Thaker Rishi, Araujo-Gutierrez Raquel, Marcos-Abdala Hernan G, Agrawal Tanushree, Fida Nadia, Kassi Mahwash
Touro College of Osteopathic Medicine, Middletown, New York, NY 10940, USA.
Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX 77030, USA.
J Clin Med. 2019 May 9;8(5):635. doi: 10.3390/jcm8050635.
Left ventricular assist devices (LVAD) cause altered flow dynamics that may result in complications such as stroke, pump thrombosis, bleeding, or aortic regurgitation. Understanding altered flow dynamics is important in order to develop more efficient and durable pump configurations. In patients with LVAD, hemodynamic assessment is limited to imaging techniques such as echocardiography which precludes detailed assessment of fluid dynamics. In this review article, we present some innovative modeling techniques that are often used in device development or for research purposes, but have not been utilized clinically. Computational fluid dynamic (CFD) modeling is based on computer simulations and particle image velocimetry (PIV) employs ex vivo models that helps study fluid characteristics such as pressure, shear stress, and velocity. Both techniques may help elaborate our understanding of complications that occur with LVAD and could be potentially used in the future to troubleshoot LVAD-related alarms. These techniques coupled with 3D printing may also allow for patient-specific device implants, lowering the risk of complications increasing device durability.
左心室辅助装置(LVAD)会导致血流动力学改变,这可能会引发中风、泵血栓形成、出血或主动脉瓣反流等并发症。了解血流动力学改变对于开发更高效、耐用的泵配置至关重要。在LVAD患者中,血流动力学评估仅限于超声心动图等成像技术,这使得无法对流体动力学进行详细评估。在这篇综述文章中,我们介绍了一些创新的建模技术,这些技术常用于设备开发或研究目的,但尚未在临床上得到应用。计算流体动力学(CFD)建模基于计算机模拟,粒子图像测速技术(PIV)采用体外模型,有助于研究压力、剪切应力和速度等流体特性。这两种技术都可能有助于深化我们对LVAD相关并发症的理解,并有可能在未来用于解决LVAD相关警报。这些技术与3D打印相结合,还可以实现针对患者的设备植入,降低并发症风险,提高设备耐用性。