Department of Neurological Surgery, and Center for Cerebrovascular Research, University of California, San Francisco, San Francisco, California, USA.
Neurosurgery. 2010 Apr;66(4):702-13; discussion 713. doi: 10.1227/01.NEU.0000367555.16733.E1.
Patient age, hemorrhagic presentation, nidal diffuseness, and deep perforating artery supply are important factors when selecting patients with brain arteriovenous malformations (AVMs) for surgery.
We hypothesized that these factors outside of the Spetzler-Martin grading system could be combined into a simple, supplementary grading system that would accurately predict neurologic outcome and refine patient selection.
A consecutive, single-surgeon series of 300 patients with AVMs treated microsurgically was analyzed in terms of change between preoperative and final postoperative modified Rankin Scale scores. Three different multivariable logistic models (full, Spetzler-Martin, and supplementary models) were constructed to test the association of combined predictor variables with the change in modified Rankin Scale score. A simplified supplementary grading system was developed from the data with points assigned according to each variable and added together for a supplementary AVM grade.
Predictive accuracy was highest for the full multivariable model (receiver operating characteristic curve area, 0.78), followed by the supplementary model (0.73), and least for the Spetzler-Martin model (0.66). Predictive accuracy of the simplified supplementary grade was significantly better than that of the Spetzler-Martin grade (P = .042), with receiver operating characteristic curve areas of 0.73 and 0.65, respectively.
This new AVM grading system supplements rather than replaces the well-established Spetzler-Martin grading system and is a better predictor of neurologic outcomes after AVM surgery. The supplementary grading scale has high predictive accuracy on its own and stratifies surgical risk more evenly. The supplementary grading system is easily applicable at the bedside, where it is intended to improve preoperative risk prediction and patient selection for surgery.
在选择脑动静脉畸形（AVM）患者进行手术时，患者年龄、出血表现、病灶弥漫程度和深部穿通动脉供应是重要的考虑因素。
我们假设这些斯佩茨勒-马丁（Spetzler-Martin）分级系统之外的因素可以组合成一个简单的补充分级系统，该系统可以准确预测神经功能预后并完善患者选择。
对 300 例接受显微手术治疗的 AVM 患者的连续单外科医生系列进行分析，根据术前和最终术后改良 Rankin 量表评分的变化来评估。构建了三个不同的多变量逻辑模型（全模型、斯佩茨勒-马丁模型和补充模型），以测试联合预测变量与改良 Rankin 量表评分变化的相关性。根据每个变量赋值并相加得到一个补充 AVM 分级，从数据中开发了一个简化的补充分级系统。
全变量多模型的预测准确性最高（接受者操作特征曲线面积为 0.78），其次是补充模型（0.73），而斯佩茨勒-马丁模型的预测准确性最低（0.66）。简化补充分级的预测准确性明显优于斯佩茨勒-马丁分级（P=0.042），其接受者操作特征曲线面积分别为 0.73 和 0.65。
这种新的 AVM 分级系统补充而不是替代了成熟的斯佩茨勒-马丁分级系统，是 AVM 手术后神经功能预后的更好预测指标。补充分级系统本身具有很高的预测准确性，并且更均匀地分层手术风险。补充分级系统易于在床边应用，旨在提高手术前的风险预测和患者选择。
