1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University.
2China National Clinical Research Center for Neurological Diseases.
J Neurosurg. 2018 Feb;128(2):530-540. doi: 10.3171/2016.10.JNS161415. Epub 2017 Mar 31.
OBJECTIVE Case selection for the surgical treatment of brain arteriovenous malformations (BAVMs) remains challenging. This study aimed to construct a predictive grading system combining lesion-to-eloquence distance (LED) for selecting patients with BAVMs for surgery. METHODS Between September 2012 and September 2015, the authors retrospectively studied 201 consecutive patients with BAVMs. All patients had undergone preoperative functional MRI and diffusion tensor imaging (DTI), followed by resection. Both angioarchitectural factors and LED were analyzed with respect to the change between preoperative and final postoperative modified Rankin Scale (mRS) scores. LED refers to the distance between the lesion and the nearest eloquent area (eloquent cortex or eloquent fiber tracts) measured on preoperative fMRI and DTI. Based on logistic regression analysis, the authors constructed 3 new grading systems. The HDVL grading system includes the independent predictors of mRS change (hemorrhagic presentation, diffuseness, deep venous drainage, and LED). Full Score combines the variables in the Spetzler-Martin (S-M) grading system (nidus size, eloquence of adjacent brain, and venous drainage) and the HDVL. For the third grading system, the fS-M grading system, the authors added information regarding eloquent fiber tracts to the S-M grading system. The area under the receiver operating characteristic (ROC) curves was compared with those of the S-M grading system and the supplementary S-M grading system of Lawton et al. RESULTS LED was significantly correlated with a change in mRS score (p < 0.001). An LED of 4.95 mm was the cutoff point for the worsened mRS score. Hemorrhagic presentation, diffuseness, deep venous drainage, and LED were independent predictors of a change in mRS score. Predictive accuracy was highest for the HDVL grading system (area under the ROC curve 0.82), followed by the Full Score grading system (0.80), the fS-M grading system (0.79), the supplementary S-M grading system (0.76), and least for the S-M grading system (0.71). Predictive accuracy of the HDVL grading system was significantly better than that of the Spetzler-Martin grade (p = 0.040). CONCLUSIONS LED was a significant predictor for the preoperative risk evaluation for surgery. The HDVL system was a good predictor of neurological outcomes after BAVM surgery. Adding the consideration of the involvement of eloquent fiber tracts to preoperative evaluation can effectively improve its predictive accuracy.
脑动静脉畸形(BAVM)的手术治疗病例选择仍然具有挑战性。本研究旨在构建一种预测分级系统,该系统结合病变与功能区之间的距离(LED),用于选择接受 BAVM 手术的患者。
2012 年 9 月至 2015 年 9 月,作者回顾性研究了 201 例连续 BAVM 患者。所有患者均接受术前功能磁共振成像(fMRI)和弥散张量成像(DTI)检查,然后进行切除。分析血管构筑因素和 LED 与术前和最终术后改良 Rankin 量表(mRS)评分变化之间的关系。LED 是指在术前 fMRI 和 DTI 上测量的病变与最近功能区(功能皮质或功能纤维束)之间的距离。基于逻辑回归分析,作者构建了 3 种新的分级系统。HDVL 分级系统包括 mRS 变化的独立预测因子(出血表现、弥漫性、深静脉引流和 LED)。全分数综合了 Spetzler-Martin(S-M)分级系统(核大小、邻近脑的功能、静脉引流)和 HDVL 中的变量。对于第三个分级系统,即 fS-M 分级系统,作者在 S-M 分级系统中添加了与功能纤维束相关的信息。比较了受试者工作特征(ROC)曲线下面积与 S-M 分级系统和 Lawton 等人的补充 S-M 分级系统的曲线下面积。
LED 与 mRS 评分变化显著相关(p<0.001)。LED 为 4.95mm 时,mRS 评分恶化的截断值。出血表现、弥漫性、深静脉引流和 LED 是 mRS 评分变化的独立预测因子。HDVL 分级系统的预测准确性最高(ROC 曲线下面积 0.82),其次是全分数分级系统(0.80)、fS-M 分级系统(0.79)、补充 S-M 分级系统(0.76),而 S-M 分级系统的预测准确性最低(0.71)。HDVL 分级系统的预测准确性明显优于 Spetzler-Martin 分级(p=0.040)。
LED 是手术前评估风险的重要预测因子。HDVL 系统是 BAVM 手术后神经功能预后的良好预测因子。在术前评估中加入对功能纤维束受累的考虑,可以有效提高其预测准确性。