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Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device.
Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device. The Journal of thoracic and cardiovascular surgery Kasinpila, P. n., Kong, S. n., Fong, R. n., Shad, R. n., Kaiser, A. D., Marsden, A. L., Woo, Y. J., Hiesinger, W. n. 2020Abstract
Aortic incompetence (AI) is observed to be accelerated in the continuous-flow left ventricular assist device (LVAD) population and is related to increased mortality. Using computational fluid dynamics (CFD), we investigated the hemodynamic conditions related to the orientation of the LVAD outflow in these patients.We identified 10 patients with new aortic regurgitation, and 20 who did not, after LVAD implantation between 2009 and 2018. Three-dimensional models of patients' aortas were created from their computed tomography scans. The geometry of the LVAD outflow graft in relation to the aorta was quantified using azimuth angles (AA), polar angles (PAs), and distance from aortic root. The models were used to run CFD simulations, which calculated the pressures and wall shear stress (rWSS) exerted on the aortic root.The AA and PA were found to be similar. However, for combinations of high values of AA and low values of PA, there were no patients with AI. The distance from aortic root to the outflow graft was also smaller in patients who developed AI (3.39 ± 0.7 vs 4.07 ± 0.77 cm, P = .04). There was no significant difference in aortic root pressures in the 2 groups. The rWSS was greater in AI patients (4.60 ± 5.70 vs 2.37 ± 1.20 dyne/cm2, P < .001). Qualitatively, we observed a trend of greater perturbations, regions of high rWSS, and flow eddies in the AI group.Using CFD simulations, we demonstrated that patients who developed de novo AI have greater rWSS at the aortic root, and their outflow grafts were placed closer to the aortic roots than those patients without de novo AI.
View details for DOI 10.1016/j.jtcvs.2020.04.164
View details for PubMedID 32653292