Pediatric moyamoya MRI score: an imaging-based scale to predict outcomes in surgically treated pediatric patients with moyamoya.

ABSTRACT

OBJECTIVE: Moyamoya is a progressive arteriopathy that predisposes patients to stroke due to stenosis of the intracranial internal carotid arteries and their proximal branches. Despite the morbidity caused by this condition, the ability to accurately predict prognosis for individual patients remains challenging. The goal of this study was to develop a systematic scoring method based on parenchymal findings on preoperative brain MRI to predict long-term outcomes for surgically treated pediatric patients with moyamoya. METHODS: A retrospective surgical cohort of pediatric patients (≤ 18 years of age at the time of the initial surgery) with moyamoya from a single center were studied. Radiological variables with existing correlations between outcomes in moyamoya or other vascular diseases were chosen to score preoperative MRI based on easily defined parenchymal findings that could be rapidly assessed and used to make a numeric score. Calculated scores were correlated with clinical outcome measures using the Pearson correlation coefficient and area under the receiver operating characteristic curve (AUROC). RESULTS: A total of 35 children with moyamoya disease or moyamoya syndrome were included in the study, with a median follow-up time of 2.6 years from the time of surgery. The pediatric moyamoya MRI score (PMMS) consists of ischemic changes (0-2; 0 = none, 1 = focal, 2 = diffuse), encephalomalacia (0-2; 0 = none, 1 = focal, 2 = diffuse), and hemorrhage (0-1; 0 = not present, 1 = present). PMMSs were highly correlated with pediatric modified Rankin Scale scores at the last follow-up (r = 0.7, 95% CI 0.44-0.84; p < 0.001) as a six-point scale, and when dichotomized (AUROC = 0.85). CONCLUSIONS: The PMMS was found to be a simple tool based on preoperative MRI data that could be quickly and easily calculated and correlated with disability. This scoring method may aid future development of predictive models of outcomes for children with moyamoya disease and moyamoya syndrome.