Endotheliopathy Is Associated With a 24-Hour Fibrinolysis Phenotype Described by Low TEG Lysis and High d-Dimer After Trauma

Objectives:. Determine associations between biomarkers of endotheliopathy, 24-hour fibrinolysis phenotypes and clinical outcomes after trauma. Background:. The vascular endothelium is a critical regulator of hemostasis and organ function. The relationship between markers of endotheliopathy and fibrinolysis following trauma has not been evaluated. Methods:. We performed a secondary analysis of prospectively collected biomarker data in the Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) randomized controlled trial. We stratified subjects by 24-hour thromboelastography (TEG) percent clot lysis (LY30) and plasma d-dimer (DD) levels and evaluated differences in endotheliopathy biomarkers and clinical outcomes between subjects with one of four 24-hour fibrinolysis phenotypes: LY30 0.9% to 2.9% (LY30norm), LY30 > 2.9% (LY30high), LY30 < 0.9% and low DD (LY30low+DDlow), and LY30 < 0.9% and high DD (LY30low+DDhigh). Results:. The analysis included 168 subjects with LY30norm, 32 with LY30high, 147 with LY30low+DDlow, and 124 with LY30low+DDhigh. LY30low+DDhigh subjects had greater injury severity and a higher incidence of severe head injury, multiorgan failure (MOF), and mortality than the other phenotypes. All endotheliopathy biomarkers were significantly higher in the LY30low+DDhigh phenotype. Adjusting for injury severity, mechanism, and head trauma, 24-hour angiopoietin-2 and soluble thrombomodulin were independently associated with the LY30low+DDhigh phenotype. Both endothelial biomarkers were discriminating for MOF. Subjects with thrombomodulin level >9.5 ng/mL and angiopoietin-2 level >3.6 ng/mL accounted for 64% of subjects who developed MOF. Conclusions:. In a multicenter trauma cohort, subjects with a fibrinolysis phenotype characterized by low TEG lysis and elevated DD 24 hours after injury have significantly worse endotheliopathy and clinical outcomes. Our findings support mechanistic evaluations of the role of the endothelium in fibrinolysis dysregulation that may drive late-stage organ injury

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