The characteristics of new-onset myasthenia gravis after COVID-19 outbreak: a cross-sectional study
摘要
Little research has been conducted on new-onset myasthenia gravis (MG) patients following the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 surged in China on December 7th, 2022. This study aimed to explore the clinical characteristics of new-onset MG patients after COVID-19 and analyze factors affecting their disease improvement.
MethodsAll new-onset MG patients before (December 1st, 2021 to December 7th, 2022) and after COVID-19 outbreak (December 8th, 2022 to November 30th, 2023) were included in this study. Data was collected through the electronic medical record system and follow-up. Multivariate logistic regression was used to identify independent predictors of clinical improvement in patients with new-onset MG.
Results359 new-onset MG patients (165 before COVID-19 outbreak and 194 after COVID-19 outbreak) were enrolled in this study. After COVID-19 outbreak, there was an increase in new-onset MG patients, with more cases occurring within the first three months. The rates of pulmonary inflammation (40.28%), COVID-19 vaccination (88.14%), and treatment with tacrolimus (15.98%) and MG duration (15 weeks, IQR: 5.75, 32) were higher, while rates of thymectomy (13.92%), baseline MG-ADL (3, IQR: 3, 6), and QMGS (7, IQR: 5,8) were lower compared to new-onset MG patients before COVID-19 outbreak. Multivariate logistic regression analysis showed that age at onset (OR 0.964, p < 0.001), baseline MG-ADL (OR 1.611, p < 0.001), and ocular MG (OR 0.401, p = 0.041) were independent predictors of clinical improvement in new-onset MG after the COVID-19 outbreak.
ConclusionIn this single-center cross-sectional study, new-onset MG patients following the COVID-19 outbreak showed altered seasonal onset patterns, milder disease severity, and higher OMG onset age. Age at onset is an independently negative predictor of improvement in new-onset MG patients after the COVID-19 outbreak. Whereas baseline MG-ADL is an independently positive predictor.