Gosselin Andrew R, Bargoud Christopher G, Sawalkar Abhishek, Mathew Shane, Toussaint Ashley, Greenen Matthew, Coyle Susette M, Macor Marie, Krishnan Anandi, Goswami Julie, Hanna Joseph S, Tutwiler Valerie
Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey.
Department of Surgery, Division of Acute Care Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey.
Shock. 2025 Apr 1;63(4):587-596. doi: 10.1097/SHK.0000000000002544. Epub 2025 Jan 23.
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.
创伤性损伤后的凝血病会损害稳定的血凝块形成，并加剧出血导致的死亡率。了解这些改变如何影响血凝块稳定性对于改善复苏至关重要。此外，纳入机器学习算法来评估临床标志物、凝血测定和生化测定，使我们能够确定这些因素对死亡率的影响。在本研究中，我们旨在量化创伤性损伤后血凝块形成和力学的变化及其与死亡率的相关性。
在给予血液制品或进行程序干预之前，从抵达急诊科的受伤患者中分离血浆。将健康供体和患者血浆的凝血动力学和力学与流变学、浊度和凝血酶生成测定进行比较。进行酶联免疫吸附测定以确定组织纤溶酶原激活物和D - 二聚体浓度。使用随机森林模型的递归消除法来评估临床和实验室因素的预测强度。
63名患者纳入研究。损伤严重程度评分中位数为17，年龄中位数为38岁，死亡率为30%。与健康供体相比，创伤患者表现出血凝块硬度降低、纤维蛋白溶解增加和凝血酶生成减少。死亡患者与健康水平的偏差最大。纤维蛋白原、血凝块硬度、D - 二聚体和组织纤溶酶原激活物均与损伤严重程度评分显著相关。机器学习算法确定了凝血动力学和凝块结构对患者预后的重要性。
凝血病的流变学标志物和生化因素与损伤严重程度相关，并且是创伤后死亡率的高度预测指标，为综合预测模型和治疗策略提供了证据。
