A noninvasive model to predict liver histology in HBeAg-positive chronic hepatitis B with alanine aminotransferase ≤ 2upper limit of normal

A nomogram based on HBeAg, AST, and age to predict non-minimal liver inflammation in CHB patients with ALT <80 U/L

Objective

Precise assessment of liver inflammation in untreated hepatitis B e antigen (HBeAg)-positive patients with chronic hepatitis B virus (HBV) infection can determine when to initiate antiviral therapy. The aim of this study was to develop and validate a nomogram model for the prediction of non-minimal liver inflammation based on liver pathological injuries combined with age and alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis B surface antigen (HBsAg), HBeAg, and HBV DNA quantification.

Methods

We retrospectively included 735 HBeAg-positive chronic hepatitis B (CHB) patients with ALT < 80 U/L as the primary cohort and prospectively enrolled 196 patients as the validation cohort. Multivariate logistic regression analysis identified independent impact factors. A nomogram to predict significant liver inflammation was developed and validated.

Results

Multivariate logistic regression analysis showed that HBeAg, AST, and age were independent risk factors for predicting non-minimal liver inflammation in untreated CHB patients. The final formula for predicting non-minimal liver inflammation was Logit(P) = -1.99 - 0.68 × Log₁₀HBeAg + 0.04 × Age + 0.06 × AST. A nomogram for the prediction of non-minimal liver inflammation was established based on the results from the multivariate analysis. The predicted probability of the model being consistent with the actual probability was validated by the calibration curves, showing the best agreement in both the primary and validation cohorts. The C-index was 0.767 (95%CI = 0.734-0.802) in the primary cohort and 0.749 (95%CI = 0.681-0.817) in the prospective validation cohort.

Conclusions

The nomogram based on HBeAg, AST, and age might help predict non-minimal liver inflammation in HBeAg-positive CHB patients with ALT < 80 U/L, which is practical and easy to use for clinicians.

Early prediction model for prognosis of patients with hepatitis-B-virus-related acute-on-chronic liver failure received glucocorticoid therapy

Background

Early prediction for short-term prognosis is essential for the management of hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). In this study, we aim to establish a noninvasive model for predicting the 90-day mortality in patients with HBV–ACLF received glucocorticoid therapy.

Methods

Two hundred and eighty patients with HBV–ACLF were enrolled from July 2010 to June 2022. All patients received routine medicine treatment and 204 of them received additional glucocorticoid treatment. Then, the patients who received glucocorticoid treatment were randomly divided into a training cohort and a validation cohort. An early prediction model for 90-day mortality of HBV–ACLF was established in the training cohort and then validated in the validation cohort.

Results

HBV–ACLF patients received glucocorticoid treatment showed significantly better survival that those not (P < 0.01). In the training cohort, a noninvasive model was generated with hepatic encephalopathy grade, INR, total bilirubin, age and SIRS status, which was named HITAS score. It showed significantly better predictive value for 90-day mortality of HBV–ACLF than MELD score and Child–Turcotte–Pugh score in both the training cohort and validation cohort. Using the Kaplan–Meier analysis with cutoff points of 2.5 and 3.47, the HITAS score can classify HBV–ACLF patients into different groups with low, intermediate and high risk of death after glucocorticoid therapy.

Conclusions

We proposed a HITAS score, which was an early prediction model for the prognosis of HBV–ACLF. It might be used to identify HBV–ACLF patients with favorable responses to glucocorticoid treatment.

Viral quasispecies quantitative analysis: a novel approach for appraising the immune tolerant phase of chronic hepatitis B virus infection

Few non-invasive models were established for precisely identifying the immune tolerant (IT) phase from chronic hepatitis B (CHB). This study aimed to develop a novel approach that combined next-generation sequencing (NGS) and machine learning algorithms using our recently published viral quasispecies (QS) analysis package. 290 HBeAg positive patients from whom liver biopsies were taken were enrolled and divided into a training group (n = 148) and a validation group (n = 142). HBV DNA was extracted and QS sequences were obtained by NGS. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) based on viral operational taxonomic units (OTUs) were performed to explore the correlations among QS and clinical phenotypes. Three machine learning algorithms, including K-nearest neighbour, support vector machine, and random forest algorithm, were used to construct diagnostic models for IT phase classification. Based on histopathology, 90 IT patients and 200 CHB patients were diagnosed. HBsAg titres for IT patients were higher than those of CHB patients (p < 0.001). HCA and PCA analysis grouped IT and CHB patients into two distinct clusters. The relative abundance of viral OTUs differed mainly within the BCP/precore/core region and was significantly correlated with liver inflammation and fibrosis. For the IT phase classification, all machine-learning models showed higher AUC values compared to models based on HBsAg, APRI, and FIB-4. The relative abundance of viral OTUs reflects the severity of liver inflammation and fibrosis. The novel QS quantitative analysis approach could be used to diagnose IT patients more precisely and reduce the need for liver biopsy.

