Patient-Specific Computational Fluid Dynamics Reveal Localized Flow Patterns Predictive of Post–Left Ventricular Assist Device Aortic Incompetence. (July 2021)

• Record Type: Journal Article
• Authors: Shad, Rohan; Kaiser, Alexander D.; Kong, Sandra; Fong, Robyn; Quach, Nicolas; Bowles, Cayley; Kasinpila, Patpilai; Shudo, Yasuhiro; Teuteberg, Jeffrey; Woo, Y. Joseph; Marsden, Alison L.; Hiesinger, William
• Is Part Of: Circulation. Volume 14:Number 7(2021)
• Publication Date: 2021-07
• Subjects: aorta -- aortic valve insufficiency -- artificial intelligence -- hemodynamics -- hydrodynamics; Heart failure -- Periodicals; 616.129005
• DOI: 10.1161/CIRCHEARTFAILURE.120.008034
• Languages: English
• ISSNs: 1941-3289

Abstract

Abstract : Background: Progressive aortic valve disease has remained a persistent cause of concern in patients with left ventricular assist devices. Aortic incompetence (AI) is a known predictor of both mortality and readmissions in this patient population and remains a challenging clinical problem. Methods: Ten left ventricular assist device patients with de novo aortic regurgitation and 19 control left ventricular assist device patients were identified. Three-dimensional models of patients' aortas were created from their computed tomography scans, following which large-scale patient-specific computational fluid dynamics simulations were performed with physiologically accurate boundary conditions using the SimVascular flow solver. Results: The spatial distributions of time-averaged wall shear stress and oscillatory shear index show no significant differences in the aortic root in patients with and without AI (mean difference, 0.67 dyne/cm 2 [95% CI, −0.51 to 1.85]; P =0.23). Oscillatory shear index was also not significantly different between both groups of patients (mean difference, 0.03 [95% CI, −0.07 to 0.019]; P =0.22). The localized wall shear stress on the leaflet tips was significantly higher in the AI group than the non-AI group (1.62 versus 1.35 dyne/cm 2 ; mean difference [95% CI, 0.15–0.39]; P <0.001), whereas oscillatory shear index was not significantly different between both groups (95% CI, −0.009 to 0.001; P =0.17). Conclusions: Computational fluid dynamics serves a unique role in studying the hemodynamic features in left ventricular assist device patients where 4-dimensional magnetic resonance imaging remains unfeasible. Contrary to the widely accepted notions of highly disturbed flow, in this study, we demonstrate that the aortic root is a region of relatively stagnant flow. We further identified localized hemodynamic features in the aortic root that challenge our understanding of how AI develops in this patient population.