Predictive tool for evident histological liver injury in chronic hepatitis B patients: Development and validation

Chronic hepatitis B (CHB) is a leading cause of liver-related mortality, progressing to fibrosis, cirrhosis, and hepatocellular carcinoma. Existing noninvasive tools (e.g., aspartate aminotransferase to platelet ratio index, fibrosis-4 index, liver stiffness measurement) and invasive liver biopsy have limitations in assessing evident histological liver injury (EHLI), highlighting the need for novel predictive models.

To develop and validate a predictive model for EHLI in CHB patients using a cohort from Hunan Province, China, to facilitate early risk identification and optimize resource allocation.

This observational real-world study enrolled 223 CHB patients (August 2020 to March 2022) from the Second Xiangya Hospital, divided into development (n = 159) and validation (n = 64) cohorts (7:3 ratio). EHLI was defined as Ishak fibrosis stage ≥ 3 and/or histologic activity index ≥ 9. Variables were screened via univariable logistic regression and least absolute shrinkage and selection operator regression, and a multivariable logistic regression model and nomogram were constructed. Performance was evaluated using area under the curve (AUC), calibration plots, Hosmer-Lemeshow test, and decision curve analysis (DCA). Gene expression profiles were analyzed to identify immune-related pathways.

L59, platelet count (PLT), alanine transaminase (ALT), and aspartate transaminase (AST) were identified as independent predictors of EHLI. The model showed high discriminative ability, with AUC of 0.921 [95% confidence interval (CI): 0.880-0.963] in the development cohort and 0.959 (95%CI: 0.910-1.0) in the validation cohort, demonstrating a 20%-32% relative improvement in AUC over conventional noninvasive scores. Calibration plots demonstrated good agreement between predicted and observed EHLI, and DCA confirmed clinical utility (threshold probabilities: 20%-80%). Transcriptomic analysis identified 210 differentially expressed genes, with hub genes (e.g., COL1A2) and transforming growth factor-β/Smad pathway involvement linked to liver injury.

A novel nomogram incorporating L59, PLT, ALT, and AST robustly predicts EHLI in CHB patients. This model, using routinely measured variables, aids clinical decision-making and optimizes resource allocation.