ArticleEClinicalMedicine2024
Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study.
Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05766449 (Risk Factors for Hepatic Inflammation, Fibrosis and Prognosis in Patients With Chronic Hepatitis B Combined With Non-alcoholic Fatty Liver Disease), which is not on this map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Risk Factors for Hepatic Inflammation, Fibrosis and Prognosis in Patients With Chronic Hepatitis B Combined With Non-alcoholic Fatty Liver Disease
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- APASL clinical practice guidelines on the management of chronic hepatitis B infection: a 2026 update.Hepatology international · 2026Guideline
- Development and validation of a machine learning-based model for identifying liver fibrosis in individuals with prior Schistosoma japonicum infection: a step toward precision management.Infectious diseases of poverty · 2026Article
- [APASL clinical practice guidelines on the management of chronic hepatitis B infection: a 2026 update].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- A Weighted Neural Network Model Based on Laboratory Tests for Identifying Lymph Node Metastases in Esophageal Squamous Cell Carcinomas.Biosensors · 2026Article
- Diagnosing and defining MASLD in people living with chronic hepatitis B.Communications medicine · 2026Review
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- Comparison of the predictive power of novel insulin resistance, lipid metabolism and obesity indices for the risk of developing dyslipidaemia in adults.BMC endocrine disorders · 2026Article
- Development and validation of an interpretable machine learning model for non-invasive screening of precancerous gastric lesions using symptom and lifestyle data: a multicentre cohort study.EClinicalMedicine · 2026Article
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- Interpretable and reproducible machine learning model for coronary calcification and segment-level stenoses stratification on computed tomography angiography.BMC medicine · 2025Article
- An explainable machine learning model for predicting postoperative cholangitis in pediatric surgical patients with pancreaticobiliary maljunction.BioData mining · 2025Article
- Gray zone and the need for expansion in chronic hepatitis B: From theory to clinical practice.World journal of hepatology · 2025Review
- Rapid discrimination ofBMJ health & care informatics · 2025Article
- Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD.Scientific reports · 2025Article
- Article
- Performance of the AASLD, EASL, and APASL Clinical Practice Guidelines in"grey zone"stages of Chinese patients with chronic hepatitis B.Hepatology international · 2025Observational
- Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding.NPJ digital medicine · 2025Article
- Automated identification ofJournal of clinical microbiology · 2025Article
- Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study.Clinical and molecular hepatology · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
26 authors at 16 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.