Alliance Across Group Treatment for Veterans With Posttraumatic Stress Disorder : The Role of Interpersonal Trauma and Treatment Type
Abstract: The authors examined initial levels and pattern of change of alliance in group treatment for posttraumatic stress disorder (PTSD) for veterans. One hundred and 78 male veterans with PTSD were recruited for this study. Participants were randomly assigned to either group cognitive–behavioral therapy (GCBT) or to group present-centered therapy (GPCT). Alliance with fellow group members was assessed every other session throughout the group (total of seven assessments). Hierarchical linear modeling was used to determine whether treatment condition or index trauma type (interpersonal or noninterpersonal) impacted initial levels of alliance or change in alliance over time. Alliance increased significantly throughout treatment in both conditions. The presence of an interpersonal index event, compared to a noninterpersonal index event, did not significantly impact either initial levels of alliance or change in alliance over time. Participants in the GCBT condition experienced significantly greater growth in alliance over time compared to those in the GPCT condition (p > .05) but did not have significantly different initial alliance ratings. The components and focus of the GCBT treatment may have facilitated more rapid bonding among members. Interpersonal traumatic experience did not negatively impact group alliance.Â
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.