A systematic review of posttraumatic stress and resilience trajectories: Identifying predictors for future treatment of veterans and service members
Abstract: Posttraumatic stress disorder (PTSD) often presents with comorbidities and can result in functional impairment. Veterans and service members report PTSD at higher rates than civilians, which represents a public health concern among those who have served or are serving in the military. Prior reviews of evidence-based treatments for PTSD demonstrate smaller effect sizes for veterans and service members than for civilians. One line of investigation that may contribute to our understanding in this area is developmental trajectory research. Understanding predictors of different symptomatic trajectories compared to resilient trajectories and vice versa may help clinicians better tailor evidence-based conceptualizations, treatments, and change agents to the individual, facilitate prevention efforts, and embark on a process-based, flexible, cognitive-behavioral approach that is patient-centered. The current systematic review examined predictors of both resilient (i.e., compared to heterogeneous symptomatic trajectories) and variable symptomatic trajectories (i.e., compared to resilient and/or other symptomatic trajectories) in veterans and service members. Twenty-seven studies met inclusion criteria. Across all included studies reporting percentages of resilience trajectories (i.e., including some studies that used the same data sets and/or samples), 73.4% reported a resilience trajectory, while the remaining 26.6% encompassed heterogeneous symptomatic trajectories on average. Predictors are presented and discussed, in addition to implications for research and treatment of veterans and service members.
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.