Detmer Felicitas J, Mut Fernando, Slawski Martin, Hirsch Sven, Bijlenga Philippe, Cebral Juan R
Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
Statistics Department, George Mason University, Fairfax, VA, USA.
Acta Neurochir (Wien). 2020 Mar;162(3):553-566. doi: 10.1007/s00701-020-04234-8. Epub 2020 Feb 1.
Hemodynamic patterns have been associated with cerebral aneurysm instability. For patient-specific computational fluid dynamics (CFD) simulations, the inflow rates of a patient are typically not known. The aim of this study was to analyze the influence of inter- and intra-patient variations of cerebral blood flow on the computed hemodynamics through CFD simulations and to incorporate these variations into statistical models for aneurysm rupture prediction.
Image data of 1820 aneurysms were used for patient-specific steady CFD simulations with nine different inflow rates per case, capturing inter- and intra-patient flow variations. Based on the computed flow fields, 17 hemodynamic parameters were calculated and compared for the different flow conditions. Next, statistical models for aneurysm rupture were trained in 1571 of the aneurysms including hemodynamic parameters capturing the flow variations either by defining hemodynamic "response variables" (model A) or repeatedly randomly selecting flow conditions by patients (model B) as well as morphological and patient-specific variables. Both models were evaluated in the remaining 249 cases.
All hemodynamic parameters were significantly different for the varying flow conditions (p < 0.001). Both the flow-independent "response" model A and the flow-dependent model B performed well with areas under the receiver operating characteristic curve of 0.8182 and 0.8174 ± 0.0045, respectively.
The influence of inter- and intra-patient flow variations on computed hemodynamics can be taken into account in multivariate aneurysm rupture prediction models achieving a good predictive performance. Such models can be applied to CFD data independent of the specific inflow boundary conditions.
血流动力学模式与脑动脉瘤的不稳定性相关。对于患者特异性计算流体动力学（CFD）模拟，患者的流入速率通常是未知的。本研究的目的是通过CFD模拟分析患者间和患者内脑血流变化对计算出的血流动力学的影响，并将这些变化纳入动脉瘤破裂预测的统计模型中。
使用1820个动脉瘤的图像数据进行患者特异性稳态CFD模拟，每个病例有九种不同的流入速率，以捕捉患者间和患者内的血流变化。基于计算出的流场，计算并比较了不同血流条件下的17个血流动力学参数。接下来，在1571个动脉瘤中训练动脉瘤破裂的统计模型，包括通过定义血流动力学“响应变量”（模型A）或按患者反复随机选择血流条件（模型B）以及形态学和患者特异性变量来捕捉血流变化的血流动力学参数。在其余249个病例中评估这两种模型。
不同血流条件下所有血流动力学参数均有显著差异（p < 0.001）。与血流无关的“响应”模型A和与血流相关的模型B表现都很好，受试者操作特征曲线下面积分别为0.8182和0.8174±0.0045。
在多变量动脉瘤破裂预测模型中可以考虑患者间和患者内血流变化对计算出的血流动力学的影响，从而获得良好的预测性能。这样的模型可以应用于独立于特定流入边界条件的CFD数据。
