Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York.
Department of Chemical Engineering, Queen's University, Kingston, Canada.
Neurosurgery. 2020 Oct 15;87(5):E557-E564. doi: 10.1093/neuros/nyaa189.
A simple dimensionless aneurysm number ($An$), which depends on geometry and flow pulsatility, was previously shown to distinguish the flow mode in intracranial aneurysms (IA): vortex mode with a dynamic vortex formation/evolution if $An > 1$, and cavity mode with a steady shear layer if $An < 1$.
To hypothesize that $An\ > \ 1$ can distinguish rupture status because vortex mode is associated with high oscillatory shear index, which, in turn, is statistically associated with rupture.
The above hypothesis is tested on a retrospective, consecutively collected database of 204 patient-specific IAs. The first 119 cases are assigned to training and the remainder to testing dataset. $An$ is calculated based on the pulsatility index (PI) approximated either from the literature or solving an optimization problem (denoted as$\ \widehat {PI}$). Student's t-test and logistic regression (LR) are used for hypothesis testing and data fitting, respectively.
$An$ can significantly discriminate ruptured and unruptured status with 95% confidence level (P < .0001). $An$ (using PI) and $\widehat {An}$ (using $\widehat {PI}$) significantly predict the ruptured IAs (for training dataset $An!:\ $AUC = 0.85, $\widehat {An}!:\ $AUC = 0.90, and for testing dataset $An!:\ $sensitivity = 94%, specificity = 33%, $\widehat {An}!:\ $sensitivity = 93.1%, specificity = 52.85%).
$An > 1$ predicts ruptured status. Unlike traditional hemodynamic parameters such as wall shear stress and oscillatory shear index, $An$ has a physical threshold of one (does not depend on statistical analysis) and does not require time-consuming flow simulations. Therefore, $An$ is a simple, practical discriminator of IA rupture status.
先前已经证明,一个依赖于几何形状和流动脉动性的简单无维动脉瘤数($An$)可以区分颅内动脉瘤(IA)中的流动模式:如果$An\gt1$,则为涡流模式,具有动态涡流形成/演化;如果$An\lt1$,则为腔室模式,具有稳定的剪切层。
假设$An\gt1$可以区分破裂状态,因为涡流模式与高振荡剪切指数相关,而振荡剪切指数又与破裂具有统计学相关性。
该假设在一个回顾性的、连续收集的 204 个患者特定的 IA 数据库上进行了测试。前 119 个病例被分配到训练数据集,其余病例被分配到测试数据集。根据脉动指数(PI)计算$An$,PI 可以从文献中近似得到,也可以通过求解优化问题(表示为$\ \widehat {PI}$)得到。学生 t 检验和逻辑回归(LR)分别用于假设检验和数据拟合。
$An$可以以 95%的置信水平(P <.0001)显著区分破裂和未破裂状态。$An$(使用 PI)和$\widehat {An}$(使用$\widehat {PI}$)显著预测破裂的 IA(对于训练数据集$An!:\ $AUC = 0.85,$\widehat {An}!:\ $AUC = 0.90,对于测试数据集$An!:\ $敏感性 = 94%,特异性 = 33%,$\widehat {An}!:\ $敏感性 = 93.1%,特异性 = 52.85%)。
$An\gt1$预测破裂状态。与传统的血流动力学参数如壁面剪切应力和振荡剪切指数不同,$An$具有一个物理阈值(不依赖于统计分析),并且不需要耗时的流动模拟。因此,$An$是一种简单实用的 IA 破裂状态的判别器。