Veterans with PTSD and Comorbid Substance use Disorders: Does Single versus Poly-substance use Disorder Affect Treatment Outcomes?
Abstract: Substance use disorders (SUD) frequently co-occur with posttraumatic stress disorder (PTSD). Little is known, however, about how individuals with a single SUD diagnosis (relating to only one substance) compare to individuals with poly-SUD diagnoses (relating to more than one substance) on substance use and PTSD treatment outcomes. To address this gap in the literature, we utilized data from a larger study investigating a 12-week integrated, exposure-based treatment (i.e., Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure, or COPE) to examine treatment outcomes by single vs. poly-SUD status. Participants were 54 Veterans (92.6% male, average age = 39.72) categorized as having single SUD (n = 39) or poly-SUD (n = 15). T-tests characterized group differences in baseline demographics and presenting symptomatology. Multilevel models examined differences in treatment trajectories between participants with single vs. poly-SUD. Groups did not differ on baseline frequency of substance use, PTSD symptoms, or treatment retention; however, individuals with poly-SUD evidenced greater reductions in percent days using substances than individuals with a single SUD, and individuals with a single SUD had greater reductions in PTSD symptoms than individuals with poly-SUD over the course of treatment. The findings from this exploratory study suggest that Veterans with PTSD and co-occurring poly-SUD, as compared to a single-SUD, may experience greater improvement in substance use but less improvement in PTSD symptoms during integrated treatment. Future research should identify ways to enhance treatment outcomes for individuals with poly-SUD, and to better understand mechanisms of change for this population.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.