A non-invasive model for predicting liver fibrosis in HBeAg-positive patients with normal or slightly elevated alanine aminotransferase

ABSTRACT

Early and accurate diagnosis of liver fibrosis is necessary for HBeAg-positive chronic hepatitis B (CHB) patients with normal or slightly increased alanine aminotransferase (ALT), Liver biopsy and many non-invasive predicting markers have several application restrictions in grass-roots hospitals. We aimed to construct a non-invasive model based on routinely serum markers to predict liver fibrosis for this population.A total of 363 CHB patients with HBeAg-positive, ALT ≤2-fold the upper limit of normal and liver biopsy data were randomly divided into training (n = 266) and validation groups (n = 97). Two non-invasive models were established based on multivariable logistic regression analysis in the training group. Model 2 with a lower Akaike information criterion (AIC) was selected as a better predictive model. Receiver operating characteristic (ROC) was used to evaluate the model and was then independently validated in the validation group.The formula of Model 2 was logit (Model value) = 5.67+0.08 × Age -2.44 × log10 [the quantification of serum HBsAg (qHBsAg)] -0.60 × log10 [the quantification of serum HBeAg (qHBeAg)]+0.02 × ALT+0.03 ×  aspartate aminotransferase (AST). The area under the ROC curve (AUC) was 0.89 for the training group and 0.86 for the validation group. Using 2 cut-off points of -2.61 and 0.25, 59% of patients could be identified with liver fibrosis and antiviral treatment decisions were made without liver biopsies, and 149 patients were recommended to undergo liver biopsy for accurate diagnosis.In this study, the non-invasive model could predict liver fibrosis and may reduce the need for liver biopsy in HBeAg-positive CHB patients with normal or slightly increased ALT.

Significant histological changes and satisfying antiviral efficacy in chronic hepatitis B virus infection patients with normal alanine aminotransferase. Antiviral therapy decision in chronic HBV patients with normal ALT

BACKGROUND AND AIMS

A proportion of chronic hepatitis B virus (HBV) infection patients with normal alanine aminotransferase (ALT) should start antiviral therapy based on liver biopsy. We aim to evaluate the proportion of such patients, find noninvasive methods for identifying and then evaluate antiviral efficacy.

METHODS

253 chronic HBV infection patients with normal ALT were analyzed at baseline and 57 patients with histological indication for antiviral therapy (Histology activity index ≥5 and/or Ishak fibrosis score ≥3) and 140 patients with elevated ALT received entecavir therapy and were followed-up to 78 weeks with a second liver biopsy in this multi-center study.

RESULTS

127 (50.2%) of 253 patients with normal ALT fulfilled histological indication for antiviral therapy. Aspartate aminotransferase (P=0.049), anti-hepatitis B virus core antibody (P=0.001) and liver stiffness measurement (P=0.000) were independent variables for identifying histological indication for antiviral therapy. A noninvasive model (AAF) performed best among independent variables and other noninvasive models with area under the operating characteristic curve of 0.887. Antiviral efficacy showed that 38 (66.7%) of 57 patients had undetectable HBV DNA. 12 (41.4%) of 29 patients who were hepatitis B e antigen (HBeAg)-positive at baseline achieved HBeAg loss and 3 (10.3%) achieved HBeAg seroconversion. 25 (43.9%) of 57 patients achieved histological response. Moreover, 57 patients with normal ALT had a similar antiviral therapy efficacy with 140 patients with elevated ALT (P>0.1) except proportion of inflammation improvement and histological response (P=0.005, P=0.049).

