A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack.

Neurol Neuroimmunol Neuroinflamm

From the Department of Neurology (Y.X., L.Y., J.S., Y.Y., Y.W., H.C., H.W., T.S., F.-D.S., X.Z., D.-C.T.), China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University; Department of Neurology (H.M.), First Hospital of Shanxi Medical University, Taiyuan; Department of Neurology (M.F., C.-S.Y., F.-D.S.), Tianjin Neurological Institute, Tianjin Medical University General Hospital; and Department of Neurology (J.F.), The First Affiliated Hospital of Chongqing Medical University, China.

Published: November 2024

AI Article Synopsis

  • The study aimed to identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and create a risk score to predict relapse.
  • Researchers followed 188 patients from the China National Registry of Neuro-Inflammatory Diseases (CNRID) and found key factors such as female sex and age at onset that influence relapse risk.
  • The developed MOG-AR score effectively predicts relapse risk, indicating that a score between 13-16 significantly correlates with a higher risk of relapse, suggesting the potential for better patient management and treatment strategies.

Article 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: A total of 188 patients (comprising 612 treatment episodes) were included in cohorts. Female (HR: 0.687, 95% CI 0.524-0.899, = 0.006), onset age 45 years or older (HR: 1.621, 95% CI 1.242-2.116, < 0.001), immunosuppressive therapy (HR: 0.338, 95% CI 0.239-0.479, < 0.001), oral corticosteroids >3 months (HR 0.449, 95% CI 0.326-0.620, < 0.001), and onset phenotype ( < 0.001) were identified as factors associated with MOGAD relapse. A predictive score, termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype), derived in prediction model, demonstrated strong predictive ability for MOGAD relapse. MOG-AR score of 13-16 indicates a higher risk of relapse (HR: 3.285, 95% CI 1.473-7.327, = 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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385954PMC
http://dx.doi.org/10.1212/NXI.0000000000200309DOI Listing

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