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.

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).

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).

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.

Moyamoya 是一种进行性动脉病，由于颅内颈内动脉及其近端分支的狭窄，使患者易患中风。尽管这种情况会导致发病率，但准确预测个体患者预后的能力仍然具有挑战性。本研究的目的是开发一种基于术前脑 MRI 实质结果的系统评分方法，以预测接受手术治疗的儿童烟雾病患者的长期预后。

研究了来自单一中心的患有烟雾病的儿科患者（初次手术时年龄≤18 岁）的回顾性手术队列。选择与烟雾病或其他血管疾病的结果之间存在相关性的放射学变量，根据容易定义的实质发现对术前 MRI 进行评分，这些发现可以快速评估并用于进行数字评分。使用 Pearson 相关系数和受试者工作特征曲线下面积 (AUROC) 将计算出的分数与临床结果测量相关联。

该研究共纳入了 35 名患有烟雾病或烟雾病综合征的儿童，从手术时间算起的中位随访时间为 2.6 年。儿科烟雾病 MRI 评分 (PMMS) 包括缺血性改变（0-2；0 = 无，1 = 局灶性，2 = 弥漫性）、脑软化（0-2；0 = 无，1 = 局灶性，2 = 弥漫性）和出血（0-1；0 = 不存在，1 = 存在）。PMMS 与最后一次随访时的儿科改良 Rankin 量表得分高度相关（r = 0.7，95% CI 0.44–0.84；p < 0.001）作为六分制，并且当进行二分时（AUROC = 0.85）。

PMMS 被发现是一种基于术前 MRI 数据的简单工具，可以快速轻松地计算并与残疾相关联。这种评分方法可能有助于未来发展烟雾病和烟雾病综合征儿童结果的预测模型。