Transaortic gunshot wound through perivisceral segment successfully managed by placement of thoracic stent graft

We describe a 36-year-old woman who presented to our facility after sustaining a gunshot wound to the epigastric region. The gunshot resulted in injury to the left lobe of the liver and the twelfth thoracic vertebral body as well as in a through- and-through injury to the abdominal aorta at the level of the celiac axis. The vascular injury was managed successfully by placement of a thoracic stent graft with coverage of the celiac axis. This case demonstrates the feasibility of managing this uncommon injury with endovascular techniques. (J Vasc Surg Cases and Innovative Techniques 2018;4:24-6.

Impact of rural versus urban geographic location on length of stay after carotid endarterectomy

Abstract Background: Reducing the incidence of extended length of stay (ELOS) after carotid endarterectomy (CEA), defined as LOS \u3e I day, is an important quality improvement focus of the Vascular Quality Initiative (VQI). Rural patients with geographic barriers pose a particular challenge for discharge and may have higher incidences of ELOS as a result. The purpose of this study was to examine the impact of patients’ home geographic location on ELOS after CEA. Methods: The VQI national database for CEA comprised the sample for analyses (N = 66,900). Rural-Urban Commuting Area (RUCA) codes, a validated system used to classify the nation’s census tracts according to rural and urban status, was applied to the VQI database and used to indicate patients’ home geographic location. LOS was categorized into two groups: LOS ≤ 1 day (66%) and LOS \u3e 1 day (ELOS) (34%). Multivariable logistic regression was conducted to examine the effect of geographic location on ELOS after adjustment for age, gender, race, and comorbid conditions. Results: A total of 66,900 patients were analyzed and the mean age of the sample was 70.5 ± 9.3 years (40% female). After adjustment for covariates, the urban group had increased risk for ELOS (OR = 1.20, p \u3c 0.001). Other factors that significantly increased risk for ELOS were non-White race/Latinx/Hispanic ethnicity (OR = 1.44, p \u3c 0.001) and nonelective status (OR =3.31, p \u3c 0.001). In addition, patients treated at centers with a greater percentage of urban patients had greater risk for ELOS (OR = 1.008, p \u3c 0.001). Conclusions: These analyses found that geographic location did impact LOS, but not in the hypothesized direction. Even with adjustment for comorbidities and other factors, patients from urban areas and centers with more urban patients were more likely to have ELOS after CEA. These findings suggest that other mechanisms, such as racial disparities, barriers in access to care, and disparities in support after discharge for urban patients may have a significant impact on LOS

Machine Learning Confirms Nonlinear Relationship between Severity of Peripheral Arterial Disease, Functional Limitation and Symptom Severity

Background: Peripheral arterial disease (PAD) involves arterial blockages in the body, except those serving the heart and brain. We explore the relationship of functional limitation and PAD symptoms obtained from a quality-of-life questionnaire about the severity of the disease. We used a supervised artificial intelligence-based method of data analyses known as machine learning (ML) to demonstrate a nonlinear relationship between symptoms and functional limitation amongst patients with and without PAD. Objectives: This paper will demonstrate the use of machine learning to explore the relationship between functional limitation and symptom severity to PAD severity. Methods: We performed supervised machine learning and graphical analysis, analyzing 703 patients from an administrative database with data comprising the toe–brachial index (TBI), baseline demographics and symptom score(s) derived from a modified vascular quality-of-life questionnaire, calf circumference in centimeters and a six-minute walk (distance in meters). Results: Graphical analysis upon categorizing patients into critical limb ischemia (CLI), severe PAD, moderate PAD and no PAD demonstrated a decrease in walking distance as symptoms worsened and the relationship appeared nonlinear. A supervised ML ensemble (random forest, neural network, generalized linear model) found symptom score, calf circumference (cm), age in years, and six-minute walk (distance in meters) to be important variables to predict PAD. Graphical analysis of a six-minute walk distance against each of the other variables categorized by PAD status showed nonlinear relationships. For low symptom scores, a six-minute walk test (6MWT) demonstrated high specificity for PAD. Conclusions: PAD patients with the greatest functional limitation may sometimes be asymptomatic. Patients without PAD show no relationship between functional limitation and symptoms. Machine learning allows exploration of nonlinear relationships. A simple linear model alone would have overlooked or considered such a nonlinear relationship unimportant