Methamphetamine Use is Associated with Increased Surgical Site Infections after Trauma Laparotomy.

BACKGROUND: Methamphetamine (METH) use causes significant vasoconstriction, which can be severe enough to cause bowel ischemia. Methamphetamines have also been shown to alter the immune response. These effects could predispose METH users to poor wound healing, increased infections, and other post-operative complications. We hypothesized that METH users would have longer length of stay and higher rates of complications compared to non-METH users. METHODS: The trauma registry for our urban Level 1 trauma center was searched for patients that received an exploratory laparotomy from 2016 to 2019. A total 204 patients met criteria and 52 (25.5%) were METH positive. Length of stay (LOS), ventilator days, abbreviated injury scale (AIS), and wound class were compared using nonparametric statistics. Age and injury severity score (ISS) were compared using a Student's t-test. A Chi Square or Fisher's Exact test was used to compare sex, mechanism of injury, and rates of infectious complications. RESULTS: Methamphetamine-positive patients had a significantly higher rate of surgical site infections (7.4% versus 0%, P = 0.001). Patients that developed surgical site infection had equivalent rates of smoking and diabetes, as well as equivalent abdominal AIS and wound class compared to those who did not develop surgical site infection. Hospital and ICU LOS, ventilator days, ISS, and mortality were equivalent between METH positive and negative patients. Rates of other infectious complications were the same between groups. CONCLUSIONS: Methamphetamine use is associated with an increased rate of surgical site infection after trauma laparotomy. Other serious complications and mortality were not affected by METH use.

Setting the Standard: Using the ABA Burn Registry to Benchmark Risk Adjusted Mortality.

Reports of single center experience and studies of larger databases have identified several predictors of burn center mortality, including age, burn size, and inhalation injury. None of these analyses has been broad enough to allow benchmarking across burn centers. The purpose of this study was to derive a reliable, risk-adjusted, statistical model of mortality based on real-life experience at many burn centers in the U.S. We used the American Burn Association 2020 Full Burn Research Dataset, from the Burn Center Quality Platform (BCQP) to identify 130,729 subjects from July 2015 through June 2020 across 103 unique burn centers. We selected 22 predictor variables, from over 50 recorded in the dataset, based on completeness (at least 75% complete required) and clinical significance. We used gradient-boosted regression, a form of machine learning, to predict mortality and compared this to traditional logistic regression. Model performance was evaluated with AUC and PR curves. The CatBoost model achieved a test AUC of 0.980 with an average precision of 0.800. The logistic regression produced an AUC of 0.951 with an average precision of 0.664. While AUC, the measure most reported in the literature, is high for both models, the CatBoost model is markedly more sensitive, leading to a substantial improvement in precision. Using BCQP data, we can predict burn mortality allowing comparison across burn centers participating in BCQP.

Using a National Burn Registry to Develop a Model for Risk-Adjusted Length of Stay Benchmarking.

Length of stay (LOS) is a frequently reported outcome after a burn injury. LOS benchmarking will benefit individual burn centers as a way to measure their performance and set expectations for patients. We sought to create a nationwide, risk-adjusted model to allow for LOS benchmarking based on the data from a national burn registry. Using data from the American Burn Association's Burn Care Quality Platform, we queried admissions from 7/2015 to 6/2020 and identified 130,729 records reported by 103 centers. Using 22 predictor variables, comparisons of unpenalized linear regression and Gradient boosted (CatBoost) regressor models were performed by measuring the R2 and concordance correlation coefficient on the application of the model to the test dataset. The CatBoost model applied to the bootstrapped versions of the entire dataset was used to calculate O/E ratios for individual burn centers. Analyses were run on 3 cohorts: all patients, 10-20% TBSA, >20% TBSA. The CatBoost model outperformed the linear regression model with a test R2 of 0.67 and CCC of 0.81 compared with the linear model with R2=0.50, CCC=0.68. The CatBoost was also less biased for higher and lower LOS durations. Gradient-boosted regression models provided greater model performance than traditional regression analysis. Using national burn data, we can predict LOS across contributing burn centers while accounting for patient and center characteristics, producing more meaningful O/E ratios. These models provide a risk-adjusted LOS benchmarking using a robust data source, the first of its kind, for burn centers.

Changes in splenic capsule with aging; beliefs and reality.

BACKGROUND: Research describing the splenic capsule and its effect on non-operative management of splenic injuries is limited. The aim of this study is to identify the current beliefs about the splenic capsule thickness and investigate changes in the splenic capsule with age. METHODS: Trauma Medical Directors were surveyed on their beliefs regarding splenic capsule thickness changes with age. Thicknesses of cadaveric splenic capsule samples were measured. RESULTS: The majority of trauma medical directors (59%) believe the capsule thickness decreases with age. There were 94 splenic specimens obtained. The splenic capsules of infants were thin and had a uniform layer of elastin fibers. With aging, the capsule becomes thick and develops a collagen layer. CONCLUSION: Most trauma directors believe the splenic capsule thickness decreases with age. However, our results demonstrate that the splenic capsule thickness increases during childhood but remains constant in adulthood.