Prediction of the generalization of myasthenia gravis with purely ocular symptoms at onset: a multivariable model development and validation. (June 2022) - Li, Feng; Zhang, Hongbin; Tao, Ya; Stascheit, Frauke; Han, Jiaojiao; Gao, Feng; Liu, Hongbo; Carmona-Bayonas, Alberto; Li, Zhongmin; Rueckert, Jens-C.; Meisel, Andreas; Zhao, Song
Background: About half of myasthenia gravis (MG) patients with purely ocular symptoms at onset progress to generalized myasthenia gravis (gMG). Objectives: To develop and validate a model to predict the generalization of MG at 6 months after disease onset in patients with ocular-onset myasthenia gravis (OoMG). Methods: Data of patients with OoMG were retrospectively collected from two tertiary hospitals in Germany and China. An accelerated failure time model was developed using the backward elimination method based on the German cohort to predict the generalization of OoMG. The model was then externally validated in the Chinese cohort, and its performance was assessed using Harrell's C-index and calibration plots. Results: Four hundred and seventy-seven patients (275 from Germany and 202 from China) were eligible for inclusion. One hundred and three (37.5%) patients in the German cohort progressed from OoMG to gMG with a median follow-up time of 69 (32–116) months. The median time to generalization was 29 (16–71) months. The estimated cumulative probability of generalization was 30.5% [95% CI (confidence interval), 24.3–36.2%) at 5 years after disease onset. The final model, which was represented as a nomogram, included five clinical variables: sex, titer of anti-AChR antibody, status of anti-MuSK antibody, age at disease onset and the presence of other autoimmune disease. External validation of the model using the bootstrap showed a C-index of 0.670 (95% CI, 0.602–0.738). Calibration curves revealed moderate agreement of predicted and observed outcomes. Conclusion: The nomogram is a good predictor for generalization in patients with OoMG that can be used to inform of the individual generalization risk, which might improve the clinical decision-making.