Biobehavioural Predictors of Treatment Outcome in Addiction

Abstract

Substance and alcohol use disorders are highly prevalent and confer a considerable burden of morbidity and mortality including medical, psychosocial, and economic consequences. Although evidence-based treatments to treat substance and alcohol use disorders are present, treatment retention and efficacy within these treatment modalities remains low. Further, individuals with substance and alcohol use disorders are clinically complex and often present with concurrent psychiatric disorders, which makes treatment delivery and response more challenging. Developing novel treatments and improving clinical outcomes in patients with substance and alcohol use disorders is predicated upon basic scientific advances in understanding the biological and behavioural determinants of treatment response. The purpose of this dissertation is to examine the biobehavioural predictors of treatment outcome in substance and alcohol use disorders through three distinct studies that each used pre-treatment variables to predict addictions treatment response. The first study used latent profile analysis to elucidate clinical profiles and independent variables associated with premature treatment termination from clinically complex sample of individuals with concurrent disorders at a large residential addiction treatment centre. The second study systematically synthesized and critically appraised empirical findings of delayed reward discounting, a transdiagnostic behavioural economic indicator of impulsivity, as a predictor of smoking cessation treatment outcome. Finally, the third study used baseline resting state functional connectivity (rsFC) to predict response to a brief intervention to reduce alcohol consumption at three-month follow-up in individuals with alcohol use disorder. The results of this dissertation extend the current literature and highlight the utility of pre-treatment clinical, behavioural, and neuroimaging data in predicting treatment response and elucidating pre-treatment patterns of rsFC that are associated with poor prognosis. The pre-treatment variables identified in this dissertation can be used to identify high-risk patient populations that may benefit from additional care pathways, adjunctive treatment, or further resources to improve patient outcomes and prognosis.