Identifying responders to gabapentin for the treatment of alcohol use disorder: an exploratory machine learning approach.
Background Gabapentin, an anticonvulsant medication, has been proposed as a treatment for alcohol use disorder (AUD). A multisite study tested gabapentin enacarbil extended-release (GE-XR; 600 mg/twice a day), a prodrug formulation, combined with a computerized behavioral intervention, for AUD. In t...
| Publicado en: | Alcohol & Alcoholism Vol. 60; no. 3; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185488926&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185488926 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07350414 FA3 jtl: Alcohol & Alcoholism issn: 07350414 maglogo: N pubinfo: dt: May2025 vid: 60 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 185488926 185488926 185488926 10.1093/alcalc/agaf010 185488926 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying responders to gabapentin for the treatment of alcohol use disorder: an exploratory machine learning approach. aug: au: Ray, Lara A Grodin, Erica N Baskerville, Wave-Ananda Donato, Suzanna Cruz, Alondra Montoya, Amanda K affil: Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Los Angeles, CA 90095, United States sug: subj: Alcoholism Drug Therapy Alcoholism Psychosocial Factors Gabapentin Administration and Dosage Gabapentin Therapeutic Use Anticonvulsants Therapeutic Use Machine Learning Methods Treatment Outcomes Human Funding Source Male Female Adult Middle Age Exploratory Research Secondary Analysis Multicenter Studies GABA Therapeutic Use GABA Analogs and Derivatives Acids, Acyclic Self-Efficacy Cognition Substance Withdrawal Syndrome Drug Development Decision Trees Iatrogenic Disease Placebos Motivation Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background Gabapentin, an anticonvulsant medication, has been proposed as a treatment for alcohol use disorder (AUD). A multisite study tested gabapentin enacarbil extended-release (GE-XR; 600 mg/twice a day), a prodrug formulation, combined with a computerized behavioral intervention, for AUD. In this multisite trial, the gabapentin GE-XR group did not differ significantly from placebo on the primary outcome of percent of subjects with no heavy drinking days. Despite the null findings, there is considerable interest in using machine learning methods to identify responders to GE-XR. The present study applies interaction tree machine learning methods to identify positive and iatrogenic (i.e. individuals who responded better to placebo than to GE-XR) treatment responders in the trial. Methods Baseline characteristics taken from the multisite trial were examined as potential moderators of treatment response using qualitative interaction trees (QUINT; N = 338; 223 M/115F). QUINT models are an exploratory decision tree approach that iteratively splits the data into leaves based on predictor variables to maximize a specific criterion. Results Analyses identified key factors that are associated with the efficacy (or iatrogenic effects) of GE-XR for AUD. Such factors are baseline drinking levels, motivation for change, confidence in their ability to reach drinking goals (i.e. self-efficacy), cognitive impulsivity, and baseline anxiety levels. Conclusion Baseline drinking levels and anxiety levels may be associated with the protracted withdrawal syndrome, previously implicated in the clinical response to gabapentin. However, these analyses underscore motivation for change and self-efficacy as predictors of clinical response to GE-XR, suggesting these established constructs should receive further attention in gabapentin research and clinical practice. Multiple studies using different machine learning methods are valuable as these novel analytic tools are applied to medication development for AUD. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|