Novel Models for Identification of the Ruptured Aneurysm in Patients with Subarachnoid Hemorrhage with Multiple Aneurysms
Novel Models for Identification of the Ruptured Aneurysm in Patients with Subarachnoid Hemorrhage with Multiple Aneurysms
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DOI：
10.3174/ajnr.a6259
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发表时间：
2019-11-01
影响因子：
3.5
通讯作者：
Meng, H.
中科院分区：
文献类型：
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作者：
Rajabzadeh-Oghaz, H.;Wang, J.;Meng, H.
背景和目的：在SAH合并多个颅内动脉瘤的患者中，出血模式往往不能提示破裂的来源。血管造影结果(颅内动脉瘤的大小和形状)可能会有所帮助，但可能不可靠。我们的目的是测试现有的参数是否可以识别多发性颅内动脉瘤患者破裂的颅内动脉瘤，以及复合预测模型是否可以提高识别能力。材料和方法：回顾分析93例SAH患者合并至少2个颅内动脉瘤的血管造影和临床资料，其中囊性动脉瘤206个，破裂93个，均经手术或明确出血模式证实。我们计算了93例患者的13个形态参数和10个血流动力学参数以及位置和类型(侧壁/分叉)，并测试了它们识别破裂的能力。为了建立预测模型，我们随机分配了70名患者进行培训，23名患者进行坚持测试。使用具有定制成本函数和10倍交叉验证的线性回归模型，我们训练了2个破裂识别模型：使用所有参数的RIMC和不包括血流动力学的RIMM。结果：25个研究参数对破裂的阳性预测值差异很大(31%？87%)，最高的是大小比为87%。RIMC包括粒度比、波动指数、相对停留时间和类型；RIMM只有粒度比、波动指数和类型。在交叉验证中，大小比、RIMM和RIMC的阳性预测值分别为86%？4%、90%？4%和93%？4%。在试验中，大小比例和RIMM的阳性预测值为85%，而RIMC的阳性预测值为92%。结论：大小比是识别破裂动脉瘤的最佳个体因素；然而，RIMC，其次是RIMM，表现优于现有参数。
BACKGROUND AND PURPOSE: In patients with SAH with multiple intracranial aneurysms, often the hemorrhage pattern does not indicate the rupture source. Angiographic findings (intracranial aneurysm size and shape) could help but may not be reliable. Our purpose was to test whether existing parameters could identify the ruptured intracranial aneurysm in patients with multiple intracranial aneurysms and whether composite predictive models could improve the identification. MATERIALS AND METHODS: We retrospectively collected angiographic and medical records of 93 patients with SAH with at least 2 intracranial aneurysms (total of 206 saccular intracranial aneurysms, 93 ruptured), in which the ruptured intracranial aneurysm was confirmed through surgery or definitive hemorrhage patterns. We calculated 13 morphologic and 10 hemodynamic parameters along with location and type (sidewall/bifurcation) and tested their ability to identify rupture in the 93 patients. To build predictive models, we randomly assigned 70 patients to training and 23 to holdout testing cohorts. Using a linear regression model with a customized cost function and 10-fold cross-validation, we trained 2 rupture identification models: RIMC using all parameters and RIMM excluding hemodynamics. RESULTS: The 25 study parameters had vastly different positive predictive values (31%?87%) for identifying rupture, the highest being size ratio at 87%. RIMC incorporated size ratio, undulation index, relative residence time, and type; RIMM had only size ratio, undulation index, and type. During cross-validation, positive predictive values for size ratio, RIMM, and RIMC were 86% ? 4%, 90% ? 4%, and 93% ? 4%, respectively. In testing, size ratio and RIMM had positive predictive values of 85%, while RIMC had 92%. CONCLUSIONS: Size ratio was the best individual factor for identifying the ruptured aneurysm; however, RIMC, followed by RIMM, outperformed existing parameters.