Xiang Jianping, Yu Jihnhee, Snyder Kenneth V, Levy Elad I, Siddiqui Adnan H, Meng Hui
Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA.
Department of Biostatistics, University at Buffalo, State University of New York, Buffalo, New York, USA.
J Neurointerv Surg. 2016 Jan;8(1):104-10. doi: 10.1136/neurintsurg-2014-011477. Epub 2014 Dec 8.
We previously established three logistic regression models for discriminating intracranial aneurysm rupture status based on morphological and hemodynamic analysis of 119 aneurysms. In this study, we tested if these models would remain stable with increasing sample size, and investigated sample sizes required for various confidence levels (CIs).
We augmented our previous dataset of 119 aneurysms into a new dataset of 204 samples by collecting an additional 85 consecutive aneurysms, on which we performed flow simulation and calculated morphological and hemodynamic parameters, as done previously. We performed univariate significance tests on these parameters, and multivariate logistic regression on significant parameters. The new regression models were compared against the original models. Receiver operating characteristics analysis was applied to compare the performance of regression models. Furthermore, we performed regression analysis based on bootstrapping resampling statistical simulations to explore how many aneurysm cases were required to generate stable models.
Univariate tests of the 204 aneurysms generated an identical list of significant morphological and hemodynamic parameters as previously (from the analysis of 119 cases). Furthermore, multivariate regression analysis produced three parsimonious predictive models that were almost identical to the previous ones, with model coefficients that had narrower CIs than the original ones. Bootstrapping showed that 10%, 5%, 2%, and 1% convergence levels of CI required 120, 200, 500, and 900 aneurysms, respectively.
Our original hemodynamic-morphological rupture prediction models are stable and improve with increasing sample size. Results from resampling statistical simulations provide guidance for designing future large multi-population studies.
我们之前基于对119个动脉瘤的形态学和血流动力学分析建立了三个用于区分颅内动脉瘤破裂状态的逻辑回归模型。在本研究中，我们测试了这些模型在样本量增加时是否仍保持稳定，并研究了不同置信水平（CI）所需的样本量。
我们通过收集另外85个连续的动脉瘤，将之前119个动脉瘤的数据集扩充为一个包含204个样本的新数据集，在新数据集上我们像之前一样进行了血流模拟并计算了形态学和血流动力学参数。我们对这些参数进行了单变量显著性检验，并对显著参数进行了多变量逻辑回归分析。将新的回归模型与原始模型进行比较。应用受试者工作特征分析来比较回归模型的性能。此外，我们基于自助重采样统计模拟进行回归分析，以探索需要多少例动脉瘤病例才能生成稳定的模型。
对204个动脉瘤的单变量检验产生了与之前（对119例病例的分析）相同的显著形态学和血流动力学参数列表。此外，多变量回归分析产生了三个简约的预测模型，这些模型与之前的模型几乎相同，模型系数的置信区间比原始模型更窄。自助法显示，置信区间的收敛水平达到10%、5%、2%和1%分别需要120、200、500和900个动脉瘤。
我们最初的血流动力学 - 形态学破裂预测模型稳定，并且随着样本量的增加而改进。重采样统计模拟的结果为设计未来的大型多人群研究提供了指导。
