Comorbid chronic pain and post-traumatic stress disorder in UK veterans: a lot of theory but not enough evidence
Abstract: Introduction: Chronic pain and post-traumatic stress disorder (PTSD) are strongly correlated in military veteran populations. The aim of this article is to review what is known about the comorbidity of the two conditions. Methods: A literature search was carried out to establish evidence for current explanatory models of why the two conditions frequently co-occur, the most appropriate treatments and current UK service provision for veterans and to identify gaps in research. Results: Chronic pain and PTSD share a number of features, yet the mechanisms behind their comorbidity are not well understood, and while each condition alone has extensive literature, there is limited evidence to support specific care and treatment for the two conditions simultaneously. In addition, there is currently no UK data for veterans with comorbid chronic pain and PTSD so it is not possible to gauge the numbers affected or to predict the numbers who will be affected in the future, and there appear to be no co-located services within the United Kingdom for the management of the two conditions simultaneously in this population. Conclusion:This review highlights a paucity of evidence in all areas of comorbid chronic pain and PTSD. Further work needs to consider fully the nature of the event that led to the development of the two conditions and examine further the possible mechanisms involved, and clinics need to establish routine and systematic evaluations of how any interventions work in practice.
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.