Predicting ICU Admission in Patients with Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy.

Introduction: Autoimmune glial fibrillary acidic protein astrocytopathy (A-GFAP-A) is an increasingly recognized neurological disorder with significant clinical management challenges, particularly in predicting the need for intensive care unit (ICU) admission. This study aimed to develop and validate predictive models to identify A-GFAP-A patients at increased risk for ICU admission. Methods: We retrospectively analyzed 107 patients (January 2021 - August 2024), randomly assigned to training and validation cohorts (7:3). Variable selection for model development was performed using random forest, least absolute shrinkage and selection operator (LASSO), and extreme gradient boosting (XGBoost). Logistic regression was used to construct a nomogram, and a decision tree was developed to facilitate rapid clinical decision-making. Model performance was assessed by area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Results: Four key predictors of ICU admission were identified: Glasgow Coma Scale (GCS) score at admission, seizures, maximum body temperature, and C-reactive protein (CRP) levels. The nomogram demonstrated excellent predictive accuracy with AUCs of 0.923 (95% CI, 0.858-0.987) in training cohort, 0.922 (95% CI, 0.836-1.000) in validation cohort, and 0.93 (95% CI, 0.883-0.972) in bootstrap validation. The model showed excellent calibration, and DCA confirmed its clinical utility. The decision tree identified GCS <15, seizures, and temperature >39°C as the most relevant indicators for high-risk stratification. Discussion: This study presents the first validated nomogram and decision tree for ICU admission risk in A-GFAP-A, based on the largest reported cohort to date, providing a valuable tool for clinical decision-making and resource optimization.