Abstract
BackgroundRecent studies have employed computational modeling to characterize deficits in aspects of decision-making not otherwise detected using traditional behavioral task outcomes. While prospect utility-based modeling has shown to differentiate decision-making patterns between users of different drugs, its relevance in the context of treatment has yet to be examined. This study investigated model-based decision-making as it relates to treatment outcome in inpatients with co-occurring mental health and substance use disorders.Methods50 patients (Mage = 38.5, SD = 11.4; 16F) completed the Cambridge Gambling Task (CGT) within 2 weeks of admission (baseline) and 6 months into treatment (follow-up), and 50 controls (Mage = 31.9, SD = 10.0; 25F) completed CGT under a single outpatient session. We evaluated 4 traditional CGT outputs and 5 decisional processes derived from the Cumulative Model. Psychiatric diagnoses and discharge data were retrieved from patient health records.ResultsGroups were similar in age, sex, and premorbid IQ. Differences in years of education were included as covariates across all group comparisons. All patients had ≥1 mental health diagnosis, with 80% having >1 substance use disorder. On the CGT, patients showed greater Deliberation Time and Delay Aversion than controls. Estimated model parameters revealed higher Delayed Reward Discounting, and lower Probability Distortion and Loss Sensitivity in patients relative to controls. From baseline to follow-up, patients (n = 24) showed a decrease in model-derived Loss Sensitivity and Color Choice Bias. Lastly, poorer Quality of Decision-Making and Choice Consistency, and greater Color Choice Bias independently predicted higher likelihood of treatment dropout, while none were significant in relation to treatment length of stay.ConclusionThis is the first study to assess a computational model of decision-making in the context of treatment for concurrent disorders. Patients were more impulsive and slower to deliberate choice than controls. While both traditional and computational outcomes predicted treatment adherence in patients, findings suggest computational methods are able to capture treatment-sensitive aspects of decision-making not accessible via traditional methods. Further research is needed to confirm findings as well as investigate the relationship between model-based decision-making and post-treatment outcomes.
Highlights
Psychiatric comorbidities are prevalent among substance users [1, 2], and their co-occurrence contribute substantially to the global disease burden [3]
This study investigated risky decision-making in patients with concurrent disorders and assessed the utility of decision-making outcomes derived from computational modeling in predicting treatment outcomes
As required for treatment admission, all patients had to have co-occurring mental health and substance use disorders confirmed at intake by a licensed medical or mental health professional
Summary
Psychiatric comorbidities are prevalent among substance users [1, 2], and their co-occurrence (or concurrent disorders) contribute substantially to the global disease burden [3]. Treatment services are often ill-equipped to effectively manage the issues of mental health and substance use concurrently [1, 5, 6], and this could in part be attributed to the relatively few data representative of concurrent disorders patients as a coherent group in treatment [5]. Research representative of concurrent disorders patients, collectively as a single clinical group, are needed to better inform the development of interventions for broader spectrum problems and risks underlying poor treatment outcomes. This study investigated model-based decisionmaking as it relates to treatment outcome in inpatients with co-occurring mental health and substance use disorders
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