Heart failure (HF) is one the leading causes of hospitalization in the elderly population (65 years or over), with a prevalence of 23 million patients worldwide (Bui, et al., 2011), thus putting a significant strain on healthcare agencies. If left untreated, HF can be fatal, and in the USA alone, approximately 1 in 9 deaths are attributed to HF (Go, et al., 2013). One of the most common treatments for HF patients involves using a battery-assisted mechanical pump, called the left ventricular assist device (LVAD), to pump blood to the human body sufficiently. Although LVAD therapy shows favorable short- to mid-term clinical results (Rose, et al., 2001), their long-term mortality rate and the device performance remain suboptimal (Grimm, et al., 2015). Primary reasons for this suboptimal clinical performance of LVAD therapy are the high-risk open-chest surgery that is required to implant the LVAD inside the human body, blood damage (hemolysis) and formation of a blood clot (thrombosis) caused due to LVAD itself. To overcome these issues, novel transcatheter based LVADs are proposed to allow implantation through minimally invasive techniques, but their hemodynamic performance is not adequately analyzed.

In silico studies based on computational fluid dynamics (CFD) are playing an increasingly important role in developing and evaluating medical devices, as they are faster and more flexible than in vitro experiments and can simulate device performance under a wide range of flow conditions and geometries. CFD models can also be combined with medical images to simulate in vivo conditions. The use of biomedical CFD has been recognized by the FDA. It has become part of its critical path initiative (CPI) to improve test methodologies and processes that could bring new and safer medical products to patients more quickly.