首页> 美国卫生研究院文献>Frontiers in Neurology >A Tractography-Based Grading Scale of Brain Arteriovenous Malformations Close to the Corticospinal Tract to Predict Motor Outcome After Surgery
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A Tractography-Based Grading Scale of Brain Arteriovenous Malformations Close to the Corticospinal Tract to Predict Motor Outcome After Surgery

机译:基于动静脉畸形的脑动静脉畸形分级量表靠近皮质脊髓束可预测手术后的运动结果

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摘要

>Background: Surgical decision-making for brain arteriovenous malformations (AVMs) close to the corticospinal tract (CST) is always challenging. The purpose of this study was to develop a tractography-based grading scale to improve preoperative risk prediction and patient selection.>Methods: We analyzed a consecutive, surgically treated series of 90 patients with AVMs within a 10-mm range from the CST demonstrated by preoperative diffusion tensor tractography. Poor motor outcome was defined as persistent postoperative limb weakness. We examined the predictive ability of nidus-to-CST distance (NCD), the closest CST level (CCL), deep perforating artery supply, as well as variables of the supplemented Spetzler-Martin grading system. Three logistic models were derived from different multivariable logistic regression analyses, of which the most predictive model was selected to construct a prediction grading scale. Receiver operating characteristic analysis was conducted to test the predictive accuracy of the grading scale.>Results: Twenty-one (23.3%) patients experienced persistent postoperative limb weakness after a mean 2.7-year follow-up. The most predictive logistic model showed NCD (P = 0.001), CCL (P = 0.017), patient age (P = 0.004), and AVM diffuseness (P = 0.021) were independent predictors for poor motor outcome. We constructed the CLAD grading scale incorporating these predictors. The predictive accuracy of the CLAD grade was better compared with the supplemented Spetzler-Martin grade (area under curve = 0.84 vs. 0.68, P = 0.023).>Conclusions: Both NCD and CCL predict motor outcome after resection of AVMs close to the CST. We propose the CLAD grading scale as an effective risk-prediction tool in surgical decision-making.>Clinical Trial Registration: , identifier: and
机译:>背景:靠近皮质脊髓束(CST)的脑动静脉畸形(AVM)的外科手术决策始终具有挑战性。这项研究的目的是建立一种基于影像学的分级量表,以改善术前风险预测和患者选择。>方法:我们分析了连续手术治疗的一系列90例10毫米范围内的AVM患者范围从术前弥散张量图像检查所证实的CST。运动结果差被定义为持续的术后肢体无力。我们检查了nidus到CST距离(NCD),最接近的CST水平(CCL),深层穿支动脉供血以及补充的Spetzler-Martin分级系统的变量的预测能力。从不同的多变量logistic回归分析中得出了三个逻辑模型,其中选择了最具预测性的模型来构建预测等级量表。进行接收者操作特征分析以检验分级量表的预测准确性。>结果: 21名患者(23.3%)在平均2.7年的随访后经历了持续的术后肢体无力。预测性最强的逻辑模型显示NCD(P = 0.001),CCL(P = 0.017),患者年龄(P = 0.004)和AVM弥散性(P = 0.021)是不良运动预后的独立预测因子。我们构建了包含这些预测因子的CLAD分级量表。与补充的Spetzler-Martin等级相比,CLAD等级的预测准确性更好(曲线下面积= 0.84 vs. 0.68,P = 0.023)。>结论: NCD和CCL均预测切除后的运动结局接近CST的AVM。我们建议CLAD分级量表作为外科手术决策中的有效风险预测工具。>临床试验注册:,标识符:和

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