Comparison of behavioral activation-enhanced cognitive processing therapy and cognitive processing therapy among U.S. service members: A randomized clinical trial
Abstract: Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) frequently co-occur and can cause significant impairment. Data are lacking as to whether interventions targeting both PTSD and MDD may improve treatment outcomes among individuals with this comorbidity compared with existing evidence-based PTSD treatments alone. This randomized trial compared the effectiveness of cognitive processing therapy (CPT) enhanced with behavioral activation (BA+CPT) versus CPT among 94 service members (52 women and 42 men; age M = 28.5 years) with comorbid PTSD and MDD. The primary outcome was clinician-administered depression symptom severity on the Montgomery-Åsberg Depression Rating Scale (MADRS) from pretreatment through 3-month follow-up. Intent-to-treat analyses using multilevel models showed statistically and clinically significant decreases in MADRS scores for both conditions over time, with no significant differences between BA+CPT and CPT. Secondary depression and PTSD symptom outcomes followed a similar pattern of results. For diagnostic MDD and PTSD outcomes using available data, no statistically significant differences between treatments emerged at posttreatment or 3-month follow-up. Sessions attended, dropout rate, and treatment satisfaction did not significantly differ between treatments. Outcomes were comparable for both treatments, suggesting that BA+CPT and CPT were similarly effective psychotherapy options for comorbid PTSD and MDD.
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.