Factor structure of posttraumatic stress disorder (PTSD) in Australian Vietnam Veterans: Confirmatory factor analysis of the Clinician-Administered PTSD Scale for DSM–5
Abstract: Introduction: The Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5) brought a change to the symptom clusters of posttraumatic stress disorder (PTSD). In line with the DSM–5 changes, an updated version of the Clinician-Administered PTSD Scale (CAPS–5) was released. The CAPS–5 is considered to be the gold-standard measure of PTSD; however, examinations of the psychometric properties and optimal factor structure of this scale are underrepresented in PTSD studies. Methods: This study used confirmatory factor analysis (CFA) to assess the factor structure of the CAPS–5 using a sample of 267 male Australian Vietnam Veterans. Models drawn from the PTSD CFA literature were used to test the underlying dimensions of PTSD: the four-factor DSM–5 model, six-factor externalizing behaviour and anhedonia models, and seven-factor hybrid model. Results: The results found that the DSM–5 model showed slightly less than adequate fit (comparative fit index [CFI] = 0.90, Tucker–Lewis index [TLI] = 0.88, root mean square error of approximation [RMSEA] = 0.064), however, other models showed acceptable fit. The anhedonia model provided a significantly better fit than the other models (CFI = 0.92, TLI = 0.90, RMSEA = 0.059). Discussion: Overall, the results supported the anhedonia model. This result may indicate that the underlying dimensions of PTSD in Australian Vietnam Veterans may best be represented by six distinct factors.
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.