ArticleInfection and drug resistance2023
Development and Validation of a Nomogram to Predict Significant Liver Inflammation in Patients with Chronic Hepatitis B.
Article in Infection and drug resistance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A machine learning predictive model for staging chronic hepatitis B inflammation based on non-invasive metrics.Scientific reports · 2026Article
- A Nomogram to Predict Day-7 Total Bilirubin in Hepatitis B Virus-Related Cirrhosis: An Early Treatment-Response Assessment Tool.Infection and drug resistance · 2026Article
- Insights into the Management of Chronic Hepatitis in Children-From Oxidative Stress to Antioxidant Therapy.International journal of molecular sciences · 2024Review
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Authors and funding
16 authors.
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Abstract
Background: Noninvasive diagnosis of liver inflammation is important for patients with chronic hepatitis B (CHB). This study aimed to develop a nomogram to predict significant liver inflammation for CHB patients. Methods: CHB patients who underwent liver biopsy were retrospectively collected and randomly divided into a development set and a validation set. The least absolute shrinkage and selection operator regression and logistic regression analysis were used to select independent predictors of significant liver inflammation, and a nomogram was developed. The performance of nomogram was assessed by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Results: A total of 1019 CHB patients with a median age of 39.0 years were included. Alanine aminotransaminase (ALT, P = 0.018), gamma-glutamyl transpeptidase (P = 0.013), prothrombin time (P < 0.001), and HBV DNA level (P = 0.030) were identified as independent predictors of significant liver inflammation in the development set. A model namely AGPD-nomogram was developed based on the above parameters. The area under the ROC curve in predicting significant inflammation was 0.765 (95% CI: 0.727-0.803) and 0.766 (95% CI: 0.711-0.821) in the development and validation sets, which were significantly higher than other indexes. The AGPD-nomogram had a high predictive value in patients with normal ALT. Moreover, the nomogram was proven to be clinically useful by DCA. Conclusion: A visualized AGPD-nomogram which incorporated routine clinical parameters was proposed to facilitate the prediction of significant liver inflammation in CHB patients. This nomogram had high accuracy in the identification of significant liver inflammation and would be a useful tool for the better management of CHB patients, especially for those with normal ALT.
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