A Multistate Study of Race and Ethnic Disparities in Access to Trauma Care.

BACKGROUND: There are well-documented disparities in outcomes for injured Black and Hispanic patients in the United States. However, patient level characteristics cannot fully explain the differences in outcomes and system-level factors, including the trauma center designation of the hospital to which a patient presents, may contribute to their worse outcomes. We aim to determine if Black and Hispanic patients are more likely to be undertriaged, compared with white patients. METHODS: This is a retrospective, cross-sectional, population-based study that uses data from the 2014 Agency for Healthcare Research and Quality Healthcare Costs and Utilization Project State Inpatient Databases. We included data from all states with available State Inpatient Databases data that included both race and hospital characteristics needed for analysis (n = 18). Logistic regression was used to identify predictors of severely injured (Injury Severity Score ≥16) patients being brought to a trauma center. RESULTS: We identified 70,970 severely injured trauma patients with complete data. Non-Hispanic White represented 74.1% of the study population, 9.8% were non-Hispanic Black, and 9.7% were Hispanic. After adjustment for other demographic and injury characteristics, Non-Hispanic Black and Hispanic patients were more likely to be undertriaged, compared with white patients (odds ratio, 1.20; 95% confidence interval, 1.12-1.29 and odds ratio, 1.39; 95% confidence interval, 1.29-1.48, respectively). Male sex and older age were associated with higher odds of undertriage, whereas urban residence, high injury severity, and penetrating injury were associated with lower odds of undertriage. CONCLUSIONS: Severely injured Black and Hispanic trauma patients are more likely to be undertriaged than otherwise similar white patients. The factors that contribute to racial and ethnic disparities in receiving trauma center care need to be identified and addressed to provide equitable trauma care.

Assessing superficial temporal artery-middle cerebral artery anastomosis patency using FLOW 800 hemodynamics.

OBJECTIVE: The objective of this study was to investigate the use of indocyanine green videoangiography with FLOW 800 hemodynamic parameters intraoperatively during superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery to predict patency prior to anastomosis performance. METHODS: A retrospective and exploratory data analysis was conducted using FLOW 800 software prior to anastomosis to assess four regions of interest (ROIs; proximal and distal recipients and adjacent and remote gyri) for four hemodynamic parameters (speed, delay, rise time, and time to peak). Medical records were used to classify patients into flow and no-flow groups based on immediate or perioperative anastomosis patency. Hemodynamic parameters were compared using univariate and multivariate analyses. Principal component analysis was used to identify high risk of no flow (HRnf) and low risk of no flow (LRnf) groups, correlated with prospective angiographic follow-ups. Machine learning models were fitted to predict patency using FLOW 800 features, and the a posteriori effect of complication risk of those features was computed. RESULTS: A total of 39 cases underwent STA-MCA bypass surgery with complete FLOW 800 data collection. Thirty-five cases demonstrated flow after anastomosis revascularization and were compared with 4 cases with no flow after revascularization. Proximal and distal recipient speeds were significantly different between the no-flow and flow groups (proximal: 238.3 ± 120.8 and 138.5 ± 93.6, respectively [p < 0.001]; distal: 241.0 ± 117.0 and 142.1 ± 103.8, respectively [p < 0.05]). Based on principal component analysis, the HRnf group (n = 10) was characterized by high-flow speed (> 75th percentile) in all ROIs, whereas the LRnf group (n = 10) had contrasting patterns. In prospective long-term follow-up, 6 of 9 cases in the HRnf group, including the original no-flow cases, had no or low flow, whereas 8 of 8 cases in the LRnf group maintained robust flow. Machine learning models predicted patency failure with a mean F1 score of 0.930 and consistently relied on proximal recipient speed as the most important feature. Computation of posterior likelihood showed a 95.29% chance of patients having long-term patency given a lower proximal speed. CONCLUSIONS: These results suggest that a high proximal speed measured in the recipient vessel prior to anastomosis can elevate the risk of perioperative no flow and long-term reduction of flow. With an increased dataset size, continued FLOW 800-based ROI metric analysis could be used to guide intraoperative anastomosis site selection prior to anastomosis and predict patency outcome.