Prediction of mechanical ventilation in Guillain-Barré syndrome at admission: Construction of a nomogram and comparison with the EGRIS model

Background: Respiratory failure requiring mechanical ventilation (MV) is a common and severe complication of Guillain-Barré syndrome (GBS) with a reported incidence ranging from 20 % to 30 %. Thus, we aim to develop a nomogram to evaluate the risk of MV in patients with GBS at admission and tailor individualized care and treatment. Methods: A total of 633 patients with GBS (434 in the training set, and 199 in the validation set) admitted to the First Hospital of Jilin University, Changchun, China from January 2010 to January 2021 were retrospectively enrolled. Subjects (n = 71) from the same institution from January 2021 to May 2022 were prospectively collected and allocated to the testing set. Multivariable logistic regression analysis was applied to build a predictive model incorporating the optimal features selected in the least absolute shrinkage and selection operator (LASSO) in the training set. The predictive model was validated using internal bootstrap resampling, an external validation set, and a prospective testing set, and the model's performance was assessed by using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Finally, we established a multivariable logistic model by using variables of the Erasmus GBS Respiratory Insufficiency Score (EGRIS) and did the same analysis to compare the performance of our predictive model with the EGRIS model. Results: Variables in the final model selected by LASSO included time from onset to admission, facial and/or bulbar weakness, Medical Research Council sum score at admission, neutrophil-to-lymphocyte ratio, and platelet-lymphocyte ratio. The model presented as a nomogram displaying favorable discriminative ability with a C-index of 0.914 in the training set, 0.903 in the internal validation set, 0.953 in the external validation set, and 0.929 in the testing set. The model was well-calibrated and clinically useful as assessed by the calibration curve and DCA. As compared with the EGRIS model, our predictive model displayed satisfactory performance. Conclusions: We constructed a nomogram for early prediction of the risk of MV in patients with GBS. This model had satisfactory performance and appeared more efficient than the EGRIS model in Chinese patients with GBS.