Neuropsychiatric Outcomes in UK Military Veterans With Mild Traumatic Brain Injury and Vestibular Dysfunction
Abstract: The objective of the study is to estimate the frequency of vestibular dysfunction following blunt, blast, and combined blunt and blast mild traumatic brain injury (mTBI) and thereon assess the long-term impact of vestibular dysfunction on neurobehavioral function and disability independently of comorbid psychiatric symptoms. The study setting is Combat Stress residential and Veterans' Outreach drop-in centers for psychological support. Participants: One hundred sixty-two help-seeking UK military veterans. Main Measures: Self-reported frequency and severity of mTBI (using the Ohio State TBI Identification Method), Vertigo Symptom Scale, PTSD Checklist for DSM-5, Kessler Psychological Distress Scale (K10), Neurobehavioral Symptom Inventory, Headache Impact Test (HIT6), Memory Complaints Inventory, World Health Organization Disability Assessment Schedule II short version (WHODAS 2.0). Seventy-two percent of the sample reported 1 or more mTBIs over their lifetime. Chi-square analyses indicated that vestibular disturbance, which affected 69% of participants, was equally prevalent following blunt (59%) or blast (47%) injury and most prevalent following blunt and blast combined (83%). Mediation analysis indicated that when posttraumatic stress disorder, depression, and anxiety were taken into account, vestibular dysfunction in participants with mTBI was directly and independently associated with increased postconcussive symptoms and functional disability. Vestibular dysfunction is common after combined blunt and blast mTBI and singularly predictive of poor long-term mental health. From a treatment perspective, vestibular rehabilitation may provide relief from postconcussive symptoms other than dizziness and imbalance.
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.