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Saturday, November 29, 2008

Patient predictors of alcohol treatment outcome: A systematic review
Journal of Substance Abuse Treatment Volume 36, Issue 1, January 2009, Pages 75-86


Patient characteristics as predictors of alcohol use disorder treatment outcome were examined on three levels, identifying whether or not variables were significant predictors of drinking-related outcome in univariate analysis, in multivariate analysis, and in multivariate analyses limited to studies including several “key predictors.” Also, a model was developed to predict total percentage of variance in treatment outcome accounted for in each study using each of the key predictors and a range of methodological factors.

The most consistent univariate predictors were baseline alcohol consumption, dependence severity, employment, gender, psychopathology rating, treatment history, neuropsychological functioning, alcohol-related self-efficacy, motivation, socioeconomic status/income, treatment goal, and religion.

When these key predictors were combined into multivariate analyses, baseline alcohol consumption and gender showed substantial reductions in predictive consistency whereas the remaining variables were not greatly affected.

The most consistent predictors overall were dependence severity, psychopathology ratings, alcohol-related self-efficacy, motivation, and treatment goal. The two predictor variables most associated with greater variance accounted for in predictive models, when controlling for broader methodological variables, were baseline alcohol consumption and dependence severity.

Few predictor variables were examined in more than a third of studies reviewed, and few variables were found to be significant predictors in a clear majority of studies. However, a subset of variables was identified, which collectively could be considered to represent a consistent set of predictors.

Too few studies controlled for other important predictor variables. Attempts to synthesize findings were often hampered by lack of agreement of the best measure for predictor variables.

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