Abstract
Introduction: Coagulopathy following traumatic injury impairs stable blood clot formation and exacerbates mortality from hemorrhage. Understanding how these alterations impact blood clot stability is critical to improving resuscitation. Furthermore, the incorporation of machine learning algorithms to assess clinical markers, coagulation assays, and biochemical assays allows us to define the contributions of these factors to mortality. In this study, we aimed to quantify changes in clot formation and mechanics after traumatic injury and their correlation to mortality. Materials and Methods: Plasma was isolated from injured patients upon arrival to the emergency department prior to blood product administration, or procedural intervention. Coagulation kinetics and mechanics of healthy donors and patient plasma were compared with rheological, turbidity and thrombin generation assays. ELISA's were performed to determine tissue plasminogen activator and D-dimer concentration. Recursive elimination with random forest models were used to assess the predictive strength of clinical and laboratory factors. Results: Sixty-three patients were included in the study. Median injury severity score was 17, median age was 38 years, and mortality was 30%. Trauma patients exhibited reduced clot stiffness, increased fibrinolysis, and reduced thrombin generation compared to healthy donors. Deceased patients exhibited the greatest deviation from healthy levels. Fibrinogen, clot stiffness, D-dimer, and tissue plasminogen activator all demonstrated significant correlation to injury severity score. Machine-learning algorithms identified the importance of coagulation kinetics and clot structure on patient outcomes. Conclusions: Rheological markers of coagulopathy and biochemical factors are associated with injury severity and are highly predictive of mortality after trauma, providing evidence for integrated predictive models and therapeutic strategies.