Injury Severity is a Key Contributor to Coagulation Dysregulation and Fibrinogen Consumption

BackgroundTraumatic injury is a leading cause of death for those under the age of 45, with 40% occurring due to hemorrhage. Severe tissue injury and hypoperfusion lead to marked changes in coagulation, thereby preventing formation of a stable blood clot and increasing hemorrhage associated mortality. ObjectivesWe aimed to quantify changes in clot formation and mechanics occurring after traumatic injury and the relationship to coagulation kinetics, and fibrinolysis. MethodsPlasma was isolated from injured patients upon arrival to the emergency department. Coagulation kinetics and mechanics of healthy donors and patient plasma were compared with rheological, turbidimetric and thrombin generation assays. ELISAs were performed to determine tissue plasminogen activator (tPA) and D-dimer concentration, as fibrinolytic markers. ResultsSixty-three patients were included in the study. The median injury severity score (ISS) was 17, median age was 37.5 years old, and mortality rate was 30%. Rheological, turbidimetric and thrombin generation assays indicated that trauma patients on average, and especially deceased patients, exhibited reduced clot stiffness, increased fibrinolysis and reduced thrombin generation compared to healthy donors. Fibrinogen concentration, clot stiffness, D-dimer and tPA all demonstrated significant direct correlation to increasing ISS. Machine learning algorithms identified and highlighted the importance of clinical factors on determining patient outcomes. ConclusionsViscoelastic and biochemical assays indicate significant contributors and predictors of mortality for improved patient treatment and therapeutic target detection. ESSENTIALSO_LITraumatic injury may lead to alterations in a patients ability to form stable blood clots C_LIO_LIA study was performed to assess how trauma severity affects coagulation kinetics C_LIO_LIKey alterations were observed in trauma patients, who exhibit weaker and slower forming clots C_LIO_LIPaired with machine learning methods, the results indicate key aspects contributing to mortality C_LI