Research Article: Predicting 12-month functional outcome in Guillain-Barré syndrome by combining acute-phase clinical data and traditional Chinese medicine syndrome features: a retrospective machine learning study

Abstract:
Functional recovery after Guillain-Barré syndrome (GBS) varies substantially. Prediction models that combine routinely available acute-phase clinical information with traditional Chinese medicine (TCM) syndrome features have not been adequately reported or validated. This study developed an acute-phase dynamic prediction model for 12-month functional outcome in GBS and evaluated discrimination, calibration, and potential clinical utility. We retrospectively screened 601 patients admitted between January 2015 and December 2023; 525 met eligibility criteria and had 12-month outcome data. The prediction index time was the end of acute in-hospital assessment/discharge; no post-discharge follow-up variables were used as predictors. Candidate predictors included demographics, acute clinical severity, laboratory and electrophysiological findings, treatment-related acute-care variables, and standardized TCM syndrome elements. Poor outcome was defined as Hughes Functional Grading Scale (HFGS) score >?=?3 at 12?months. Patients were stratified into a training cohort ( n =?368) and a hold-out validation cohort ( n =?157). Preprocessing, imputation, standardization, feature selection, and synthetic oversampling were performed within the training data only. Logistic regression, random forest, and a fully connected neural-network model were compared. Performance was assessed with 95% confidence intervals, calibration plots, Brier score, decision-curve analysis, and formal AUC comparisons. Poor 12-month outcome occurred in 148 of 525 patients (28.2%). Mechanical ventilation, admission HFGS score, AMSAN subtype, qi deficiency, and blood stasis were independently associated with poor outcome. In the hold-out validation cohort, AUCs were 0.744 (95% CI 0.655–0.833) for logistic regression, 0.843 (95% CI 0.780–0.906) for random forest, and 0.887 (95% CI 0.831–0.943) for the neural network. The neural network had the lowest Brier score (0.123) and showed acceptable calibration (calibration intercept 0.03; slope 0.94). DeLong testing showed that the neural network outperformed logistic regression ( p =?0.006) but was not statistically superior to random forest ( p =?0.174). Decision-curve analysis suggested net benefit for the machine-learning models across clinically plausible thresholds. In this single-center retrospective cohort, acute-phase clinical variables and TCM syndrome elements were associated with 12-month functional outcome in GBS. The neural-network model had the highest numerical AUC and lowest Brier score, but it was not statistically superior to random forest; therefore, no superiority over random forest is claimed. External validation, comparison with established prognostic scores when required variables are available, and prospective implementation studies are required before clinical use.

Introduction:
Functional recovery after Guillain-Barré syndrome (GBS) varies substantially. Prediction models that combine routinely available acute-phase clinical information with traditional Chinese medicine (TCM) syndrome features have not been adequately reported or validated. This study developed an acute-phase dynamic prediction model for 12-month functional outcome in GBS and evaluated discrimination, calibration, and potential clinical utility.

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