A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber

摘要: Thrombus formation is a multiscale phenomenon triggered by platelet deposition over protrombotic surface (eg. ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in early diagnosis of thrombotic events, integration models thrombus at different scales requires comprehensive understanding role and limitation each modelling approach. We propose three approaches to predict deposition. Specifically, we consider measurements under blood flow conditions perfusion chamber time periods (3, 5, 10, 20 30 minutes) shear rates 212 s−1, 1390 s−1 1690 s−1. Our are: i) model based on mass-transfer boundary layer theory; ii) machine-learning approach; iii) phenomenological model. The results indicate average have median errors 21%, 20.7% 14.2%, respectively. study demonstrates feasibility using an empirical data set as proxy real-patient scenario which practitioners accumulated given number patients want obtain new patient about whom they only current observation certain variables.