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WORLD HOSPITAL DIRECTORY
Addiction Medical Alcohol and Alcoholism - current issue
<span class="paragraphSection"><div class="boxTitle">Abstract</div><div class="boxTitle">Aims</div>Magnetic resonance imaging (MRI) studies have identified brain structural predictors of treatment response in individuals with alcohol use disorder (AUD) but with varying findings and primarily in male veterans. The present study investigated cortical surface area and thickness (CT) as predictors of brief intervention response in community-based adults with AUD.<div class="boxTitle">Methods</div>Sixty-five non-treatment-seeking adults with AUD (44.6% male, aged 33.2 ± 1.3 years) underwent an MRI and received a brief intervention comprising personalized feedback and motivational interviewing, with follow-up ~6–8 weeks later to quantify changes in drinks/week (DPW), the primary outcome. Eighteen bilateral <span style="font-style:italic;">a priori</span> regions of interest (ROIs) were used to predict DPW at follow-up, adjusting for baseline drinking. Significant predictors were examined with secondary outcomes, percent drinking and heavy drinking days, and in relation to out-of-scanner measures of impulsivity and comorbidities.<div class="boxTitle">Results</div>Participants exhibited significant decreases in alcohol consumption in response to the brief intervention. Eight bilateral CT ROIs in the frontal, temporal, and occipital lobes, most notably medial orbitofrontal, middle temporal, and lateral occipital gyri, predicted DPW; however, only three predicted the secondary outcomes. Significant associations were observed between CT in frontal and occipital regions and impulsivity (delay discounting, lack of premeditation), executive functioning, anxiety, and stress.<div class="boxTitle">Conclusions</div>Thinner frontal, temporal, and occipital ROIs predicted poorer brief intervention response, with notable overlap with brain regions previously implicated in AUD. Clarifying whether these regions reflect premorbid or acquired differences and, if the latter, the potential for recovery of cortical gray matter following drinking reductions are future priorities.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div><div class="boxTitle">Background</div>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.<div class="boxTitle">Methods</div>Baseline characteristics taken from the multisite trial were examined as potential moderators of treatment response using qualitative interaction trees (QUINT; <span style="font-style:italic;">N</span> = 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.<div class="boxTitle">Results</div>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.<div class="boxTitle">Conclusion</div>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.</span>
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