Prestigiacomo Charles J, He Wenzhuan, Catrambone Jeffrey, Chung Stephanie, Kasper Lydia, Pasupuleti Latha, Mittal Neelesh
Departments of Neurological Surgery, University of Medicine of Dentistry of New Jersey, Newark, New Jersey 07101, USA.
J Neurosurg. 2009 Jan;110(1):1-6. doi: 10.3171/2008.5.17558.
The goal of this study was to establish a biomathematical model to accurately predict the probability of aneurysm rupture. Biomathematical models incorporate various physical and dynamic phenomena that provide insight into why certain aneurysms grow or rupture. Prior studies have demonstrated that regression models may determine which parameters of an aneurysm contribute to rupture. In this study, the authors derived a modified binary logistic regression model and then validated it in a distinct cohort of patients to assess the model's stability.
Patients were examined with CT angiography. Three-dimensional reconstructions were generated and aneurysm height, width, and neck size were obtained in 2 orthogonal planes. Forward stepwise binary logistic regression was performed and then applied to a prospective cohort of 49 aneurysms in 37 patients (not included in the original derivation of the equation) to determine the log-odds of rupture for this aneurysm.
A total of 279 aneurysms (156 ruptured and 123 unruptured) were observed in 217 patients. Four of 6 linear dimensions and the aspect ratio were significantly larger (each with p < 0.01) in ruptured aneurysms than unruptured aneurysms. Calculated volume and aneurysm location were correlated with rupture risk. Binary logistic regression applied to an independent prospective cohort demonstrated the model's stability, showing 83% sensitivity and 80% accuracy.
This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with good accuracy. The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm.
本研究的目的是建立一个生物数学模型，以准确预测动脉瘤破裂的概率。生物数学模型纳入了各种物理和动态现象，有助于深入了解某些动脉瘤为何生长或破裂。先前的研究表明，回归模型可以确定动脉瘤的哪些参数会导致破裂。在本研究中，作者推导了一个改良的二元逻辑回归模型，然后在一个不同的患者队列中对其进行验证，以评估该模型的稳定性。
对患者进行CT血管造影检查。生成三维重建图像，并在两个正交平面上获取动脉瘤的高度、宽度和颈部尺寸。进行向前逐步二元逻辑回归，然后将其应用于37例患者的49个动脉瘤的前瞻性队列（不包括在方程的原始推导中），以确定该动脉瘤破裂的对数优势。
在217例患者中总共观察到279个动脉瘤（156个破裂，123个未破裂）。破裂动脉瘤的6个线性维度中的4个以及纵横比显著大于未破裂动脉瘤（每个p < 0.01）。计算出的体积和动脉瘤位置与破裂风险相关。应用于独立前瞻性队列的二元逻辑回归证明了该模型的稳定性，灵敏度为83%，准确率为80%。
这个动脉瘤破裂的二元逻辑回归模型能够很好地准确识别动脉瘤的状态。该技术的应用及其验证表明，生物形态测量数据及其关系在确定动脉瘤状态方面可能具有重要价值。
