Central Signs Predict Antiganglioside Antibodies in Guillain–Barré Syndrome: A Machine Learning-Based Analysis

Objective To investigate the association between central nervous system (CNS) signs and antiganglioside antibody profiles in patients with Guillain–Barré syndrome (GBS), and to evaluate the predictive value of clinical features for GM2 antibody positivity using logistic regression and machine learning models. Methods A total of 200 patients with GBS were retrospectively analyzed. Clinical data including age, neurological signs, cerebrospinal fluid parameters, and antiganglioside antibody results were collected. Logistic regression was used to assess independent predictors of GM2 antibody positivity. An XGBoost model was constructed to evaluate predictive performance. Model discrimination was assessed by receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics. Feature importance was analyzed to interpret model behavior. Results Among the 200 patients, the positivity rates for GM1, GM2, GD1a, and GQ1b antibodies were 33.5%, 25%, 23%, and 21%, respectively. Central signs were observed in 35 of the 50 GM2-positive patients. Logistic regression identified central signs (OR = 7.92, 95% CI: 3.77–17.76) and Babinski sign (OR = 3.16, 95% CI: 1.39–7.38) as independent predictors of GM2 positivity. The XGBoost model achieved comparable discrimination (AUC = 0.735) to the logistic regression model (AUC = 0.741). Feature importance analysis revealed central signs and Babinski sign as dominant contributors. Conclusion CNS signs, particularly the presence of Babinski sign and generalized hyperreflexia, are significantly associated with GM2 antibody positivity in GBS. These features may serve as practical clinical indicators to prompt antibody testing and guide immunotherapy decisions.