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Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

Sarcopenia and obesity can negatively impact quality of life and cause chronic fragility, and are associated with neuromuscular diseases, including myasthenia gravis (MG). The long-term consequences of body composition changes in chronic MG remain unknown; we therefore evaluated changes in body composition, including sarcopenia, obesity, lean body mass, and the prevalence of sarcopenic obesity in patients. In this cross-sectional study, 35 patients with MG (mean age: 56.1 years) and 175 matched controls were enrolled. Body fat mass and skeletal muscle mass were measured using whole body dual-energy X-ray absorptiometry. Patients with MG exhibited a higher prevalence of obesity and higher android adiposity and total body fat percentage than those of controls. Although the prevalence of sarcopenia and sarcopenic obesity did not increase with age, there was a decrease in arm and android muscle mass in patients with MG compared with controls. Lower muscle mass percentages were correlated with increased age and MG severity, but not with corticosteroid use. Thus, MG is associated with increased risk for obesity and decreased muscle mass with aging, regardless of corticosteroid use. Therefore, accurate diagnosis of body composition changes in MG could facilitate the application of appropriate therapies to promote health, improve quality of life, and prevent fragility.