Adult self-reported childhood maltreatment types are associated with treatment satisfaction and alcohol relapse in patients with comorbid substance use and mental health disorders.

BACKGROUND AND OBJECTIVES: Individuals with comorbid substance use and mental health disorders (concurrent disorders; CD) report poor treatment outcomes, high prevalence of childhood maltreatment, and mostly negative experiences with treatment. No studies to date have examined childhood maltreatment and treatment outcomes in CD. This study investigated self-reported childhood maltreatment as it relates to treatment satisfaction and substance use relapse among CD patients. METHODS: The 258 CD inpatients completed a self-report questionnaire package, comprising the Childhood Trauma Questionnaire and the Inpatient Consumer Survey (ICS). Childhood maltreatment was assessed according to five subtypes and self-perceived treatment satisfaction was rated across six ICS domains. Psychiatric diagnoses, substance use status and relapse data were retrieved via patient medical charts. RESULTS: Emotional neglect was associated with lower ratings across all ICS domains and physical neglect was associated with a lower rating for 'outcome of care'. Childhood sexual abuse was associated with a greater likelihood of alcohol relapse. No other relationships were statistically significant. DISCUSSION AND CONCLUSIONS: The presence of childhood neglect (but not abuse) was more associated with overall treatment dissatisfaction, and sexual abuse alone increased the likelihood of alcohol relapse. These findings suggest some early adverse experiences in CD patients may increase negative experiences in treatment while others contribute to the risk of substance use. Broader longitudinal research is needed to examine the trajectory leading to negative outcomes. SCIENTIFIC SIGNIFICANCE: This is the first study to report differential patterns of association by type of childhood maltreatment on negative outcomes in treatment among CD patients.

Blood Oxygenation and Heart Rate Changes After Diamorphine Intravenous Injection During Opioid Agonist Treatment for Outpatients With Heroin Dependence.

OBJECTIVES: More than 60 million people use opioids each year, and many countries have declared an opioid overdose crisis. Heroin, one of the most commonly used opioids, has depressant effects on autonomic functioning; however, few studies have been able to examine the effects of heroin or its pharmaceutically prepared equivalent, diamorphine, in human clinical populations. The present study examined heart rate and oxygen saturation in the minutes immediately after acute diamorphine administration in outpatients with heroin dependence. METHODS: The sample was a subset of participants (N = 36) in the German Project of Heroin Assisted Treatment of Opiate Dependent Patients Trial in Bonn, Germany. Patients were given 3 daily doses of intravenous diamorphine. Doses were determined on an individual basis by study physicians. Pulse oximetry was recorded at baseline and at 30-second intervals from 0 to 450 seconds after diamorphine administration. RESULTS: Heart rate was significantly higher than baseline at 30 seconds after diamorphine administration and significantly lower than baseline at 270 seconds onward. Oxygen saturation was significantly lower than baseline at 60 seconds onward. CONCLUSIONS: Results are consistent with other studies in which depressant effects of opioids were observed. Our findings suggest that even therapeutic doses of diamorphine may have rapid and significant-predominantly depressant-effects on oxygenation and heart rate in populations that frequently use opioids. Monitoring of potential adverse opioid effects would be beneficial even in populations presumed to have developed physiological tolerance.

Changes in Loss Sensitivity During Treatment in Concurrent Disorders Inpatients: A Computational Model Approach to Assessing Risky Decision-Making.

BACKGROUND: Recent 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. METHODS: 50 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. RESULTS: Groups 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. CONCLUSION: This 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.