The current grading system for moyamoya disease (MMD) is focused on angiographic studies with limited clinical application. The authors aimed to determine relevant factors that may impact postoperative outcome and establish a scoring system to predict the functional outcome.
Adult patients with MMD who underwent treatment between 1998 and 2016 were included. Factors such as age, sex, comorbidity, smoking, MMD family history, initial presentation, multimodal imaging modalities, and types of surgical revascularization were thoroughly reviewed. These factors were analyzed to determine possible risk factors related to unfavorable 6-month postoperative outcomes using the modified Rankin Scale (mRS) (unfavorable: mRS score ≥ 3). A scoring system was developed using these independent risk factors to predict the outcome and validated using prospectively collected data from multiple centers between 2017 and 2018.
Of 302 patients for whom applications were submitted, 260 patients (321 hemispheres) met the diagnostic criteria. In multivariate analysis, hyperlipidemia, smoking, cerebral infarction on preoperative CT or MRI, and moderately to severely reduced regional cerebrovascular reserve results from Diamox SPECT were significantly related to unfavorable outcome. The authors developed a scoring system and stratified patients into risk groups according to their scores: low-risk (score 0–3), intermediate-risk (score 4–6), and high-risk (score 7–9) groups. This model demonstrated both good discrimination and calibration using C-statistics and the Hosmer-Lemeshow goodness-of-fit test showing 0.812 (95% CI 0.743–0.881) (p = 0.568) for the development and 0.954 (95% CI 0.896–1) (p = 0.097) for the temporal and external validation cohort.
The authors’ scoring system is readily adoptable to predict the postoperative outcome for MMD. Their data revealed the importance of smoking and hyperlipidemia, which were the only modifiable factors included in the scoring system. The authors validated their scoring system both internally and externally and maintained good performance, highlighting the system’s generalizability and reliability.