Mehrabadi Marmar, Casa Lauren D C, Aidun Cyrus K, Ku David N
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Ann Biomed Eng. 2016 Aug;44(8):2339-2350. doi: 10.1007/s10439-016-1550-5. Epub 2016 Jan 21.
The ability to predict the timescale of thrombotic occlusion in stenotic vessels may improve patient risk assessment for thrombotic events. In blood contacting devices, thrombosis predictions can lead to improved designs to minimize thrombotic risks. We have developed and validated a model of high shear thrombosis based on empirical correlations between thrombus growth and shear rate. A mathematical model was developed to predict the growth of thrombus based on the hemodynamic shear rate. The model predicts thrombus deposition based on initial geometric and fluid mechanic conditions, which are updated throughout the simulation to reflect the changing lumen dimensions. The model was validated by comparing predictions against actual thrombus growth in six separate in vitro experiments: stenotic glass capillary tubes (diameter = 345 µm) at three shear rates, the PFA-100(®) system, two microfluidic channel dimensions (heights = 300 and 82 µm), and a stenotic aortic graft (diameter = 5.5 mm). Comparison of the predicted occlusion times to experimental results shows excellent agreement. The model is also applied to a clinical angiography image to illustrate the time course of thrombosis in a stenotic carotid artery after plaque cap rupture. Our model can accurately predict thrombotic occlusion time over a wide range of hemodynamic conditions.
预测狭窄血管中血栓形成闭塞的时间尺度的能力可能会改善患者血栓形成事件的风险评估。在血液接触装置中,血栓形成预测可以带来改进的设计,以将血栓形成风险降至最低。我们基于血栓生长与剪切速率之间的经验相关性,开发并验证了一种高剪切血栓形成模型。开发了一个数学模型,以基于血液动力学剪切速率预测血栓的生长。该模型根据初始几何和流体力学条件预测血栓沉积,这些条件在整个模拟过程中不断更新,以反映管腔尺寸的变化。通过将预测结果与六个单独的体外实验中的实际血栓生长情况进行比较,对该模型进行了验证:三种剪切速率下的狭窄玻璃毛细管(直径 = 345 µm)、PFA-100(®) 系统、两种微流体通道尺寸(高度 = 300 和 82 µm)以及一个狭窄的主动脉移植物(直径 = 5.5 mm)。预测的闭塞时间与实验结果的比较显示出极好的一致性。该模型还应用于临床血管造影图像,以说明斑块帽破裂后狭窄颈动脉中血栓形成的时间进程。我们的模型可以在广泛的血液动力学条件下准确预测血栓形成闭塞时间。