CONCLUSIONS

Half of chronic HBV patients with normal ALT should start antiviral therapy based on liver biopsy. A noninvasive model could be used as a reliable tool for antiviral therapy decision. Patients with normal or elevated ALT had a similar antiviral efficacy.

A noninvasive model to predict liver histology for antiviral therapy decision in chronic hepatitis B with alanine aminotransferase < 2 upper limit of normal

Background

At present, most assessments of liver fibrosis staging mainly focus on non-invasive diagnostic methods. This study aims to construct a noninvasive model to predict liver histology for antiviral therapy in chronic hepatitis B (CHB) with alanine aminotransferase (ALT) < 2 times upper limit of normal (ULN).

Methods

We retrospectively analyzed 577 patients with CHB who received liver biopsy and whose ALT was less than 2 ULN. Then they were randomly divided into a training group and a validation group. Through logistic regression analysis, a novel predictive model was constructed in the training group to predict significant changes in liver histology [necro-inflammatory activity grade (G) ≥ 2 or fibrosis stage (S) ≥ 2] and then validated in the validation group.

Results

If liver biopsy showed moderate or severe inflammation or significant fibrosis, antiviral treatment was recommended. Aspartate aminotransferase (AST), anti-hepatitis B virus core antibody (anti-HBC) and glutamine transpeptidase (GGT) were identified as independent predictors for antiviral therapy, with area under the ROC curve (AUROC) of 0.649, 0.647 and 0.616, respectively. Our novel model index, which combined AST, anti- HBC and GGT with AUROC of 0.700 and 0.742 in training set and validation set.

Conclusions

This study established a noninvasive model to predict liver histology for antiviral treatment decision in patients with CHB with ALT < 2 ULN, which can reduce the clinical needs of liver biopsy.

AST and HBeAg Level Can Help to Distinguish Non-Minimal Liver Inflammation in Persistently Normal Alanine Aminotransferase of Chronic HBV Infection

Objectives: The current study aimed to investigate the characteristics of HBV serum markers (HBsAg, HBeAg), biochemical indicators, HBV DNA, and the age to distinguish minimal from non-minimal liver histological inflammation group in HBeAg-positive chronic HBV-infected patients with ALT≤ 1ULN (40U/L). Methods: The HBeAg-positive patients with treatment-naïve hospitalized at Ditan hospital from January 2008 to January 2017 are investigated. Patients were separated into two groups of minimal and non-minimal (mild and moderate) histological inflammation group by liver biopsy specimens. Data were analyzed using the SPSS package. Results: There were both positive (age, ALT, and AST) and negative correlation factors (serum HBsAg, HBeAg, or HBV DNA quantitation) to the liver inflammation grades. Multivariate regression analysis indicated that HBeAg (P < 0.001, b = -0.554, Exp (B) = 0.575) and AST (P = 0.003, b = 0.074, Exp (B) = 1.077) were independent influential factors. The cutoff values of HBeAg and AST were separately 2.85 Log10S/CO (AUC0.724, Sensitivity64%, Specificity79%), 28U/L (AUC0.726, Sensitivity68%, Specificity 78%) to distinguish Minimal from Non-minimal liver histological inflammation in chronic HBV-infected patients with ALT ≤ 1 ULN (40U/L). Conclusions: In total, 31.34% (115/367) of patients with chronic HBV infection who had non-minimal (mild and moderate) liver histological inflammation reached the required inflammation levels for antiviral treatment in HBeAg-positive patients with persistently normal ALT. HBeAg (cutoff < 2.85 Log10S/CO) and AST (cutoff > 28 U/L) were the independent influential factors of predicting non-minimal liver inflammation with ALT ≤ 1 ULN (40U/L).

Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B

It is still vague for chronic hepatitis B (CHB) patients with normal or mildly increasing alanine aminotransferase (ALT) level to undergo antiviral treatment or not. The purpose of our study was to establish a noninvasive model based on routine blood test to predict liver histopathology for antiviral therapy. This retrospective study enrolled 258 CHB patients with liver biopsy from the First Hospital of Quanzhou (training cohort, n=126) and Huashan Hospital (validation cohort, n=132). Histologic grading of necroinflammation (G) and liver fibrosis (S) was performed according to the Scheuer scoring system. A novel model, ATPI, including aspartate aminotransferase (AST), total bilirubin (TBil), and platelets (PLT), was developed in training cohort. The area under ROC curves (AUC) of ATPI for predicting antiviral therapy indication was 0.83 in training cohort and was 0.88 in the validation cohort, respectively. Similarly, ATPI also displayed the highest AUC in predicting antiviral therapy indication in CHB patients with normal or mildly increasing ALT level. In conclusion, ATPI is a novel independent model to predict liver histopathology for antiviral therapy in CHB patients with normal and mildly increased ALT levels.

