The Relationship between Serum Bilirubin and Elevated Fibrotic Indices among HBV Carriers: A Cross-Sectional Study of a Chinese Population

Epidemiology of Hepatitis C Virus in HIV Patients from West Mexico: Implications for Controlling and Preventing Viral Hepatitis

The complex epidemiology of hepatitis C virus (HCV) infection among human immunodeficiency virus (HIV) patients in West Mexico remains poorly understood. Thus, this study aimed to investigate the HCV prevalence, HCV-associated risk factors, and HCV genotypes/subtypes and assess their impacts on liver fibrosis in 294 HIV patients (median age: 38 years; 88.1% male). HCV RNA was extracted and amplified by PCR. Hepatic fibrosis was assessed using three noninvasive methods: transient elastography (TE), the aspartate aminotransferase (AST)-to-platelets ratio index score (APRI), and the fibrosis-4 score (FIB4). Patients with liver stiffness of ≥9.3 Kpa were considered to have advanced liver fibrosis. HCV genotypes/subtypes were determined by line probe assay (LiPA) or Sanger sequencing. The prevalence of HIV/HCV infection was 36.4% and was associated with injection drug use (odds ratio (OR) = 13.2; 95% confidence interval (CI) = 5.9-33.6; p < 0.001), imprisonment (OR = 3.0; 95% CI = 1.7-5.4; p < 0.001), the onset of sexual life (OR = 2.6; 95% CI = 1.5-4.5; p < 0.001), blood transfusion (OR = 2.5; 95% CI = 1.5-4.2; p = 0.001), tattooing (OR = 2.4; 95% CI = 1.4-3.9; p = 0.001), being a sex worker (OR = 2.3; 95% CI = 1.0-5.4; p = 0.046), and surgery (OR = 1.7; 95% CI = 1.0-2.7; p = 0.042). The HCV subtype distribution was 68.2% for 1a, 15.2% for 3a, 10.6% for 1b, 3.0% for 2b, 1.5% for 2a, and 1.5% for 4a. The advanced liver fibrosis prevalence was highest in patients with HIV/HCV co-infection (47.7%), especially in those with HCV subtype 1a. CD4+ counts, albumin, direct bilirubin, and indirect bilirubin were associated with liver fibrosis. In conclusion, HCV infection had a significant impact on the liver health of Mexican HIV patients, highlighting the need for targeted preventive strategies in this population.

Sociodemographic and medical characteristics of liver cirrhosis deaths in a Ghanaian tertiary hospital

Objective

Cirrhosis is common in Ghana because of its high risk factors prevalence. However, information on cirrhosis in Ghana is lacking. This study aimed to study the clinical, and laboratory characteristics of cirrhotic patients in a tertiary hospital in Ghana.

Design

This was a retrospective study of sociodemographic characteristics, symptoms and signs, biochemical and fibrotic indices, treatments, and complications data of 247 patients with cirrhosis who died on admission.

Setting

This study was carried out at the Gastroenterology Unit of the Korle-Bu Teaching Hospital, Ghana.

Results

Two-thirds (68.0%) of the patients were within 30 to 60 years, with more than half (73.7%) being males. The most common aetiological factors among the patients were Hepatitis B virus infection (53.8%), alcohol use (31.6%) and Hepatitis C virus infection (4.9%). More than half (55.0%) of the patients reported late for admission, and 67.2% died within the first two weeks of admission. The most common clinical feature was abdominal distension (61.1% of patients), and the least was upper-abdominal mass (14.2%). The levels of most liver test parameters were elevated, fibrotic indices were high, and haemoglobin and albumin levels were reduced. More than half (53.8%) of the patients were in Child Pugh class B. The most common complication was hepatic encephalopathy; the least was hepato-renal syndrome. Definite treatment for complications of cirrhosis was lacking.

Conclusion

Deaths from cirrhosis at the hospital were mostly of young males with chronic hepatitis B infection. Implementation of hepatitis B prevention and treatment guidelines can help reduce cirrhosis deaths.

Funding

None declared.

CXCL8, CXCL9, CXCL10, and CXCL11 as biomarkers of liver injury caused by chronic hepatitis B

Background

Chronic hepatitis B (CHB) remains a significant global health problem, leading to recurrent inflammation and liver-damaging diseases such as fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Currently, although diagnostic markers for CHB are well established, the indicators for predicting liver injury caused by hepatitis B virus (HBV) infection still need to be further explored. Thus, the identification of credible infectious indicators is urgently needed to facilitate timely clinical intervention and avoid the progression of disease malignancy.

