A Tractography-Based Grading Scale of Brain Arteriovenous Malformations Close to the Corticospinal Tract to Predict Motor Outcome After Surgery - Li, Maogui; Jiang, Pengjun; Guo, Rui; Liu, Qingyuan; Yang, Shuzhe; Wu, Jun; Cao, Yong; Wang, Shuo (Frontiers in Neurology, 2019)
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 ninety patients with AVMs within 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 = .001), CCL (P = .017), patient age (P = .004), and AVM diffuseness (P = .021) were independent predictors for poor motor outcome. We constructed the CLAD grading scale incorporating these predictors. The predictive acccuracy of the CLAD grade was better, compared with the supplemented Spetzler-Martin grade (area under curve = 0.84 vs. 0.68, P = .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 Tials: ClinicalTrials.gov, No. NCT01758211 and NCT02868008.