Predictive tool for evident histological liver injury in chronic hepatitis B patients: Development and validation.
1/5 보강
[BACKGROUND] Chronic hepatitis B (CHB) is a leading cause of liver-related mortality, progressing to fibrosis, cirrhosis, and hepatocellular carcinoma.
- 95% CI 0.910-1.0
APA
Dai ZS, Cao X, et al. (2026). Predictive tool for evident histological liver injury in chronic hepatitis B patients: Development and validation.. World journal of hepatology, 18(2), 113348. https://doi.org/10.4254/wjh.v18.i2.113348
MLA
Dai ZS, et al.. "Predictive tool for evident histological liver injury in chronic hepatitis B patients: Development and validation.." World journal of hepatology, vol. 18, no. 2, 2026, pp. 113348.
PMID
41809472
Abstract
[BACKGROUND] Chronic hepatitis B (CHB) is a leading cause of liver-related mortality, progressing to fibrosis, cirrhosis, and hepatocellular carcinoma. Existing noninvasive tools (, 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.
[AIM] 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.
[METHODS] This observational real-world study enrolled 223 CHB patients (August 2020 to March 2022) from the Second Xiangya Hospital, divided into development ( = 159) and validation ( = 64) cohorts (7:3 ratio). EHLI was defined as Ishak fibrosis stage ≥ 3 and/or histologic activity index ≥ 9. Variables were screened 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.
[RESULTS] 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 (, ) and transforming growth factor-β/Smad pathway involvement linked to liver injury.
[CONCLUSION] 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.
[AIM] 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.
[METHODS] This observational real-world study enrolled 223 CHB patients (August 2020 to March 2022) from the Second Xiangya Hospital, divided into development ( = 159) and validation ( = 64) cohorts (7:3 ratio). EHLI was defined as Ishak fibrosis stage ≥ 3 and/or histologic activity index ≥ 9. Variables were screened 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.
[RESULTS] 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 (, ) and transforming growth factor-β/Smad pathway involvement linked to liver injury.
[CONCLUSION] 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.