Predicting adherence to treatment for methamphetamine dependence from neuropsychological and drug use variables.

TitlePredicting adherence to treatment for methamphetamine dependence from neuropsychological and drug use variables.
Publication TypeJournal Article
Year of Publication2009
AuthorsDean, AC, London ED, Sugar CA, Kitchen CMR, Swanson A-N, Heinzerling KG, Kalechstein AD, Shoptaw S
JournalDrug and alcohol dependence
Volume105
Issue1-2
Pagination48-55
Date Published2009 Nov 1
ISSN1879-0046
KeywordsAdult, Affect, Amphetamine-Related Disorders, Analysis of Variance, Antidepressive Agents, Second-Generation, Bupropion, Cognitive Therapy, Crime, Female, Humans, Male, Memory, Short-Term, Methamphetamine, Neuropsychological Tests, Patient Compliance, Predictive Value of Tests, Reaction Time, Smoking, Social Environment, Socioeconomic Factors, Treatment Outcome
Abstract

Although some individuals who abuse methamphetamine have considerable cognitive deficits, no prior studies have examined whether neurocognitive functioning is associated with outcome of treatment for methamphetamine dependence. In an outpatient clinical trial of bupropion combined with cognitive behavioral therapy and contingency management (Shoptaw, S., Heinzerling, K.G., Rotheram-Fuller, E., Steward, T., Wang, J., Swanson, A.N., De La Garza, R., Newton, T., Ling, W., 2008. Randomized, placebo-controlled trial of bupropion for the treatment of methamphetamine dependence. Drug Alcohol Depend 96, 222-232.), 60 methamphetamine-dependent adults completed three tests of reaction time and working memory at baseline. Other variables that were collected at baseline included measures of drug use, mood/psychiatric functioning, employment, social context, legal status, and medical status. We evaluated the relative predictive value of all baseline measures for treatment outcome using Classification and Regression Trees (CART; Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Wadsworth, Belmont, CA.), a nonparametric statistical technique that produces easily interpretable decision rules for classifying subjects that are particularly useful in clinical settings. Outcome measures were whether or not a participant completed the trial and whether or not most urine tests showed abstinence from methamphetamine abuse. Urine-verified methamphetamine abuse at the beginning of the study was the strongest predictor of treatment outcome; two psychosocial measures (e.g., nicotine dependence and Global Assessment of Functioning) also offered some predictive value. A few reaction time and working memory variables were related to treatment outcome, but these cognitive measures did not significantly aid prediction after adjusting for methamphetamine usage at the beginning of the study. On the basis of these findings, we recommend that research groups seeking to identify new predictors of treatment outcome compare the predictors to methamphetamine usage variables to assure that unique predictive power is attained.

DOI10.1042/AN20110027
Alternate JournalDrug Alcohol Depend