(230ar) Predicting Fractional Flow Reserve Using Computational Fluid Dynamics and Response Surface Method Statistics
Cardiovascular diseases remain the number one cause of death above cancer in the US. Invasive measurement of fractional flow reserve (FFR) is the gold standard of care to determine if a patient has significant blockage (stenosis) that limits blood flow to the heart muscle in the coronary arteries. FFR is determined invasively by inserting a pressure measurement wire across a stenotic coronary lesion, and then computing the ratio of pressure distal to the stenosis during maximum blood flow to pressure during normal flow in the same artery. FFR < 0.8 suggests that the stenosis limits blood flow to the down-stream heart muscle and should be treated by stent placement. In this study, we modeled blood flow through coronary arteries exhibiting varying degrees of stenosis using computational fluid dynamics (CFD). A second-order central-composite design with analysis by RSM identified the effects of blood pressure, flow rate, and degree of stenosis on computationally determined FFR values. Analysis of variance (ANOVA) showed a high variance coefficient (R2) value of 0.935, thus ensuring a satisfactory adjustment of the model with the CFD results. According to the RSM analysis, flow rate had a significant effect on FFR (p<0.05). Increasing flow rate decreased FFR, which is consistent with clinical data. On the other hand, the effect of pressure over a physiologically relevant range was not significant. From the statistical analysis, FFR can be predicted for a given coronary anatomy as a function of blood pressure and flow rate.