Interaction Between Psychiatric Symptoms and History of Mild TBI When Evaluating Postconcussion Syndrome in Veterans
Abstract: Introduction: Symptoms of postconcussive syndrome (PCS) after mild TBI (mTBI) have been shown to resolve quickly, yet new research raises questions about possible long-term effects of this condition. It is not clear how best to address assessment and treatment when someone reports lingering symptoms of PCS. One self-report measure used by the VA and the DoD is the Neurobehavioral Symptom Inventory (NSI), but this measure may be affected by underlying psychiatric symptoms. We investigated whether the NSI is sensitive to mTBI after considering a number of psychiatric and demographic factors. Methods: This study examined which factors are associated with NSI scores in a Veteran sample (n = 741) that had recently returned from deployment. Results: Post-traumatic stress disorder (PTSD) and depression accounted for most of the variance on the NSI. Although history of mTBI was initially related to NSI, this association was no longer significant after other covariates were considered. Conclusions: The NSI score was primarily explained by symptoms of PTSD and depression, suggesting that the NSI is not specific to the experience of a brain injury. We recommend cautious interpretation when this measure is used in the chronic phase after mTBI, especially among patients with comorbid depression or PTSD.
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.