DSM-5 criterion-a-based trauma types in service members and veterans seeking treatment for posttraumatic stress disorder
Abstract: In posttraumatic stress disorder (PTSD), the assumption of the equipotentiality of traumas ignores potentially unique contexts and consequences of different traumas. Accordingly, Stein et al. (2012) developed a reliable typing scheme in which assessors categorized descriptions of traumatic events into six "types": life threat to self (LTS), life threat to other, aftermath of violence (AV), traumatic loss, moral injury by self (MIS), and moral injury by other (MIO). We extended this research by validating the typing scheme using participant endorsements of type, rather than assesor-based types. We examined the concordance of participant and assesor types, frequency, and validity of participant-based trauma types by examining associations with baseline mental and behavioral health problems. Interviewers enrolled military personnel and veterans (N = 1,443) in clinical trials of PTSD and helped them select the most currently distressing Criterion-A trauma. Participants and, archivally, assessors typed the distressing aspect(s) of this experience. AV was the most frequently participant-endorsed type, but LTS was the most frequently rated worst part of an event. Although participants endorsed MIS and MIO the least frequently, these were associated with worse mental and behavioral health problems. The agreement between participants and assessors regarding the worst part of the event was poor. Because of discrepancies between participant and assessor typologies, clinical researchers should use participants' ratings, and these should trump assessor judgment. Differences in pretreatment behavioral and mental health problems across some participant-endorsed trauma types partially support the validity of the participant ratings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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.