ArticleJournal of translational medicine2018
Prediction of hepatic necroinflammatory activity in patients with chronic hepatitis B by a simple noninvasive model.
Article in Journal of translational medicine, 2018. 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
- An RFH-NPT-based nomogram for predicting the long-term survival of liver cirrhosis patients: a multicenter study.European journal of clinical nutrition · 2026Article
- The Importance of Transaminases Flare in Liver Elastography: Characterization of the Probability of Liver Fibrosis Overestimation by Hepatitis C Virus-Induced Cytolysis.Microorganisms · 2020Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundA model was constructed using clinical and serum variables to discriminate between chronic hepatitis B (CHB) patients with and without significant necroinflammatory activity (score 4-18 vs. score 0-3).
methodsConsecutive CHB patients who underwent liver biopsy were divided into two sequential groups: a training group (n = 401) and a validation group (n = 401). Multivariate analysis identified alanine aminotransferase, γ-glutamyltransferase, prothrombin time and albumin as independent predictors of necroinflammatory activity.
resultsThe area under the receiver operating characteristic curve was 0.826 for the training group and 0.847 for the validation group. Using a cut-off score of H ≤ 0.375, significant necroinflammatory activity (score 4-18) was excluded with high accuracy [78.2% negative predictive value (NPV), 72% positive predictive value (PPV), and 90.8% sensitivity] in 238 (59.4%) of 401 patients in the training group and with the same certainty (88.1% NPV, 61.2% PPV, and 95.1% sensitivity) among 204 (50.9%) of 401 patients in the validation group. Similarly, applying a cut-off score of H > 0.720, significant necroinflammatory activity was correctly identified with high accuracy (90.8% PPV, 57.7% NPV, and 92.0% specificity) in 150 (37.4%) of 401 patients in the training group and with the same certainty (91.8% PPV, 64.6% NPV, and 95.4% specificity) in 188 (46.9%) of 401 patients in the validation group.
conclusionsA predictive model based on easily accessible variables identified CHB patients with and without significant necroinflammatory activity with a high degree of accuracy. This model may decrease the need for liver biopsy for necroinflammatory activity grading in 72.1% of CHB patients.
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