholism in the United States to date. We used three different tree-based methods for identification of subgroups with enhanced treatment response on acamprosate based on over 100 predictors measured at baseline in COMBINE. No heavy drinking during the last two months of treatment was the considered outcome. All three methods identified consecutive days of abstinence prior to treatment as the most important moderator of treatment effect. Acamprosate was beneficial for participants with shorter abstinence (1 week or less) especially when body mass index was low or normal. In this group, 46% of participants receiving active acamprosate abstained from heavy drinking compared to 23% of those receiving placebo acamprosate. Prior treatment, age, drinking goal and cognitive inefficiency were identified as moderators of acamprosate effects by one of the three methods. In conclusion, acamprosate may be beneficial for participants with shorter abstinence who are not overweight or obese. One hypothesis for this finding is that this subgroup may have greater glutamatergic hyperactivity, a target of acamprosate, and may achieve better drug plasma levels based on their lower BMI. In contrast, those with extended pretreatment abstinence who have an otherwise good prognosis did not benefit from acamprosate. Further validation of the results in independent data sets is necessary. (C) 2015 Elsevier B.V. and ECNP. All rights reserved.

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