Noninvasive diagnosis model for predicting significant liver inflammation in patients with chronic hepatitis B in the immune-tolerant phase
Abstract The presence of significant liver inflammation is an important indication for antiviral therapy in immune-tolerant (IT)phase with chronic hepatitis B(CHB) patients. This study aims to establish a non-invasive model to assess significant liver inflammation in the IT-phase of CHB patients. Th...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87756-4 |
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| Summary: | Abstract The presence of significant liver inflammation is an important indication for antiviral therapy in immune-tolerant (IT)phase with chronic hepatitis B(CHB) patients. This study aims to establish a non-invasive model to assess significant liver inflammation in the IT-phase of CHB patients. This multicenter retrospective study included a total of 535 IT-phase CHB patients who underwent liver biopsy, and were randomly divided into a training and a validation set. In the training cohort, the relevant indices were initially screened using univariate analysis. Then the least absolute shrinkage and selection operator and multivariable logistic regression were used to identify the significant independent risk factors and establish a predictive model. A diagnostic nomogram was constructed. Calibration curves, decision curve analysis, and receiver operating characteristic curves were utilized to evaluate the performance of the nomogram. In this study, 37.0% of the patients exhibited significant liver inflammation. Baseline characteristics revealed a median age of 35.0 years, with males accounting for 51.7% of the cohort. Age, Aspartate aminotransferase (AST), Prothrombin (PT), Albumin (ALB) and Hepatitis B virus DNA (HBV DNA) were identified as independent predictors of significant liver inflammation in the immune-tolerant phase, and a nomogram was constructed based on these indicators. The predictive model demonstrated good calibration and discrimination in both the training set and the validation set (aera under the curve (AUC) of 0.741 and 0.740, respectively). The nomogram can accurately identify significant liver inflammation in immune-tolerant phase CHB patients and facilitate the early initiation of antiviral therapy, thereby reducing the need for clinical liver biopsies. |
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| ISSN: | 2045-2322 |