Аңдатпа
Background: Autoimmune hepatitis (AIH) is a chronic immune-mediated liver disease that may progress to cirrhosis. This study aimed to develop and externally validate interpretable machine-learning models for the classification of prevalent cirrhosis in patients with AIH. Methods: The development cohort included 55 patients with biopsy-confirmed AIH. Cirrhosis was defined histologically as F4, whereas F0–F3 was classified as non-cirrhosis. Logistic Regression with L2 regularization, Random Forest, and XGBoost were evaluated. The original stratified 70/30 hold-out analysis was retained, and repeated stratified five-fold cross-validation with 20 repeats was additionally performed to assess internal stability. Primary external validation was performed in an independent histology-matched cohort of 42 patients. Models were applied without refitting, recalibration, or threshold optimization. Discrimination, probabilistic accuracy, calibration, and threshold-dependent classification metrics were evaluated. Results: In the original held-out test set, AUROC was 0.900 for Logistic Regression with L2 regularization, 0.830 for Random Forest, and 0.890 for XGBoost. In repeated cross-validation, mean AUROC was 0.878 ± 0.021, 0.914 ± 0.015, and 0.896 ± 0.015, respectively. In the primary external validation cohort, Random Forest showed the highest numerical discrimination (AUROC 0.810; 95% CI, 0.653–0.933), followed by XGBoost (0.728; 95% CI, 0.566–0.878) and Logistic Regression with L2 regularization (0.716; 95% CI, 0.545–0.875). Random Forest also had the lowest Brier score (0.188). However, confidence intervals were wide and overlapping. Elastography stage alone achieved an AUROC of 0.745, and the numerical improvement of the full Random Forest model was not statistically clear. Conclusions: Machine-learning models integrating routinely available clinical, biochemical, immunological, and elastography-related variables showed preliminary external transportability for the classification of prevalent cirrhosis in AIH. However, the small development and validation cohorts, uncertainty in calibration, and lack of a clearly demonstrated incremental advantage over elastography alone indicate that larger prospective multicenter studies are required before clinical implementation.