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Objective: Myasthenia gravis (MG), a low-prevalence autoimmune disorder characterized by clinical heterogeneity and unpredictable disease fluctuations, presents significant risks of acute exacerbations requiring intensive care. These crises contribute substantially to patient morbidity and mortality. This study aimed to develop and validate machine-learning models for predicting intensive care unit (ICU) admission risk among patients with MG-related disease instability. Methods: In this retrospective analysis of 314 MG patients hospitalized between 2015 and 2018, we implemented four machine learning algorithms, including logistic regression, support vector machine, extreme gradient boosting (XGBoost), and random forest, to predict ICU admission risk. The models incorporated fourteen clinical parameters as predictive features. The SHapley Additive exPlanations method was utilized to assess the importance of factors associated with ICU admission. Results: Through 10-fold cross-validation, the XGBoost model demonstrated superior predictive performance (area under the receiver operating characteristic curve: 0.8943, accuracy: 0.8603, sensitivity: 0.7222, and specificity: 0.9125). Among the analyzed features, MG severity, as classified by the Myasthenia Gravis Foundation of America clinical classification, was identified as the most significant factor influencing ICU admission. Additionally, disease duration, a key continuous variable, was inversely correlated with the risk of ICU admission. Conclusion: MG severity is the primary determinant of ICU admission, with shorter disease duration increasing the risk, possibly due to greater susceptibility to exacerbations. The XGBoost model exhibited excellent performance and accuracy, effectively identifying critical clinical factors for predicting ICU admission risk in MG patients. This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making, optimize resource allocation, and ultimately improve patient outcomes.

Under the time-based criteria, patients with unknown onset stroke (UOS) are ineligible for reperfusion therapies. However, previous studies suggest that some patients with UOS may benefit from reperfusion. Several imaging modalities have been suggested to select patients for intervention, but the optimal imaging criteria are still controversial. Herein we present a series of four cases using 10-point CT-ASPECTS to support our decision of reperfusion therapy. We decided based on history, symptoms, and the 10-point CT-ASPECTS alone. Each patient's history suggested that the stroke just took place. All four patients had apparent clinical symptoms, with 10-point CT-ASPECTS. All of them had a reduction in their NIHSS after the reperfusion therapy. 10-point CT-ASPECTS could be used to support the presumption that the stroke just happens in patients with UOS. Further study is warranted to elucidate the value of CT-ASPECTS for UOS patients. Keywords: ASPECTS, Unknown onset stroke, Intravenous thrombolysis, Mechanical thrombectomy, Reperfusion therapy