A Computational Approach To The Study Of Trauma
Trauma with hypovolemic shock is an extreme pathological state that challenges the body to maintain blood pressure and oxygenation in the face of hemorrhagic blood loss. In conjunction with surgical actions and transfusion therapy, survival requires the patient’s blood to maintain hemostasis to stop bleeding. The physics of the problem are multiscale: (1) the systemic circulation sets the global blood pressure in response to blood loss and resuscitation therapy, (2) local tissue perfusion is altered by localized vasoregulatory mechanisms and bleeding, and (3) altered blood and vessel biology resulting from the trauma as well as local hemodynamics control the assembly of clotting components at the site of injury. Building upon ongoing modeling efforts to simulate arterial or venous thrombosis in a diseased vasculature, we have developed models of trauma (both multiscale and machine-learning based) to understand patient risk and predict response. Key results were: (1) the upstream vascular network rapidly depressurizes to reduce blood loss, (2) wall shear rates at the hemorrhaging wound exit are sufficiently high (~10,000 s-1) to drive von Willebrand Factor unfolding, (3) full coagulopathy results in >2L blood loss in 2 hours for severing all vessels of 0.13 to 0.005 mm diameter within the bifurcating network, whereas full hemostasis limits blood loss to <100 mL within 2>min, and (4) hemodilution from transcapillary refill increases blood loss and could be implicated in trauma induced coagulopathy. Machine learning based methods were also implemented to understand trauma patient outcomes. A 400-estimator gradient boosting classifier was trained to predict survival probability and the model is able to predict a survival probability for any trauma patient and accurately distinguish between a deceased and survived patient in 92.4% of all cases. Partial dependence curves (Psurvival vs. feature value) obtained from the trained model revealed the global importance of Glasgow coma score, age, and systolic blood pressure while pulse rate, respiratory rate, temperature, oxygen saturation, and gender had more subtle single variable influences. Shapley values, which measure the relative contribution of each of the 8 features to individual patient risk, were computed for several patients and quantified patient-specific warning signs.