A promising non-invasive index for predicting liver inflammation in chronic hepatitis B patients with alanine aminotransferase ≤2 upper limit of normal

Inexpensive and simple non-invasive indexes for predicting liver inflammation are urgently required, but have been poorly studied in chronic hepatitis B (CHB) patients with alanine transaminase (ALT) ≤2 times the upper limit of normal (ULN). A total of 356 CHB patients with ALT ≤2 ULN who presented at Huashan Hospital (n=181) and the First Hospital of Quanzhou (n=175) were enrolled and randomly divided into an experimental assessment cohort (n=238) and validation cohort (n=118) at a ratio of 2:1. Histological analysis of liver tissue was performed to determine the pathological stage according to the Scheuer scoring system. For the experimental assessment cohort, univariate and multivariate analysis identified aspartate aminotransferase (AST) and albumin (ALB) as independent predictors of liver necroinflammation [liver necroinflammation grade (G)≥2] in patients with ALT ≤2 ULN. Therefore, a novel index, the AST-to-ALB ratio (ATAR), was proposed, which had a better diagnostic performance [area under receiver operating characteristic curve (AUC)=0.721] than that of ALB (AUC=0.632; P=0.039 vs. ATAR) and AST (AUC=0.682; P=0.082 vs. ATAR). In the validation cohort, the AUC of ATAR (0.728) to identify patients with a G≥2 was slightly greater than that of AST (0.660; P=0.149 vs. ATAR) and ALB (0.672; P=0.282 vs. ATAR). Furthermore, a similar diagnostic superiority was also demonstrated in patients with ALT ≤1 ULN. Thus, ATAR may be a promising non-invasive surrogate marker for liver necroinflammation CHB patients with ALT ≤2 ULN and thereby determine whether anti-viral treatment should be initiated.

Serum YKL-40 as a biomarker for liver fibrosis in chronic hepatitis B patients with normal and mildly elevated ALT

Purpose

YKL-40 is a chitinase-like protein expressed in multiple tissues including liver and is reported as a fibrosis marker. This study aimed to determine whether YKL-40 could serve as a diagnostic marker for the assessment of liver fibrosis in chronic hepatitis B patients with normal and mildly elevated ALT.

Methods

Six hundred and eighty-five patients with chronic hepatitis B infection were enrolled in this study from October 2013 to March 2016. All patients underwent liver biopsy and then staged based on Ishak histological system. Serum YKL-40 levels were measured by Human Magnetic Luminex Assays.

Results

Among chronic hepatitis B patients with normal and mildly elevated ALT, almost more than 30% of patients have significant liver fibrosis. Serum YKL-40 levels increased significantly in parallel with the progression of fibrosis in patients with ALT less than two times the upper limit of normal range (P < 0.0001). Multivariate analysis revealed that serum YKL-40, hyaluronic acid, PLT, and AST were independently associated with significant fibrosis. We established a novel YKL-40-based fibrosis model for patients with ALT less than two times the upper limit of normal range (ULN). YKL-40 model was superior to APRI, FIB-4, Forns’ index, and Hui model for diagnosis of significant fibrosis in patients with ALT < 2ULN, with AUROCs of 0.786 [95% confidence interval (CI) 0.726–0.846] in the training group, 0.831 (95%CI 0.752–0.910) in the validation group and 0.801 (95%CI 0.753–0.849) in the entire cohort.

Conclusion

Serum YKL-40 is a feasible biomarker of liver fibrosis in chronic hepatitis B patients. YKL-40 model was superior to APRI, FIB-4, Forns’ index and Hui model for diagnosis of significant fibrosis in patients with normal and mildly elevated ALT.

Electronic supplementary material

The online version of this article (10.1007/s15010-018-1136-2) contains supplementary material, which is available to authorized users.