Component II - Predicting Treatment Outcome in Adolescent ADD Although an abundance of studies have associated comorbid psychiatric disorders, as well as some psychological constructs, with a poor response to Alcohol Use Disorder (ADD) treatment, the neurobiological processes underlying these associations have received scant attention. The proposed study will attempt to elucidate some of these processes. For example, we will examine whether AUD-positive youths exhibit neurophysiological and genetic signs that are more strongly correlated with outcome than Conduct Disorder. Also, we will ask whether these signs improve the prediction of outcome beyond that predicted by selfefficacy and readiness to change. Because neurophysiological status and genotype are objectively measured, they are not influenced by the variable reliabilities of the adolescent patient's report and the clinician's judgment. Accordingly, they offer an opportunity to estimate risk for treatment failure with a greater level of precision and may eventually prove valuable in clinical settings. N=235, 13-18 y.o. adolescents meeting DSM-IV criteria for AUD will complete a standardized treatment consisting of 2 weeks of individual Motivation Enhancement Therapy (MET) followed by 8-weekly sessions of group Cognitive Behavioral Therapy (CBT). The therapy will utilize an established treatment manual. All participants will be assessed for outcomes at the end of the 10 week treatment and at quarterly ntervals during a 12-month post-treatment period. Outcomes will be measured by the number of days of drinking and heavy drinking, and days of marijuana use. The primary analysis will employ structural equation modeling techniques to evaluate a causal path linking treatment outcome to GABRA2 genotype, electroencephalographic and event related potential signs of frontal brain dysfunction, and conduct disorder ymptoms. Relevance to Public Health: The proposed study will provide valuable data regarding predictors of treatment outcome in adolescents with Alcohol Use Disorders. It will examine the typical predictors, ncluding comorbidity, but it will also explore the value of objective laboratory measures, including candidate genes. The findings may have implications for devising optimal treatments based upon a systematic assessment of risk for treatment success or failure.
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