Predictors of Quality of Life in Myasthenia Gravis Patients from Southwestern China: Validation of Clinical and Socioenvironmental Determinants.

Abstract

Introduction: Myasthenia gravis (MG) presents a substantial clinical burden, characterized by increased incidence of myasthenic crises, heterogeneity in treatment response, significant functional impairment, and gradually increasing mortality rates with marked geographical heterogeneity across China. While improving quality of life (QOL) is the focus of MG management, multifactorial determinants of QOL impairment remain unclear, especially in socioeconomically underrepresented regions, particularly Southwestern China. This study aimed to explore myasthenia-specific risk factors for QOL and develop a parsimonious prediction model.

Methods: This study performed univariate and multivariate regression analyses on 310 MG patients diagnosed at the First Affiliated Hospital of Chongqing Medical University between January 2022 and February 2025 from Southwestern China. The QOL of patients was evaluated with the 15-item Myasthenia Gravis Quality of Life (MG-QOL15). Disease severity was evaluated with current Myasthenia Gravis Foundation of America (MGFA) classification, MG-related activity of daily living (MG-ADL) score and quantitative myasthenia gravis (QMG) score. Relevant clinical and demographic data were included in the analysis.

Results: In the analysis of basic characteristics, higher ADL (p < 0.001), worse MGFA classification (p < 0.001), lower education level (p = 0.006), thymic abnormalities (p = 0.004), and treatment (p = 0.003) were significantly correlated with poor QOL. However, factors such as age of onset, gender, and antibody status showed no significant impact. The multivariate models (Model 1-6) further confirmed that MG-ADL (OR = 8.397), QMG score (OR = 4.357), MGFA classification, and thymus histology (thymic hyperplasia OR = 4.505, thymoma OR = 2.472) were independent risk factors for QOL. Corticosteroids combined with immunotherapy were found to significantly improve QOL compared to monotherapy. Model validation indicated that Model 5, which incorporates MG-ADL, MGFA classification, thymus histology, and education level, had the optimal overall performance (area under the curve = 0.835, specificity 0.917), balancing predictive accuracy and clinical applicability.

Conclusion: By identifying key predictors, including clinical severity, thymic abnormalities, and education level, this study developed a multidimensional prediction model for QOL in MG patients.