The underlying mechanisms by which PTSD symptoms are associated with cardiovascular health in male UK military personnel: The ADVANCE cohort study
Abstract: Post-Traumatic Stress Disorder (PTSD) has been identified as an independent risk factor for cardiovascular disease, but the mechanisms of this relationship are not well understood. This study investigates the associations between PTSD symptom clusters (hyperarousal, intrusive thoughts, avoidance behaviours and emotional numbing) and mechanisms of cardiovascular disease including cardiometabolic effects, inflammation, and haemodynamic functioning. In the ADVANCE study cohort of UK male military personnel, 1111 participants were assessed for PTSD via questionnaire and cardiovascular risk via venous blood sampling, pulse wave analysis and dual energy x-ray absorptiometry between 2015 and 2020. Variable selection procedures were conducted to assess which of the symptom clusters if any were associated with cardiovascular risk outcomes. Associations were confirmed via robust regression modelling. Avoidance behaviours were associated with greater systolic Blood Pressure (BP) (Adjusted Coefficient (AC) 0.640 (95% Confidence Interval (CI) 0.065, 1.149). Emotional numbing was associated with greater estimated glucose disposal rate (AC -0.021 (95%CI -0.036, -0.005). Hyperarousal was associated with greater levels of (log)triglycerides (exponentiated-AC 1.009 (95%CI 1.002, 1.017). Intrusive thoughts were associated with greater visceral adipose tissue (AC 0.574 (95%CI 0.020, 1.250). Nonlinear relationships were observed between emotional numbing with heart rate and intrusive thoughts with systolic BP. Limited evidence is present for symptom associations with lipoproteins and pulse wave velocity. No associations were observed between PTSD symptom clusters and high sensitivity c-reactive protein, diastolic BP, total cholesterol, or haemoglobin fasting glucose. In conclusion, symptom clusters of PTSD were associated with increased cardiovascular risk via cardiometabolic and haemodynamic functioning mechanisms, but not inflammation.
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.