Association Between Emergency Medical Service Agency Volume and Mortality in Trauma Patients.

OBJECTIVE: The aim of this study was to evaluate the association of annual trauma patient volume on outcomes for emergency medical services (EMS) agencies. BACKGROUND: Regionalization of trauma care saves lives. The underlying concept driving this is a volume-outcome relationship. EMS are the entry point to the trauma system, yet it is unknown if a volume-outcome relationship exists for EMS. METHODS: A retrospective analysis of prospective cohort including 8 trauma centers and 20 EMS air medical and metropolitan ground transport agencies. Patients 18 to 90 years old with injury severity scores ≥9 transported from the scene were included. Patient and agency-level risk-adjusted regression determined the association between EMS agency trauma patient volume and early mortality. RESULTS: A total of 33,511 were included with a median EMS agency volume of 374 patients annually (interquartile range: 90-580). Each 50-patient increase in EMS agency volume was associated with 5% decreased odds of 6-hour mortality (adjusted odds ratio=0.95; 95% CI: 0.92-0.99, P =0.03) and 3% decreased odds of 24-hour mortality (adjusted odds ratio=0.97; 95% CI: 0.95-0.99, P =0.04). Prespecified subgroup analysis showed EMS agency volume was associated with reduced odds of mortality for patients with prehospital shock, requiring prehospital airway placement, undergoing air medical transport, and those with traumatic brain injury. Agency-level analysis demonstrated that high-volume (>374 patients/year) EMS agencies had a significantly lower risk-standardized 6-hour mortality rate than low-volume (<374 patients/year) EMS agencies (1.9% vs 4.8%, P <0.01). CONCLUSIONS: A higher volume of trauma patients transported at the EMS agency level is associated with improved early mortality. Further investigation of this volume-outcome relationship is necessary to leverage quality improvement, benchmarking, and educational initiatives.

Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model.

Objective: Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality. Background: The critical administration threshold (CAT, 3 or more units of red blood cells in a 60-minute period) indicates severe hemorrhage and predicts mortality, whereas early identification of such patients improves survival. Methods: Patients from the PRospective, Observational, Multicenter, Major Trauma Transfusion and Pragmatic, Randomized Optimal Platelet, and Plasma Ratio studies were identified as either CAT+ or CAT-. Candidate variables were separated into 4 tiers based on the anticipated time of availability during the patient's assessment. ML models were created with the stepwise addition of variables and compared with the baseline performance of the assessment of blood consumption (ABC) score for CAT+ prediction using a cross-validated training set and a hold-out validation test set. Results: Of 1245 PRospective, Observational, Multicenter, Major Trauma Transfusion and 680 Pragmatic, Randomized Optimal Platelet and Plasma Ratio study patients, 1312 were included in this analysis, including 862 CAT+ and 450 CAT-. A CatBoost gradient-boosted decision tree model performed best. Using only variables available prehospital or on initial assessment (Tier 1), the ML model performed superior to the ABC score in predicting CAT+ patients [area under the receiver-operator curve (AUC = 0.71 vs 0.62)]. Model discrimination increased with the addition of Tier 2 (AUC = 0.75), Tier 3 (AUC = 0.77), and Tier 4 (AUC = 0.81) variables. Conclusions: A dynamic ML model reliably identified CAT+ trauma patients with data available within minutes of trauma center arrival, and the quality of the prediction improved as more patient-level data became available. Such an approach can optimize the accuracy and timeliness of massive transfusion protocol activation.