Title

Mathematical Modeling Of Patient-Specific Ventricular Assist Device Implantation To Reduce Particulate Embolization Rate To Cerebral Vessels

Keywords

circulatory assist devices; circulatory hemodynamics,brain thrombosis; computer applications; stroke

Abstract

Stroke is the most devastating complication after ventricular assist device (VAD) implantation, with an incidence of 14%-47% despite improvements in device design and anticoagulation. This complication continues to limit the widespread implementation of VAD therapy. Patient-specific computational fluid dynamics (CFD) analysis may elucidate ways to reduce this risk. A patient-specific three-dimensional model of the aortic arch was generated from computed tomography. A 12mm VAD outflow-graft (VAD-OG) "anastomosed" to the aorta was rendered. CFD was applied to study blood flow patterns. Particle tracks, originating from the VAD, were computed with a Lagrangian phase model and percentage of particles entering the cerebral vessels was calculated. Twelve implantation configurations of the VAD-OG and three particle sizes (2, 4, and 5 mm) were considered. Percentage of particles entering the cerebral vessels ranged from 6% for the descending aorta VAD-OG anastomosis, to 14% for the ascending aorta at 90 deg VAD-OG anastomosis. Values were significantly different among all configurations (X2=3925, p<0.0001). Shallower and more cephalad anastomoses prevented formation of zones of recirculation in the ascending aorta. In this computational model and within the range of anatomic parameters considered, the percentage of particles entering the cerebral vessels from a VAD-OG is reduced by nearly 60% by optimizing outflow-graft configuration. Ascending aorta recirculation zones, which may be thrombogenic, can also be eliminated. CFD methods coupled with patient-specific anatomy may aid in identifying the optimal location and angle for VAD-OG anastomosis to minimize stroke risk. © 2014 by ASME.

Publication Date

1-1-2014

Publication Title

Journal of Biomechanical Engineering

Volume

136

Issue

7

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/1.4026498

Socpus ID

84900852202 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84900852202

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