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Background The implementation of safety policies in farming-related injuries in West Virginia has been lacking. Farming-related injuries have resulted in massive injuries that have resulted in life long injuries and death. Therefore, this study aims to review 12 years of our level 1 trauma data and describe the incidence rate and patterns of priority-related farming injuries in West Virginia, as well as the specific co-morbidities and related injuries that might be more susceptible to damage. Methods We examined 82 cases of farm-related injuries that required trauma-priority related intervention from 2005 -2016. We harvested data from the Charleston Area Medical Center Trauma registry to investigate associated injuries. We defined farm equipment as any mechanical or automated tool used on a farm for related farm upkeep or farm-related activity. Multinomial logistic regression was used to understand the overall impact on the differing effects of years of injuries. Results The total number of farming-related injury cases was 82. The most statistically suggestive finding was those that had a positive narcotics urine test at (p= 0.062) (-.3230-12.82). Those with a history of CHF (congestive heart failure) also had a significant statistical relationship at (p=0.001) (-5.477-1.394). Alcohol use disorder was also a significant statistical relationship (p=0.012) (-5.127--.6728). The most common injuries were concussions at 18 % ( 15/82) followed by rib fractures at 17 % ( 14/82). Conclusion Farming-related injuries appear to have increased risks on specific body and organ systems, as described in our initial data analysis. Specific co-morbidities also have been documented to show a higher risk of injury and would need further investigation. Specific years show a higher prevalence of farming injuries compared to other years. Further research is needed to explore these underlying findings.

Imaging surveillance after endovascular aortic aneurysm repair (EVAR) is critical. In this study we analyzed compliance with imaging surveillance after EVAR and its effect on clinical outcomes.Retrospective analysis of prospectively collected data of 565 EVAR patients (August 2001-November 2013), who were followed using duplex ultrasound and/or computed tomography angiography. Patients were considered noncompliant (NC) if they did not have any follow-up imaging for 2 years and/or missed their first post-EVAR imaging over 6 months. A Kaplan-Meier analysis was used to compare compliance rates in EVAR patients with hostile neck (HN) vs favorable neck (FN) anatomy (according to instructions for use). A multivariate analysis was also done to correlate compliance and comorbidities.Forty-three percent were compliant (7% had no follow-up imaging) and 57% were NC. The mean follow-up for compliant patients was 25.4 months (0-119 months) vs 31.4 months for NC (0-140 months). The mean number of imaging was 3.5 for compliant vs 2.6 for NC (P.0001). Sixty-four percent were NC for HN patients vs 50% for FN patients (P = .0007). The rates of compliance at 1, 2, 3, 4, and 5 years for all patients were 78%, 63%, 55%, 45%, and 32%; and 84%, 68%, 61%, 54%, and 40% for FN patients; and 73%, 57%, 48%, 37%, and 25% for HN patients (P = .009). The NC rate for patients with late endoleak and/or sac expansion was 58% vs 54% for patients with no endoleak (P = .51). The NC rate for patients with late reintervention was 70% vs 53% for patients with no reintervention (P = .1254). Univariate and multivariate analyses showed that patients with peripheral arterial disease had an odds ratio of 1.9 (P = .0331), patients with carotid disease had an odds ratio of 2 (P = .0305), and HN patients had an odds ratio of 1.8 (P = .0007) for NC. Age and residential locations were not factors in compliance.Overall, compliance of imaging surveillance after EVAR was low, particularly in HN EVAR patients, and additional studies are needed to determine if strict post-EVAR surveillance is necessary, and its effect on long-term clinical outcome.

