The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.

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Arterial stiffness is an important risk factor for cardiovascular events. Radial augmentation index (AI r) can be more conveniently measured compared with carotid-femoral pulse wave velocity (cfPWV). However, the performance of AI r in assessing arterial stiffness is limited. This study proposes a novel index AI rd, a combination of AI r and diastolic augmentation index (AI d) with a weight α, to achieve better performance over AI r in assessing arterial stiffness. 120 subjects (43 ± 21 years old) were enrolled. The best-fit α is determined by the best correlation coefficient between AI rd and cfPWV. The performance of the method was tested using the 12-fold cross validation method. AI rd (r = 0.68, P < 0.001) shows a stronger correlation with cfPWV and a narrower prediction interval than AI r (r = 0.61, P < 0.001), AI d (r = −0.17, P = 0.06), the central augmentation index (AI c) (r = 0.61, P < 0.001) or AI c normalized for heart rate of 75 bpm (r = 0.65, P < 0.001). Compared with AI r (age, P < 0.001; gender, P < 0.001; heart rate, P < 0.001; diastolic blood pressure, P < 0.001; weight, P = 0.001), AI rd has fewer confounding factors (age, P < 0.001; gender, P < 0.001). In conclusion, AI rd derives performance improvement in assessing arterial stiffness, with a stronger correlation with cfPWV and fewer confounding factors.

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