CFD case study to optimize surgical adjustment of ventricular assist device implantation to minimize stroke risk part I: steady-state CFD modeling

Presently, mechanical support is the most promising alternative to cardiac transplantation. Ventricular Assist Devices (VADs) were originally used to provide mechanical circulatory support in patients waiting planned heart transplantation (“bridge-to-transplantation” therapy). The success of short-term bridge devices led to clinical trials evaluating the clinical suitability of long-term support (“destination” therapy) with left ventricular assist devices (LVADs). The first larger-scale, randomized trial that tested long-term support with a LVAD reported a 44% reduction in the risk of stroke or death in patients with a LVAD. In spite of the success of LVADs as bridge-to-transplantation and long-term support. Patients carrying these devices are still at risk of several adverse events. The most devastating complication is caused by embolization of thrombi formed within the LVAD or inside the heart into the brain. Despite anticoagulation management and improved LVAD design, there is still significant occurrence of thromboembolic events in patients. Investigators have reported that the incidence of thromboembolic cerebral events ranges from 14% to 47% over a period of 6-12 months. Accepting the current rate of thrombus formation within the LVAD, an alternative method to reduce the incidence of cerebral embolization is hypothesized: thromboembolism to the carotid and vertebral arteries can be minimized by adjusting the placement of the LVAD outflow conduit, or by the placement of an aorticto-innominate artery bypass graft, or by the placement of an aortic-to-left-carotid artery bypass graft, or possibly a combination of these. We present a computational fluid dynamics (CFD) study of the aortic arch bed hemodynamics using a representative geometry of the human aortic arch and an alternative aortic bypass whose express purpose is to investigate the hypothesis. We utilize the CFD code, STARCCM+, in which a Lagrangian particle-tracking model is coupled to the fluid flow solver to predict particle trajectories. In this