Introduction: Distance and other factors may play a significant role in both the implementation of care and the number of secondary health outcomes. The distance from an injury site can play a substantial impact on the mortality of different injuries and access to health-related services. Within this study, we sought to understand the effect of the site to treating center distance and access to relevant health-care services on effect and secondary injuries of coal mining-related injuries. Methods: We evaluated patient demographics, coal mining locations, and patient outcomes. This study is a retrospective review of patients with coal mining hand injuries included in our Level 1 trauma database as well as the amount of health-related resources defined by WV GIS Health Care (Hospitals). The sample consists of coal mine workers. All patients had to meet the study criteria and were admitted during the study period of January 1, 2005, through March 31, 2015. We then were able to locate the coal mines where these coal miners were injured based on information from their medical records and the availability of health-care resources (hospitals) around the zones of injury, as well as health-related resources from the WV GIS database. The sample size was n = 104. Data were collected in an Excel spreadsheet. Stata 11.2 was used to conduct a multinomial logistic regression. A hub analysis was performed to understand the overall distance associated with the injury site and care using Arch GIS 10.6. A hotspot analysis was also performed in order to understand the differences of different zones of concern of injury sites in Southern West Virginia, from January 1, 2005, to March 31, 2015. Results: Variables collected are as follows: a total number of coal miners were 104, with an average age of miners being similar in age demographics at 19–62. Coal mines are registered on the map with relevance to injury based on location; severity scale is presented. The most common injuries were fractures at 42.3% (47/104) followed by amputations at 26.9% (28/104). The index finger had an overall higher rate of injury at 28.8% (30/104). To understand the overall impact of specific injuries of coal miners based on the location, we peered into the number of injuries sustained at particular sites over the 10 years. Within this, the attached group, the average distance per injury to a medical center that was able to assist with hand injuries, was 46.1 miles. The shortest distance was within one mile of the center, and the most prolonged distance was 83.3 miles. The highest concentration of coal mining injuries were over 5 miles away from any health care organizations. The at-risk area of coal mining injuries was identified at (P = 0.001) and outside of health-care resources that can be seen within the hotspot analysis tested within this analysis. Conclusion: Specific injuries appear to be more at risk on different portions of the hand, as described in our initial data analysis. We also found that particular coal mines seem to harbor more coal mining hand injuries. Rural health care has the challenge of injuries occurring at extended distances and requires that treatment becomes as efficient as possible to maximize the chances of a full recovery. Aim and Objectives: The objective of this study is to understand if the distance between the location of injury and trauma center plays a role in the outcome of coal mines related to hand injuries treated at Charleston Area Medical Center.

Background Validation of subclavian duplex ultrasound velocity criteria (SDUS VC) to grade the severity of subclavian artery stenosis has not been established or systematically studied. Currently, there is a paucity of published literature and lack of practitioner consensus for how subclavian duplex velocity findings should be interpreted in patients with subclavian artery stenosis. Objective The objective of the present study was to validate SDUS measurements using subclavian conventional or computed tomography angiogram (subclavian angiogram [SA])-derived measurements. Secondary objectives included measuring the correlation between SDUS peak systolic velocities and SA measurements, and to determine the optimal cutoff value for predicting significant stenosis (>70%). Methods This is a retrospective review of all patients with suspected subclavian artery stenosis and a convenience sample of carotid artery patients who underwent SDUS and SA from May 1999 to July 2013. SA reference vessel and intralesion minimal lumen diameters were measured and compared with SDUS velocities obtained within 3 months of the imaging study. Percent stenosis was calculated using the North American Symptomatic Carotid Endarterectomy Trial method for detecting stenosis in a sufficiently large cohort. Receiver operating characteristic curves was generated for SDUS VC to predict >70% stenosis. Velocity cutoff points were determined with equal weighting of sensitivity and specificity. Results We examined 268 arteries for 177 patients. The majority of the arteries were for female patients (52.5%) with a mean age of 66.7 ± 11.1 years. Twenty-three arteries had retrograde vertebral artery flow and excluded from further analysis. For the remaining 245 arteries, the average peak systolic velocity was 212.6 ± 110.7 cm/s, with a range of 45-626 cm/s. Average stenosis was 25.8% ± 28.2%, with a range of 0% to 100%. Following receiver operating characteristic analysis, we found a cutoff value of >240 cm/s to be most predictive of >70%. Area under the curve was 0.94 with 95% confidence intervals of 0.91 to 0.97. The sensitivity and specificity for predicting >70% stenosis was 90.9 and 82.5%, respectively. Conclusions In patients with known or suspected disease involving the great vessels, a subclavian artery flow velocity exceeding 240 cm/s seems to be predictive of significant subclavian stenosis. Thus, we propose new SDUS VC, for predicting subclavian artery stenosis. However, because of the use of a convenience sample, it is possible that the current proposed cutoff point might need to be adjusted for other populations.