Okuda lecture: Challenges of hepatitis B in the era of antiviral therapy

Chan, Henry Lik Yuen

doi:10.1111/jgh.14534

Cited by 6 publications

(5 citation statements)

References 70 publications

(179 reference statements)

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“…Liver biopsy typically shows no fibrosis and minimal inflammation. This can be explained by the relative lack of immune pressure on the virus [9]. Early natural history studies of immunetolerant phase revealed a benign disease course.…”

Section: Current Understanding Of Immune-tolerant Phasementioning

confidence: 99%

Unresolved issues of immune tolerance in chronic hepatitis B

Lee

Chan

2020

J Gastroenterol

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During the natural course of chronic hepatitis B virus infection, immune-tolerant phase is characterized by high viral replication, the presence of HBV e antigen (HBeAg), and normal or minimally elevated serum alanine aminotransferase. Immune-tolerant phase is usually regarded as a benign course of the disease. International guidelines recommend observation rather than treatment during immune-tolerant phase. In this article, we review unresolved issues related to the definition of true immunetolerant phase and the benefit of antiviral treatment. Defining true immune-tolerant phase requires a careful approach and long-term follow-up. In previous studies, many patients were misclassified as being immune-tolerant phase. Noninvasive methods of assessing fibrosis are warranted for patients in the immune-tolerant phase. Yet, there has been controversy over the benefit and harm of antiviral treatment for immune-tolerant phase patients. Thus, further larger scale studies are needed to investigate the prognosis of patients in true immune-tolerant phase and their need for antiviral therapy.

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Section: Current Understanding Of Immune-tolerant Phasementioning

confidence: 99%

Unresolved issues of immune tolerance in chronic hepatitis B

Lee

Chan

2020

J Gastroenterol

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“…Although the current first-line oral nucleos(t)ide analogues (NAs), such as entecavir (ETV) or tenofovir disoproxil fumarate (TDF), can effectively inhibit hepatitis B virus (HBV) replication, HCC can still develop in chronic hepatitis B (CHB) patients undergoing NA treatment. 2 , 4 , 5 A 10-year follow-up study conducted in Europe revealed the occurrence of new HCC cases even after more than 5 years of NA treatment. 6 Thus, it is essential to stratify the risk of HCC in patients with HBC.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study

Hou,

Yan,

Shi

et al. 2024

OTT

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Object Our objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN). Methods We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People’s Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People’s Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves. Results In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670–0.768), PAGE-B (0. 710) (95% CI 0.660–0.759), FIB-4 (0.693) (95% CI 0.640–0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654–0.756) (p<0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0–27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2–99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6–60.7), and the NPV was 91.6% (95% CI 89.4–93.4). These findings were validated in the independent validation cohort. Conclusion The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC.

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“…A general consensus suggested using risk stratification scores in patients with GEV, which could help assess the risk of bleeding, prevent bleeding, and reduce the mortality of patients [ 6 ]. The hepatic venous pressure gradient is an important for stratifying liver cirrhosis patients, which can predict hypertension-related portal circulation complications [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

Hou

Zhang

et al. 2023

Diagn Pathol

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Background Liver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN). Methods We included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model. Results The 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk. The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P < 0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices. Conclusions The ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies.

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Okuda lecture: Challenges of hepatitis B in the era of antiviral therapy

Cited by 6 publications

References 70 publications

Unresolved issues of immune tolerance in chronic hepatitis B

Unresolved issues of immune tolerance in chronic hepatitis B

Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study

Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

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