Grinstein, J; Torii, R; Bourantas, CV; Garcia-Garcia, HM; (2021) Left Ventricular Assist Device Flow Pattern Analysis Using a Novel Model Incorporating Left Ventricular Pulsatility. ASAIO Journal , 67 (7) pp. 724-732. 10.1097/MAT.0000000000001341.

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Abstract

Our current understanding of flow through the circuit of left ventricular assist device (LVAD), left ventricle and ascending aorta remains incompletely understood. Computational fluid dynamics, which allow for analysis of flow in the cardiovascular system, have been used for this purpose, although current simulation models have failed to fully incorporate the interplay between the pulsatile left ventricle and continuous-flow generated by the LVAD. Flow-through the LVAD is dependent on the interaction between device and patient-specific factors with suboptimal flow patterns evoking increased risk of LVAD-related complications. Computational fluid dynamics can be used to analyze how different pump and patient factors affect flow patterns in the left ventricle and the aorta. Computational fluid dynamics simulations were carried out on a patient with a HeartMate II. Simulations were also conducted for theoretical scenarios substituting HeartWare HVAD, HeartMate 3 (HM3) in continuous mode and HM3 with Artificial Pulse. An anatomical model of the patient was reconstructed from computed tomography (CT) images, and the LVAD outflow was used as the inflow boundary condition. The LVAD outflow was calculated separately using a lumped-parameter-model of the systemic circulation, which was calibrated to the patient based on the patient-specific ventricular volume change reconstructed from 4 dimensional computed tomography and pulmonary capillary wedge pressure tracings. The LVADs were implemented in the lumped-parameter-model via published pressure head versus flow (H-Q) curves. To quantify the flushing effect, virtual contrast agent was released in the ascending aorta and its flushing over the cycles was quantified. Shear stress acting on the aortic endothelium and shear rate in the bloodstream were also quantified as indicators of normal/abnormal blood flow, especially the latter being a biomarker of platelet activation and hemolysis. LVAD speeds for the HVAD and HM3 were selected to match flow rates for the patient’s HMII (9,000 RPM for HMII, 5,500 RPM for HM3, and 2,200 RPM for HVAD), the cardiac outputs were 5.81 L/min, 5.83 L/min, and 5.92 L/min, respectively. The velocity of blood flow in the outflow cannula was higher in the HVAD than in the two HeartMate pumps with a cycle average (range) of 0.92 m/s (0.78–1.19 m/s), 0.91 m/s (0.86–1.00 m/s), and 1.74 m/s (1.40–2.24 m/s) for HMII, HM3, and HVAD, respectively. Artificial pulse increased the peak flow rate to 9.84 L/min for the HM3 but the overall cardiac output was 5.96 L/min, which was similar to the continuous mode. Artificial pulse markedly decreased blood stagnation in the ascending aorta; after six cardiac cycles, 48% of the blood was flushed out from the ascending aorta under the continuous operation mode while 60% was flushed under artificial pulse. Shear stress and shear rate in the aortic arch were higher with the HVAD compared to the HMII and HM3, respectively (shear stress: 1.76 vs. 1.33 vs. 1.33 Pa, shear rate: 136 vs. 91.5 vs. 89.4 s–1). Pump-specific factors such as LVAD type and programmed flow algorithms lead to unique flow patterns which influence blood stagnation, shear stress, and platelet activation. The pump-patient interaction can be studied using a novel computational fluid dynamics model to better understand and potentially mitigate the risk of downstream LVAD complications.

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