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Predictive tool for evident histological liver injury in chronic hepatitis B patients: Development and validation
Zhong-Shang Dai1, Xin Cao2, Yong-Fang Jiang3
1Department of Infectious Diseases, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Insights
A new model using L59, platelet count (PLT), alanine transaminase (ALT), and aspartate transaminase (AST) accurately predicts evident histological liver injury (EHLI) in chronic hepatitis B (CHB) patients. This tool aids early risk identification and resource allocation for better CHB management.
Area of Science:
- Hepatology and viral hepatitis research.
- Biomarker discovery and predictive modeling in liver disease.
- Genomic and transcriptomic analysis of liver injury.
Background:
- Chronic hepatitis B (CHB) poses significant mortality risks, leading to severe liver conditions like fibrosis, cirrhosis, and cancer.
- Current noninvasive and invasive methods for assessing liver injury in CHB have limitations.
- There is a critical need for advanced predictive models to identify evident histological liver injury (EHLI).
Purpose of the Study:
- To develop and validate a predictive model for EHLI in CHB patients.
- To utilize a cohort from Hunan Province, China, for model development and validation.
- To facilitate early risk stratification and optimize resource allocation in CHB patient management.
Main Methods:
- An observational, real-world study involving 223 CHB patients.
- Development and validation cohorts (7:3 ratio) were established.
- EHLI defined as Ishak fibrosis stage ≥ 3 and/or histologic activity index ≥ 9.
- Multivariable logistic regression and nomogram construction using screened variables.
- Model performance assessed via AUC, calibration plots, and decision curve analysis (DCA).
- Gene expression profiling to identify immune-related pathways.
Main Results:
- L59, platelet count (PLT), alanine transaminase (ALT), and aspartate transaminase (AST) identified as independent EHLI predictors.
- The nomogram achieved high discrimination: AUC 0.921 (development) and 0.959 (validation).
- Demonstrated significant AUC improvement over conventional noninvasive scores.
- Good agreement between predicted and observed EHLI, with confirmed clinical utility via DCA.
- Transcriptomic analysis revealed 210 differentially expressed genes, implicating the TGF-β/Smad pathway.
Conclusions:
- A novel nomogram incorporating L59, PLT, ALT, and AST effectively predicts EHLI in CHB patients.
- The model utilizes routinely available laboratory data for robust prediction.
- This tool supports clinical decision-making and optimizes resource allocation for CHB management.
Background:
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.
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 (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.
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 (e.g., COL1A2) 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.
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