The nomogram model predicts relapse risk in myelin oligodendrocyte glycoprotein antibody-associated disease: a single-center study

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Background Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an autoimmune disorder of the central nervous system, characterized by seropositive MOG antibodies. MOGAD can present with a monophasic or relapsing course, where repeated relapses may lead to a worse prognosis and increased disability. Currently, little is known about the risk factors for predicting MOGAD relapse in a short period, and few established prediction models exist, posing a challenge to timely and personalized clinical diagnosis and treatment. Methods From April 2018 to December 2023, we enrolled 88 patients diagnosed with MOGAD at the First Hospital of Shanxi Medical University and collected basic clinical data. The data were randomly divided into a training cohort (80%) and a validation cohort (20%). Univariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent risk factors for 1-year relapse. A prediction model was constructed, and a nomogram was developed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate and internally validate model performance. Results Among 88 MOGAD patients, 29 relapsed within 1 year of onset (33%). A total of 4 independent risk factors for predicting relapse were identified: female sex (P=0.040), cortical encephalitis phenotype (P=0.032), serum MOG antibody titer ≥1:32 (P=0.007), and immunosuppressive therapy after the first onset (P= 0.045). The area under curve (AUC) value of the nomogram prediction model constructed with these four factors was 0.866 in the training cohort, and 0.864 in the validation cohort. The cutoff value of the total nomogram score was 140 points, distinguishing the low relapse risk group from the high relapse risk group (P < 0.001). The calibration curve demonstrated high consistency in prediction, and the DCA showed excellent net benefit in the prediction model. Tested by ROC curve, calibration curve, and DCA, the nomogram model also demonstrates significant value in predicting MOGAD relapse within 2 years. Conclusion The nomogram model we developed can help accurately predict the relapse risk of MOGAD patients within one year of onset and assist clinicians in making treatment decisions to reduce the chance of relapse.

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