A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack
Yun Xu, Huaxing Meng, Moli Fan, Linlin Yin, Jiali Sun, Yajun Yao, Yuzhen Wei, Hengri Cong, Huabing Wang, Tian Song, Chun-Sheng Yang, Jinzhou Feng, Fu‐Dong Shi, Xinghu Zhang, De‐Cai Tian
Abstract
BACKGROUND AND OBJECTIVES: To identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to develop and validate a simple risk score for predicting relapse. METHODS: In China National Registry of Neuro-Inflammatory Diseases (CNRID), we identified patients with MOGAD from March 2023 and followed up prospectively to September 2023. The primary endpoint was MOGAD relapse, confirmed by an independent panel. Patients were randomly divided into model development (75%) and internal validation (25%) cohorts. Prediction models were constructed and internally validated using Andersen-Gill models. Nomogram and relapse risk score were generated based on the final prediction models. RESULTS: = 0.004). DISCUSSION: The risk of MOGAD relapse seems to be predictable. Further validation of MOG-AR score developed from this cohort to determine appropriate treatment and monitoring frequency is warranted. TRIAL REGISTRATION INFORMATION: CNRID, NCT05154370, registered December 13, 2021, first enrolled December 15, 2021.