Abstract TMP89: Predictive Model of Ischemic Event Recurrence in Pediatric Moyamoya

Background: Moyamoya is a progressive cerebral arteriopathy and one of the leading causes of stroke recurrence in children. Clinical severity scores have been developed for adult patients given the multifaceted pathophysiology of moyamoya. However, these scores were not designed to assess the risk of ischemic event recurrence, and they have not been validated in children. Objective: To develop a predictive model of ischemic event recurrence adapted to pediatric moyamoya. Methods: We retrospectively reviewed a single-center cohort of moyamoya patients, extracting demographic, clinical, and radiographic data. A binary logit mixed-effects regression was applied to predict ischemic event recurrence or disease progression based on this data. Variables were selected based on current literature. The model was validated using three approaches: validation, leave-one-out cross-validation (LOOCV), and 10-fold cross-validation, assessing performance via area under the curve (AUC), prediction accuracy, and binary classification entropy (BCE). Results: Two-hundred and twenty hemispheres were analyzed. Sixty-six hemispheres showed evidence of ischemic event recurrence or disease progression over a mean follow-up period of 6.23 ± 4.19 years. The median time to event was 21 months. Age at presentation (β=-0.036; p=0.48), Asian ancestry (β=1.74; p=0.002), asymptomatic presentation (β=1.084; p=0.207)), ischemic symptoms at presentation (β=2.15; p=0.011), evidence of infarction on initial brain MRI (β=-0.79; p=0.155), presence of an ivy sign (β=-0.2; p=0.67), unilateral disease (β=-0.97; p=0.077) with an intercept at β=-2.02 (p=0.052) were the variables included in the model. Computed model included LOOCV and 10-fold produced highest prediction accuracy (0.77 and 0.773 respectively), whereas 10-fold cross-validation had the smallest BCE value along with AUC=0.736. Conclusions: We present a pediatric-specific predictive model for the recurrence of ischemic events in moyamoya patients, demonstrating good accuracy. Our performance metrics indicate that 10-fold cross-validation is the preferred method for predicting moyamoya recurrence. This model provides a framework for developing a scoring scale that can be used in clinical practice to quantify risk and guide treatment decisions.