Abstract: A bomb blast may cause the full severity range of traumatic brain injury (TBI), from mild concussion to severe, penetrating injury. The pathophysiology of blast-related TBI is distinctive, with injury magnitude dependent on several factors, including blast energy and distance from the blast epicentre. The prevalence of blast-related mild TBI in modern war zones has varied widely, but detection is optimised by battlefield assessment of concussion and follow-up screening of all personnel with potential concussive events. There is substantial overlap between post-concussive syndrome and post-traumatic stress disorder, and blast-related mild TBI seems to increase the risk of post-traumatic stress disorder. Post-concussive syndrome, post-traumatic stress disorder, and chronic pain are a clinical triad in this patient group. Persistent impairment after blast-related mild TBI might be largely attributable to psychological factors, although a causative link between repeated mild TBIs caused by blasts and chronic traumatic encephalopathy has not been established. The application of advanced neuroimaging and the identification of specific molecular biomarkers in serum for diagnosis and prognosis are rapidly advancing, and might help to further categorise these injuries.
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.