TXA does not affect levels of TBI-related biomarkers in blunt TBI with ICH: A secondary analysis of the prehospital TXA for TBI trial.

BACKGROUND: Brain specific biomarkers such as glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCH-L1), and microtubule-associated protein-2 (MAP-2) have been identified as tools for diagnosis in traumatic brain injury (TBI). Tranexamic acid (TXA) has been shown to decrease mortality in patients with intracranial hemorrhage (ICH). The effect of TXA on these biomarkers is unknown. We investigated whether TXA affects levels of GFAP, UCH-L1, and MAP-2, and whether biomarker levels are associated with mortality in patients receiving TXA. METHODS: Patients enrolled in the prehospital TXA for TBI trial had GFAP, UCHL-1 and MAP-2 levels drawn at 0 hour and 24 hours postinjury (n = 422). Patients with ICH from blunt trauma with a GCS <13 and SBP >90 were randomized to placebo, 2 g TXA bolus, or 1 g bolus +1 g/8 hours TXA infusion. Associations of TXA and 24-hour biomarker change were assessed with multivariate linear regression. Association of biomarkers with 28-day mortality was assessed with multivariate logistic regression. All models were controlled for age, GCS, ISS, and AIS head. RESULTS: Administration of TXA was not associated with a change in biomarkers over 24 hours postinjury. Changes in biomarker levels were associated with AIS head and age. On admission, higher GFAP (odds ratio [OR], 1.75; confidence interval [CI], 1.31-2.38; p < 0.001) was associated with increased 28-day mortality. At 24 hours postinjury, higher levels of GFAP (OR, 2.09; CI, 1.37-3.30; p < 0.001 and UCHL-1 (OR, 2.98; CI, 1.77-5.25; p < 0.001) were associated with mortality. A change in UCH levels from 0 hour to 24 hours postinjury was also associated with increased mortality (OR, 1.68; CI, 1.15-2.49; p < 0.01). CONCLUSION: Administration of TXA does not impact change in GFAP, UCHL-1, or MAP-2 during the first 24 hours after blunt TBI with ICH. Higher levels of GFAP and UCH early after injury may help identify patients at high risk for 28-day mortality. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

Trauma as a Public Health Moment: Addressing Vaccine Uptake in Trauma Patients.

Objective: Our objective was to identify factors associated with COVID-19 vaccination in trauma patients and to provide an opportunity for patients to engage in conversations about vaccination. Background: The trauma surgery service offers a unique opportunity to promote preventative health interventions in hard-to-reach populations. Methods: Trauma inpatients in Chicago, IL were recruited for this mixed-methods study from February 2022 to April 2022. Participants completed a survey on demographics, COVID-19 vaccination status, and Experiences of Discrimination Scale adapted for medical settings. Differences between vaccinated and unvaccinated patients were analyzed using the Wilcoxon-rank sum test. A semistructured, qualitative interview was completed. Qualitative data was transcribed and analyzed using Grounded Theory Methodology. Results: Fifty-eight trauma patients were surveyed, representing 88% of patients approached. Only 23 (40%) patients reported full vaccination to COVID-19. Previous vaccination (at least 1 dose) was associated with greater concern for COVID-19 (OR 3.47, 95% CI 1.987-6.964, P < 0.001) and higher income (OR 1.21, 95% CI 1.02-1.44, P = 0.03). Higher Experiences of Discrimination Scale scores were associated with decreased likelihood of prior vaccination (OR 0.97, 95% CI 0.95-0.99, P = 0.04). On qualitative analysis, recurrent themes included vaccination motivated by either community-based or personal health-related values, and disinterest in vaccination based on perceived low need or skepticism of experimentation. Fifteen patients (26%) eligible for a vaccine dose consented to onsite vaccination after the survey. Conclusions: Trauma patients who have experienced more discrimination in medical settings have lower rates of COVID-19 vaccination. Vaccination rates in our population were over 2 times lower than citywide rates, but admission to the trauma service can increase comprehensive care.

Social Vulnerability and Firearm Violence: Geospatial Analysis of 5 US Cities.

BACKGROUND: Firearm violence is now endemic to certain US neighborhoods. Understanding factors that impact a neighborhood's susceptibility to firearm violence is crucial for prevention. Using a nationally standardized measure to characterize community-level firearm violence risk has not been broadly studied but could enhance prevention efforts. Thus, we sought to examine the association between firearm violence and the social, structural, and geospatial determinants of health, as defined by the Social Vulnerability Index (SVI). STUDY DESIGN: In this cross-sectional study, we merged 2018 SVI data on census tract with shooting incidents between 2015 and 2021 from Baltimore, Chicago, Los Angeles, New York City, and Philadelphia. We used negative binomial regression to associate the SVI with shooting incidents per 1,000 people in a census tract. Moran's I statistics and spatial lag models were used for geospatial analysis. RESULTS: We evaluated 71,296 shooting incidents across 4,415 census tracts. Fifty-five percent of shootings occurred in 9.4% of census tracts. In all cities combined, a decile rise in SVI resulted in a 37% increase in shooting incidents (p < 0.001). A similar relationship existed in each city: 30% increase in Baltimore (p < 0.001), 50% in Chicago (p < 0.001), 28% in Los Angeles (p < 0.001), 34% in New York City (p < 0.001), and 41% in Philadelphia (p < 0.001). Shootings were highly clustered within the most vulnerable neighborhoods. CONCLUSIONS: In 5 major US cities, firearm violence was concentrated in neighborhoods with high social vulnerability. A tool such as the SVI could be used to inform prevention efforts by directing resources to communities most in need and identifying factors on which to focus these programs and policies.

