Comparing the prevalence of probable DSM-IV and DSM-5 posttraumatic stress disorder in a sample of U.S. Military Veterans using the PTSD checklist
Abstract: Posttraumatic stress disorder (PTSD) changed substantially when Diagnostic and Statistical Manual of Mental Disorders transitioned from fourth (DSM-IV) to fifth (DSM-5) edition. Hoge et al. found that although diagnostic prevalence remained consistent across nomenclatures, diagnostic concordance was low (55%). Study goals were to examine both the generalizability of these findings and whether either diagnosis systematically excluded patients. U.S. veterans (N = 1,171) who completed the PTSD Checklist for DSM-IV (PCL-S) and DSM-5 (PCL-5) were classified as: probable PTSD on both measures; probable PTSD on PCL-S only; probable PTSD on PCL-5 only; or no PTSD on either measure. Diagnostic prevalence was equivalent. Unlike Hoge et al.’s findings, diagnostic concordance was high (91.3%). Furthermore, observed demographic and severity differences were driven by disparities between veterans in the no PTSD versus the probable PTSD groups, not diagnostic changes. Findings suggest translatability across measures and that diagnostic changes do not systematically exclude patients.
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.