Key result
Computational heart valve models offer cost-effective evaluation of flow parameters and risk in valvular disease.
Why the study?
Heart valve repair and replacement carry long-term stability and biocompatibility issues, highlighting the need for more robust methods such as computational modeling to resolve valvular disease.
Computational methods provide a cost-effective approach to evaluate heart valve flow parameters and improve the understanding of valvular disease.
May inform valvular device and risk research; leaves open clinical translation pending validation.
This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid-structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility.
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Toma et al. (2022) conducted a review in Valvular disease. Computational heart valve models was evaluated. Computational heart valve models and fluid dynamics analyses provide a cost-effective tool to evaluate flow parameters and understand risk and compatibility in valvular disease.
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