Multicenter Study of Perioperative Hepatic Angioembolization as an Adjunct for Management of Major Operative Hepatic Trauma.

BACKGROUND: The management of major liver trauma continues to evolve in trauma centers across the US with increasing use of minimally invasive techniques. Data on the outcomes of these procedures remain minimal. The objective of this study was to evaluate patient complications after perioperative hepatic angioembolization as an adjunct to management of major operative liver trauma. STUDY DESIGN: A retrospective multi-institutional study was performed at 13 level 1 and level 2 trauma centers from 2012 to 2021. Adult patients with major liver trauma (grade 3 and higher) requiring operative management were enrolled. Patients were divided into 2 groups: angioembolization (AE) and no angioembolization (NO AE). Univariate and multivariate analyses were performed. RESULTS: A total of 442 patients were included with AE performed in 20.4% (n = 90 of 442) of patients. The AE group was associated with higher rates of biloma formation (p = 0.0007), intra-abdominal abscess (p = 0.04), pneumonia (p = 0.006), deep vein thrombosis (p = 0.0004), acute renal failure (p = 0.004), and acute respiratory distress syndrome (p = 0.0003), and it had longer ICU and hospital length of stay (p < 0.0001). On multivariate analysis, the AE had a significantly higher amount intra-abdominal abscess formation (odds ratio 1.9, 95% CI 1.01 to 3.6, p = 0.05). CONCLUSIONS: This is one of the first multicenter studies comparing AE in specifically operative high-grade liver injuries and found that patients with liver injury that undergo AE in addition to surgery have higher rates of both intra- and extra-abdominal complications. This provides important information that can guide clinical management.

A novel machine-learning tool to identify community risk for firearm violence: The Firearm Violence Vulnerability Index.

BACKGROUND: Firearm violence in the United States is a public health crisis, but accessing accurate firearm assault data to inform prevention strategies is a challenge. Vulnerability indices have been used in other fields to better characterize and identify at-risk populations during crises, but no tool currently exists to predict where rates of firearm violence are highest. We sought to develop and validate a novel machine-learning algorithm, the Firearm Violence Vulnerability Index (FVVI), to forecast community risk for shooting incidents, fill data gaps, and enhance prevention efforts. METHODS: Open-access 2015 to 2022 fatal and nonfatal shooting incident data from Baltimore, Boston, Chicago, Cincinnati, Los Angeles, New York City, Philadelphia, and Rochester were merged on census tract with 30 population characteristics derived from the 2020 American Community Survey. The data set was split into training (80%) and validation (20%) sets; Chicago data were withheld for an unseen test set. XGBoost, a decision tree-based machine-learning algorithm, was used to construct the FVVI model, which predicts shooting incident rates within urban census tracts. RESULTS: A total of 64,909 shooting incidents in 3,962 census tracts were used to build the model; 14,898 shooting incidents in 766 census tracts were in the test set. Historical third grade math scores and having a parent jailed during childhood were population characteristics exhibiting the greatest impact on FVVI's decision making. The model had strong predictive power in the test set, with a goodness of fit ( D2 ) of 0.77. CONCLUSION: The Firearm Violence Vulnerability Index accurately predicts firearm violence in urban communities at a granular geographic level based solely on population characteristics. The Firearm Violence Vulnerability Index can fill gaps in currently available firearm violence data while helping to geographically target and identify social or environmental areas of focus for prevention programs. Dissemination of this standardized risk tool could also enhance firearm violence research and resource allocation. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Social Vulnerability Index is strongly associated with urban pediatric firearm violence: An analysis of five major US cities.

BACKGROUND: Firearm-related injury in children is a public health crisis. The Social Vulnerability Index (SVI) identifies communities at risk for adverse effects due to natural or human-caused crises. We sought to determine if SVI was associated with pediatric firearm-related injury and thus could assist in prevention planning. METHODS: The Centers for Disease Control and Prevention's 2018 SVI data were merged on census tract with 2015 to 2022 open-access shooting incident data in children 19 years or younger from Baltimore, Chicago, Los Angeles, New York City, and Philadelphia. Regression analyses were performed to uncover associations between firearm violence, SVI, SVI themes, and social factors at the census tract level. RESULTS: Of 11,654 shooting incidents involving children, 52% occurred in just 6.7% of census tracts, which were on average in the highest quartile of SVI. A decile increase in SVI was associated with a 45% increase in pediatric firearm-related injury in all cities combined (incidence rate ratio, 1.45; 95% confidence interval, 1.41-1.49; p < 0.001). A similar relationship was found in each city: 30% in Baltimore, 51% in Chicago, 29% in Los Angeles, 37% in New York City, and 35% in Philadelphia (all p < 0.001). Socioeconomic status and household composition were SVI themes positively associated with shootings in children, as well as the social factors below poverty, lacking a high school diploma, civilian with a disability, single-parent household, minority, and no vehicle access. Living in areas with multi-unit structures, populations 17 years or younger, and speaking English less than well were negatively associated. CONCLUSION: Geospatial disparities exist in pediatric firearm-related injury and are significantly associated with neighborhood vulnerability. We demonstrate a strong association between SVI and pediatric shooting incidents in multiple major US cities. Social Vulnerability Index can help identify social and structural factors, as well as geographic areas, to assist in developing meaningful and targeted intervention and prevention efforts. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.