Why the study?
Accurate numerical simulations of coronary blood flow, particularly in high-risk atherosclerotic patients, have presented a major challenge for clinical applications.
Does a numerical simulation combining the five-element Windkessel and sPTT models accurately estimate FFR compared to invasive measurement in a patient with coronary artery stenosis?
Does a numerical simulation combining the five-element Windkessel and sPTT models accurately estimate FFR compared to invasive measurement in a patient with coronary artery stenosis?
A novel computational model combining the five-element Windkessel and sPTT models accurately estimated FFR with only 0.37% deviation from invasive measurement in a proof-of-concept case.
Coronary artery diseases (CADs) are a leading cause of death worldwide. Accurate numerical simulations of coronary blood flow, especially in high-risk atherosclerotic patients, have been a major challenge for clinical applications. This study pioneers a novel approach combining the physiologically accurate five-element Windkessel and sPTT models to enhance the accuracy of the hemodynamics and the fractional flow reserve (FFR) parameter. User-defined functions (UDFs) of the outlet pressure boundary condition (Windkessel model) and the viscoelastic characteristics of blood (sPTT model) were developed and dynamically loaded with ANSYS® 2023 software. In a proof-of-concept study, a patient’s left coronary artery with 40% stenosis was provided by the hospital for further analysis. The numerical FFR value obtained in the present work skews only 0.37% from the invasive measurement in the hospital. This highlights the important roles of both blood viscoelasticity and the five-element Windkessel model in hemodynamic simulations. This proof-of-concept of the FFR numerical calculation tool provides a promising comprehensive assessment of atherosclerosis in a fast, accurate, more affordable, and fully non-invasive manner. After validation with more patient cases in the future, this tool could be employed in hospitals and offer a more accurate and individualized approach for the diagnosis and treatment of CAD.
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Fernandes et al. (2023) studied this question.
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