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Platelets play a salient role in the pathogenesis of coronary diseases; primarily through their adhesion to other platelets, endothelial cells and plasma proteins. It is necessary for platelets to activate in order for them to adhere to these different substrates. One of the key regulatory mechanical factors in platelet activation is shear stress, which has been shown to alter multiple platelet functions through the activation of mechanoreceptors. Our goal was to investigate how different numerical shear stress tracking techniques affect platelet adhesion estimates within physiologically relevant computational models. Previously, we developed a physiological coronary artery computational fluid dynamics model. Shear stress waveforms, obtained from these models, were used to monitor  in vitro  platelet and endothelial cell adhesion marker expression. In this work, the adhesion marker expression data was regressed to obtain numerical functions for receptor expression predictions. These functions were input into a customized adhesion model utilizing different shear stress tracking techniques. For the normal vascular conditions and minimal pathological disease models, shear stress tracking did not significantly affect the adhesion estimates. However, for the severe pathological model, the two shear stress tracking methods had vastly different estimates. Therefore, shear stress tracking methods must be chosen accurately to predict platelet adhesion potentials for accurate modeling techniques.

Platelet adhesion potential estimation in a normal and diseased coronary artery model: effects of shear stress magnitude versus shear stress history