Tang Xiaoyu, Zhou Longjiang, Wen Lili, Wu Qi, Leng Xiaochang, Xiang Jianping, Zhang Xin
Department of Neurosurgery, Jinling School of Clinical Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Department of Neurosurgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Front Neurol. 2022 Jan 20;12:811281. doi: 10.3389/fneur.2021.811281. eCollection 2021.
The objective of this study was to identify the morphological and hemodynamic factors associated with the rupture of multiple intracranial aneurysms regardless of patient-related factors and establish a statistical model for aneurysm rupture risk assessment.
The digital subtraction angiography (DSA) data of 104 mirror intracranial aneurysms in 52 consecutive patients were retrospectively analyzed in this study. 21 morphological parameters and hemodynamic parameters were calculated by 3-dimensional reconstruction and computational fluid dynamics (CFD) simulation. Significant differences ( < 0.05) between the two groups were subsequently tested with the multivariate logistic regression to identify the independent risk factors. A prediction model was established based on the independent risk factors. The receiver operating characteristics (ROCs) were generated to estimate the prediction performance. A cohort of patients with multiple intracranial aneurysms admitted in our institute from January 2021 to October 2021 was introduced to verify the value of the model.
Significant differences between the ruptured and unruptured aneurysms were found in 15 out of 19 parameters. Bleb formation, neck width, and size ratio were independent factors in the multivariate logistic regression. A prediction model based on the three independent risk factors was established: Odds = -1.495 - 0.707 × + 3.061 × + 2.1 × (bleb formation: Yes = 1, No = 0). The area under the curve (AUC) value of the model was 0.901. In the validation cohort, the prediction model showed satisfying performance in assessing multiple aneurysm rupture risk with a sensitivity of 100% and specificity of 88.46%.
Bleb formation, neck width, and size ratio were independently associated with aneurysm rupture status. The prediction model may help in identifying the aneurysm with high rupture risk.
本研究的目的是确定与多发性颅内动脉瘤破裂相关的形态学和血流动力学因素,而不考虑患者相关因素,并建立一个用于评估动脉瘤破裂风险的统计模型。
本研究回顾性分析了52例连续患者的104个镜像颅内动脉瘤的数字减影血管造影(DSA)数据。通过三维重建和计算流体动力学(CFD)模拟计算了21个形态学参数和血流动力学参数。随后用多因素逻辑回归检验两组之间的显著差异(<0.05),以确定独立危险因素。基于独立危险因素建立了预测模型。生成受试者工作特征(ROC)曲线以评估预测性能。引入了一组2021年1月至2021年10月在我院收治的多发性颅内动脉瘤患者,以验证该模型的价值。
19个参数中的15个在破裂和未破裂动脉瘤之间存在显著差异。在多因素逻辑回归中,瘤泡形成、瘤颈宽度和大小比是独立因素。建立了基于这三个独立危险因素的预测模型:Odds = -1.495 - 0.707× + 3.061× + 2.1×(瘤泡形成:是=1,否=0)。该模型的曲线下面积(AUC)值为0.901。在验证队列中,该预测模型在评估多发性动脉瘤破裂风险方面表现出令人满意的性能,敏感性为100%,特异性为88.46%。
瘤泡形成、瘤颈宽度和大小比与动脉瘤破裂状态独立相关。该预测模型可能有助于识别具有高破裂风险的动脉瘤。