Related Experiment Video
Updated: May 20, 2026
Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations
Published on: October 20, 2017
Evaluating performance of the spetzler-martin supplemented model in selecting patients with brain arteriovenous
Helen Kim1, Tony Pourmohamad, Erick M Westbroek
1Center for Cerebrovascular Research, Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, USA.kimhel@anesthesia.ucsf.edu
Background And Purpose:
Our recently proposed point scoring model includes the widely-used Spetzler-Martin (SM)-5 variables, along with age, unruptured presentation, and diffuse border (SM-Supp). Here we evaluate the SM-Supp model performance compared with SM-5, SM-3, and Toronto prediction models using net reclassification index, which quantifies the correct movement in risk reclassification, and validate the model in an independent data set.
Methods:
Bad outcome was defined as worsening between preoperative and final postoperative modified Rankin Scale score. Point scores for each model were used as predictors in logistic regression and predictions evaluated using net reclassification index at varying thresholds (10%-30%) and any threshold (continuous net reclassification index >0). Performance was validated in an independent data set (n=117).
Results:
Net gain in risk reclassification was better using the SM-Supp model over a range of threshold values (net reclassification index=9%-25%) and significantly improved overall predictions for outcomes in the development data set, yielding a continuous net reclassification index of 64% versus SM-5, 67% versus SM-3, and 61% versus Toronto (all P<0.001). In the validation data set, the SM-Supp model again correctly reclassified a greater proportion of patients versus SM-5 (82%), SM-3 (85%), and Toronto models (69%).
Conclusions:
The SM-Supp model demonstrated better discrimination and risk reclassification than several existing models and should be considered for clinical practice to estimate surgical risk in patients with brain arteriovenous malformation.
