Key result
Computational fluid dynamics shows promise for atherosclerosis risk stratification despite inconsistencies from parameter definitions.
Why the study?
Computational fluid dynamics results are often inconsistent with pathophysiological manifestations due to inconsistent parameters, limited data, and plaque complexity.
This review highlights the challenges and inconsistencies in applying computational fluid dynamics to cardiovascular flow and atherosclerosis, suggesting areas for methodological improvement.
CFD warrants caution before clinical atherosclerosis risk stratification; leaves open the need for standardized parameters in future modeling studies.
Computational Fluid Dynamics (CFD) plays an important role in assessment of genesis and prognosis of atherosclerosis and as a promising tool for risk stratification. CFD is widely used by a majority of the researchers, although, many experimental studies show that the results obtained by CFD are inconsistent with the physio-pathological manifestations, which is mainly due to inconsistent definition of parameters, limited data availability and extreme complexity and heterogeneous behavior of plaque growth and its rupture. In this review, the results obtained by Computational Fluid Dynamics are summarized and an attempt is made to deduce the broad causes of various errors and possible solutions.
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Kumar et al. (2023) conducted a review in Atherosclerosis. Computational Fluid Dynamics (CFD) was evaluated. Computational Fluid Dynamics is a promising tool for atherosclerosis risk stratification, though results are often inconsistent with physio-pathological manifestations due to parameter definitions.
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