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

Yun Xu1, Huaxing Meng1, Moli Fan1

  • 1From 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.

Neurology(R) Neuroimmunology & Neuroinflammation
|September 9, 2024
PubMed
Abstract

Insights

Predicting relapse in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is possible. A new MOG-AR score identifies patients at higher risk for MOGAD relapse, aiding treatment decisions.

Area of Science:

  • Neuroimmunology
  • Neurology
  • Clinical Prediction Models

Background:

  • Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a rare autoimmune disorder affecting the central nervous system.
  • Predicting relapse is crucial for managing MOGAD and optimizing patient outcomes.
  • Identifying reliable predictors of relapse can guide therapeutic strategies and monitoring protocols.

Purpose of the Study:

  • To identify predictors of relapse in patients diagnosed with MOGAD.
  • To develop and validate a simple risk score for predicting MOGAD relapse.
  • To inform clinical management and personalize treatment approaches for MOGAD patients.

Main Methods:

  • Prospective cohort study of 188 MOGAD patients from the China National Registry of Neuro-Inflammatory Diseases (CNRID).
  • Patients were randomly assigned to model development (75%) and internal validation (25%) cohorts.
  • Andersen-Gill models were used to construct and validate prediction models, generating a nomogram and relapse risk score (MOG-AR).

Main Results:

  • Key predictors for MOGAD relapse included female sex, age 45 years or older, immunosuppressive therapy, prolonged oral corticosteroid use (>3 months), and onset phenotype.
  • The developed MOG-AR score, incorporating these factors, demonstrated strong predictive ability for MOGAD relapse.
  • A MOG-AR score of 13-16 indicated a significantly higher risk of relapse (HR: 3.285).

Conclusions:

  • MOGAD relapse risk is predictable using clinical and treatment-related factors.
  • The MOG-AR score shows promise for identifying patients at high risk of relapse.
  • Further external validation of the MOG-AR score is warranted to guide treatment and monitoring frequency.

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