TRUE-MOGAD Score: A Novel Scoring System to Identify MOGAD Among Positive MOG-IgG Test Results

Area of Science:

• Neuroimmunology
• Clinical Neurology
• Diagnostic Biomarkers

Background:

• Myelin oligodendrocyte glycoprotein (MOG) antibodies (MOG-IgG) are key biomarkers for MOG antibody-associated disease (MOGAD).
• A significant challenge in MOGAD diagnosis is the common occurrence of false-positive MOG-IgG test results.
• Accurate differentiation between true MOGAD and false positives is crucial for appropriate patient management.

Purpose of the Study:

• To develop and validate a practical scoring tool, the TRUE-MOGAD score, to improve the diagnostic accuracy of MOGAD in MOG-IgG positive individuals.
• To guide the interpretation of MOG-IgG test results and assist clinicians in diagnostic and therapeutic decision-making.

Main Methods:

• An observational study was conducted on MOG-IgG positive patients at Mayo Clinic (May 2022-December 2024).
• Predictive variables for MOGAD were identified using logistic regression, and a scoring system was derived.
• The TRUE-MOGAD score was evaluated using receiver operating characteristic analysis and validated internally and externally.

Main Results:

• Out of 215 MOG-IgG positive patients, 144 met the 2023-MOGAD diagnostic criteria (positive predictive value of 67.0%).
• The TRUE-MOGAD score incorporates factors like MOG-IgG titers, clinical phenotype (relapsing optic neuritis, encephalitis), age, MRI findings, CSF oligoclonal bands, and onset pattern.
• The score demonstrated high diagnostic accuracy with an area under the curve (AUC) of 0.983, validated internally (AUC = 0.976) and externally (AUC = 0.977).

Conclusions:

• The TRUE-MOGAD score is a validated and practical tool for accurately identifying MOGAD in patients with positive MOG-IgG results.
• This scoring system can significantly aid clinicians in making informed diagnostic and therapeutic decisions for suspected MOGAD cases.
• The study provides Class II evidence supporting the use of the TRUE-MOGAD score for MOGAD prediction in relevant patient populations.