A machine learning model incorporating the globulin-to-platelet index for predicting severe fibrosis in autoimmune...

Accurate staging of liver fibrosis in autoimmune hepatitis (AIH) remains challenging due to the invasive nature and sampling limitations of liver biopsy. This study aimed to identify readily available predictors of severe fibrosis and to develop an AIH-specific noninvasive machine-learning model. This two-stage study retrospectively enrolled 208 patients with biopsy-confirmed AIH, with prospective validation in 26 additional patients. Transient elastography (TE) was performed in 110 retrospective and 12 prospective patients. Severe fibrosis was defined as Scheuer stages S3 to S4. Candidate variables underwent univariable and multivariable logistic regression with collinearity control. A random forest (RF) model was trained on the independent predictors and evaluated by the area under the receiver operating characteristic curve (AUROC), calibration, and decision curve analysis. Shapley Additive exPlanations were used for interpretability. Inflammatory activity was graded by Scheuer and prespecified for subgroup analyses (G0-G2 vs G3-G4). A TE-inclusive RF model was also developed in the TE subgroup. The globulin-to-platelet index, international normalized ratio, and blood urea nitrogen were identified as independent predictors of severe fibrosis in AIH. The RF model based on these variables yielded AUROCs of 0.863 (95% confidence interval [CI], 0.802-0.917) in the training set, 0.747 (95% CI, 0.602-0.863) in the test set, and 0.784 (95% CI, 0.556-0.959) in the prospective cohort. Stratified by inflammatory grade, AUROCs were 0.842 (95% CI, 0.757-0.914) in G0-G2 and 0.814 (95% CI, 0.726-0.886) in G3-G4. In contrast, the aspartate aminotransferase-to-platelet ratio index and fibrosis-4 index performed poorly overall and deteriorated further under moderate-to-severe inflammation. In the TE subgroup, the RF model outperformed TE alone (AUROC, 0.786 vs 0.682), and performance improved further when TE was integrated (AUROC, 0.898 [95% CI, 0.841-0.949]). Globulin-to-platelet index, international normalized ratio, and blood urea nitrogen were independent predictors of severe fibrosis in AIH. An RF model constructed from these markers provided a robust, noninvasive tool whose performance was preserved across inflammatory grades and was further enhanced by incorporating TE.