Blood flow velocity prediction in aorto-iliac stent grafts using computational fluid dynamics and Taguchi method. (1st May 2017)

• Record Type: Journal Article
• Authors: Chong, Albert Y.; Doyle, Barry J.; Jansen, Shirley; Ponosh, Stefan; Cisonni, Julien; Sun, Zhonghua
• Is Part Of: Computers in biology and medicine. Volume 84(2017)
• Page Start: 235
• Page End: 246
• Publication Date: 2017-05-01
• Subjects: Analysis of variance -- Aortoiliac stenting -- Computational fluid dynamics -- Multiple linear regressions -- Orthogonal array -- Peak Systolic Velocity -- Taguchi method; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.03.015
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Covered Endovascular Reconstruction of Aortic Bifurcation (CERAB) is a new technique to treat extensive aortoiliac occlusive disease with covered expandable stent grafts to rebuild the aortoiliac bifurcation. Post stenting Doppler ultrasound (DUS) measurement of maximum peak systolic velocity (PSVmax ) in the stented segment is widely used to determine patency and for follow up surveillance due to the portability, affordability and ease of use. Anecdotally, changes in hemodynamics created by CERAB can lead to falsely high PSVmax requiring CT angiography (CTA) for further assessment. Therefore, the importance of DUS would be enhanced with a proposed PSVmax prediction tool to ascertain whether PSVmax falls within the acceptable range of prediction. We have developed a prediction tool based on idealized models of aortoiliac bifurcations with various infra-renal PSV (PSVin ), iliac to aortic area ratios (R) and aortoiliac bifurcation angles (α). Taguchi method with orthogonal arrays (OA) was utilized to minimize the number of Computational Fluid Dynamics (CFD) simulations performed under physiologically realistic conditions. Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) analyses were performed to assess Goodness of fit and to predict PSVmax. PSVin and R were found to contribute 94.06% and 3.36% respectively to PSVmax . The Goodness of fit based on adjusted R 2 improved from 99.1% to 99.9% based on linear and exponential functions. The PSVmax predictor based on the exponential model was evaluated with sixteen patient specific cases with a mean prediction error of 9.9% and standard deviation of 6.4%. Eleven out of sixteen cases (69%) in our current retrospective studies would have avoided CTA if the proposed predictor was used to screen out DUS measured PSVmax with prediction error greater than 15%. The predictor therefore has the potential to be used as a clinical tool to detect PSVmax more accurately post aortoiliac stenting and might reduce diagnostic errors and avoid unnecessary expense and risk from CTA follow-up imaging.