A comparison of the CAPS-5 and PCL-5 to assess PTSD in military and veteran treatment-seeking samples
Abstract: Background: This study was an examination of the puzzling finding that people assessed for symptoms of posttraumatic stress disorder (PTSD) consistently score higher on the self-report PTSD Checklist for DSM-5 (PCL-5) than the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Both scales purportedly assess PTSD severity with the same number of items, scaling, and scoring range, but differences in scores between measures make outcomes difficult to decipher. Objective: The purpose of this study was to examine several possible psychometric reasons for the discrepancy in scores between interview and self-report. Method: Data were combined from four clinical trials to examine the baseline and posttreatment assessments of treatment-seeking active duty military personnel and veterans. Results: As in previous studies, total scores were higher on the PCL-5 compared to the CAPS-5 at baseline and posttreatment. At baseline, PCL-5 scores were higher on all 20 items, with small to large differences in effect size. At posttreatment, only three items were not significantly different. Distributions of item responses and wording of scale anchors and items were examined as possible explanations of the difference between measures. Participants were more likely to use the full range of responses on the PCL-5 compared to interviewers. Conclusions: Suggestions for improving the congruence between these two scales are discussed. Administration of interviews by trained assessors can be resource intensive, so it is important that those assessing PTSD severity are afforded confidence in the equivalence of their assessment of PTSD regardless of the assessment method used.
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.