Patient‐ and therapist‐rated alliance predict improvements in posttraumatic stress disorder symptoms and substance use in integrated treatment
Abstract: Concurrent Treatment of Posttraumatic Stress Disorder (PTSD) and Substance Use Disorders Using Prolonged Exposure (i.e., COPE) is an efficacious, integrated, psychotherapy that attends to PTSD and substance use disorders simultaneously. No study has examined how therapeutic alliance functions during the provision of COPE and how this compares to non‐integrated treatments, such as relapse prevention (RP) for substance use disorders. Understanding the role of alliance in COPE versus RP could inform treatment refinement and ways to enhance treatment outcomes. Participants (N = 55 veterans) were randomized to 12, individual, weekly sessions of COPE or RP in a randomized clinical trial. Piecewise linear mixed effect models examined how mid‐treatment (1) patient‐rated alliance, (2) therapist‐rated alliance, and (3) the convergence between patient‐ and therapist‐rated alliance as measured by a difference score predicted reductions in PTSD symptoms and substance use across treatment and follow‐up periods. Both patient‐ and therapist‐rated alliance predicted reductions in PTSD symptoms in COPE. Higher patient‐rated alliance predicted lower percent days using substances in RP. Difference score models showed higher patient‐rated alliance relative to therapist‐rated alliance scores predicted symptom reductions in COPE whereas higher therapist‐rated alliance scores relative to patient‐rated alliance scores predicted symptom reductions in RP. Preliminary findings show a unique relationship between the rater of the alliance and treatment modalities. Patient‐rated alliance may be important in trauma‐focused, integrated treatments whereas therapist‐rated alliance may be more important in skills‐focused, substance use interventions.
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.