A Supplementary Grading Scale for Selecting Patients With Brain Arteriovenous Malformations for Surgery

Neurosurgery 66:702-713, 2010. 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.

OBJECTIVE: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSION: This new AVM grading system supplements rather than replaces the wellestablished 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.

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