Mathematical Model for Combined Effects of Temperature and Pressure in Causing Soft Tissue Injury

Abstract

Pressure and thermal injuries affect millions of lives every year. Both types of injury have been a focal priority for the medical community to understand and treat, especially since World War II. Typically, each injury mechanism is studied in isolation due to differences in the primary factors causing the trauma. The serial confluence of applied tissue deformation, inflammation, and ischemia imposed over time combine to cause mechanically derived injury, whereas high or low temperatures are causative factors for thermal injuries. Modeling and simulation analyses have been used to develop an understanding of both types of injury pathways to predict threshold conditions for the onset of damage and to develop methods of prevention. Although thermal and mechanical injury processes are often viewed independently, prior experiments have demonstrated that these two phenomena may have active cross-coupling. Temperature can act as a prophylactic or accelerant to pressure injuries, depending on its magnitude, to radically alter the shape, position, time, and final disposition. In this study, we developed a finite element model to predict injury as a function of time, temperature, mechanical deformation, and local blood perfusion. The model embodies an equation with constitutive terms that are relevant to physiological processes that govern the development of injury. The current model is a composite derived from regression of experimental data and conformation to the physiological structure. It expresses highly nonlinear coupling across the domains of thermal, mechanical, and fluid transport and is solved for composite hard and soft tissue geometry combinations. We evaluated the model for a broad range of trauma conditions defined by subject morphology, mechanical loading, temperature, and time. Moreover, we implemented the model within a finite element framework to estimate the risk of injury for specific stress conditions via large-scale simulations.