The relationship between combat-related traumatic amputation and subclinical cardiovascular risk
Abstract: Background: The relationship between acute combat-related traumatic injury (CRTI) to coronary flow reserve (CFR) and subclinical cardiovascular risk have not been examined and was the primary aim of this study. Methods and results: UK combat veterans from the ADVANCE cohort study (UK-Afghanistan War 2003–14) with traumatic limb amputations were compared to injured non-amputees and to a group of uninjured veterans from the same conflict. Subclinical cardiovascular risk measures included fasted blood atherogenic index of plasma (AIP), triglyceride-glucose index (TyG; insulin resistance), the neutrophil-lymphocyte ratio (NLR) and high-sensitivity C-reactive protein (hs-CRP; vascular inflammation), body mass index (BMI) and visceral fat volume (dual-energy X-ray absorptiometry) and 6-min walk distance (6MWD; physical performance). The subendocardial viability ratio (SEVR), to estimate CFR, was calculated using arterial pulse waveform analysis (Vicorder device). In total 1144 adult male combat veterans were investigated, comprising 579 injured (161 amputees, 418 non-amputees) and 565 uninjured men. AIP, TyG, NLR, hs-CRP, BMI, total body fat and visceral fat volume were significantly higher and the SEVR and 6MWD significantly lower in the amputees versus the injured-non-amputees and uninjured groups. The SEVR was lowest in those with above knee and multiple limb amputations. CRTI (ExpB 0.96; 95% CI 0.94–0.98: p < 0.0001) and amputation (ExpB 0.94: 95% CI 0.91–0.97: p < 0.0001) were independently associated with lower SEVR after adjusting for age, rank, ethnicity and time from injury. Conclusion: CRTI, traumatic amputation and its worsening physical deficit are associated with lower coronary flow reserve and heightened subclinical cardiovascular risk.
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.