Machine learning-based identification of concomitant stroke and prognostic analysis in patients with Guillain-Barré syndrome: a retrospective study

BackgroundGuillain-Barré syndrome (GBS) constitutes an immune-mediated inflammatory polyradiculoneuropathy. Stroke may coexist with GBS during the same clinical episode, but the associated clinical predictors remain insufficiently characterized.

The present investigation therefore sought to construct a machine learning-based model for identifying concomitant stroke in patients with GBS.MethodsThis retrospective cohort study included 260 patients with GBS who received care at the Second Affiliated Hospital of Army Medical University from January 1, 2015, to December 31, 2024. All candidate predictors were collected at admission.

Feature selection was conducted using LASSO regression, and seven machine learning algorithms were developed and compared. An independent external validation cohort of 60 patients was obtained from the First Affiliated Hospital during the same period.

Patients were subsequently grouped according to model-estimated probabilities, and short-term functional outcomes were compared between groups.ResultsNine clinical predictors were selected to construct seven machine learning models. The neural network architecture exhibited the best performance for identifying concomitant stroke.

Internal validation yielded an AUROC of 0.838 (95% CI: 0.739–0.923) for the optimal model. Sensitivity analysis excluding patients with documented prior stroke showed comparable performance.