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Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B
Chenyang Huang1,2, Ying Lu3, Ziwei Wang2
1Medical School of Chinese PLA, Beijing 100853, China.
Biomedicines
|January 8, 2025
Summary
Monitoring age, ALT, AST, and GGT levels helps identify children with chronic hepatitis B (CHB) at risk for liver inflammation and fibrosis. Early detection through these markers aids in managing pediatric liver disease.
Area of Science:
- Hepatology
- Pediatric Gastroenterology
- Viral Hepatitis Research
Background:
- Chronic hepatitis B (CHB) poses a significant global health challenge in children.
- Liver inflammation and fibrosis are key concerns impacting disease progression and long-term outcomes in pediatric CHB.
- Accurate assessment of liver pathology is crucial for effective management.
Purpose of the Study:
- To evaluate the predictive value of clinical indicators for liver inflammation and fibrosis severity in pediatric CHB patients.
- To identify key biochemical markers and age as predictors of liver pathology in children with CHB.
- To inform the development of non-invasive diagnostic algorithms for pediatric liver disease.
Main Methods:
- Retrospective analysis of 1629 pediatric CHB patients (2000-2021).
- Liver biopsies assessed using the Scheuer scoring system for inflammation and fibrosis.
- Restricted cubic spline regression models used to analyze associations between age, ALT, AST, GGT, and liver pathology.
Main Results:
- Older age correlated with increased risk of moderate-to-severe inflammation (OR 2.21) and significant fibrosis (OR 2.22).
- Elevated ALT (≥80 U/L) linked to higher likelihood of moderate-to-severe inflammation (OR 1.82).
- Higher GGT (≥50 U/L) significantly associated with advanced fibrosis (OR 2.62).
Conclusions:
- Regular monitoring of ALT, AST, and GGT levels is vital for identifying pediatric CHB patients at high risk.
- Age and biochemical markers (ALT, GGT) are valuable predictors of liver inflammation and fibrosis severity.
- Integrating these factors into non-invasive algorithms can improve early detection and management of pediatric liver pathology.
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