Development and Validation of a Nomogram for Predicting Generalization in Patients With Ocular Myasthenia Gravis

Area of Science:

• Neurology
• Clinical Prediction Models

Background:

• Ocular myasthenia gravis (OMG) is a subtype of myasthenia gravis affecting ocular muscles.
• Predicting disease generalization is crucial for timely treatment and management.

Purpose of the Study:

• To develop and validate a nomogram for predicting 1- and 2-year generalization probabilities in OMG patients.
• To stratify OMG patients into distinct risk groups for disease generalization.

Main Methods:

• A cohort of 501 OMG patients was analyzed.
• A stepwise Cox regression model was used to construct the nomogram.
• Nomogram performance was assessed using C-indexes, calibration curves, Kaplan-Meier analysis, and decision curve analysis (DCA).

Main Results:

• The final nomogram included five variables: sex, onset age, repetitive nerve stimulation findings, acetylcholine receptor antibody test results, and thymic status.
• The nomogram demonstrated good discrimination (C-indexes 0.733-0.788) and calibration.
• High-risk groups identified by the nomogram showed higher 2-year cumulative generalization rates.

Conclusions:

• The developed nomogram effectively predicts 1- and 2-year generalization probabilities in OMG.
• The nomogram can aid neurologists in treatment decisions and patient selection for clinical trials.