Methods

The Gene Expression Omnibus (GEO) database GSE83148 data set was used to explore the hub genes for HBV infection. The quantitative real-time polymerase chain reaction (qPCR) was used to identify the impact of HBV infection on the expression of hub gene at the cell level. At the same time, serum samples and clinical information were collected from healthy, HBV-free and CHB patients. The enzyme-linked immunosorbent assay (ELISA) was used to verify the results of cell experiments and Pearson correlation analysis was used to clarify hub genes correlation with HBV infection indicators and liver injury-related indicators. Finally, the Gene Expression Profiling Interactive Analysis (GEPIA) database was used to analyze the differences in the expression of hub gene in liver injury diseases.

Results

Chemokine (C-X-C motif) ligand (CXCL)8, CXCL9, CXCL10, and CXCL11 were identified as hub genes in HBV infection. After HBV infection, the expression of the four chemokines was significantly increased and the concentrations secreted into serum were also increased. Moreover, the four chemokines were significantly correlated with HBV infection-related indicators and liver injury-related indicators, which were positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hepatitis B e antigen (HBeAg), and negatively correlated with AST/ALT ratio and hepatitis B core antibody (HBcAb). In addition, the expression of CXCL9, CXCL10, and CXCL11 in HCC tissues was significantly higher than in normal tissues.

Conclusion

Using a combination of bioinformatics, cell experiments, and clinical correlation analysis, this study showed that CXCL8, CXCL9, CXCL10, and CXCL11 can be used as serum biomarkers to forecast liver injury caused by HBV infection.

Elevated Levels of IL-33, IL-17 and IL-25 Indicate the Progression from Chronicity to Hepatocellular Carcinoma in Hepatitis C Virus Patients

Hepatitis C virus (HCV) is one of the most epidemic viral infections in the world. Three-quarters of individuals infected with HCV become chronic. As a consequence of persistent inflammation, a considerable percentage of chronic patients progress to liver fibrosis, cirrhosis, and finally hepatocellular carcinoma. Cytokines, which are particularly produced from T-helper cells, play a crucial role in immune protection against HCV and the progression of the disease as well. In this study, the role of interleukins IL-33, IL-17, and IL-25 in HCV patients and progression of disease from chronicity to hepatocellular carcinoma will be characterized in order to use them as biomarkers of disease progression. The serum levels of the tested interleukins were measured in patients suffering from chronic hepatitis C (CHC), hepatocellular carcinoma (HCC), and healthy controls (C), and their levels were correlated to the degree of liver fibrosis, liver fibrosis markers and viral load. In contrast to the IL-25 serum level, which increased in patients suffering from HCC only, the serum levels of both IL-33 and IL-17 increased significantly in those patients suffering from CHC and HCC. In addition, IL-33 serum level was found to increase by liver fibrosis progression and viral load, in contrast to both IL-17 and IL-25. Current results indicate a significant role of IL-33 in liver inflammation and fibrosis progress in CHC, whereas IL-17 and IL-25 may be used as biomarkers for the development of hepatocellular carcinoma.

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.

A Powerful Nomogram Based on the Novel D-Index to Predict Prognosis After Surgical Resection of Hepatocellular Carcinoma

Purpose

Conventional staging and scoring systems such as the Tumor, Node, and Metastasis; Cancer of the Liver Italian Program; Barcelona Clinic Liver Cancer; and Okuda have failed to predict overall survival (OS) in patients with resected primary hepatocellular carcinoma. Thus, we aimed to establish a novel D-index and nomogram to improve prognostic accuracy.

Patients and Methods

We selected 396 patients who underwent liver resection between January 2007 and February 2015 at the First Affiliated Hospital of Wenzhou Medical University. These patients were randomly divided into the training and validation groups in a ratio of 7:3.

Results

We generated a nomogram using five independent risk factors, including the D-index (calculated by total bilirubin × tumor size/the ratio of fat-to-muscle area ^0.5) in the training set. The predictive performance of the nomogram was similar in both the training and validation cohorts according to the concordance index. The nomogram demonstrated the strongest predictive power for 1-year, 3-year, and 5-year OS, with the area under the receiving operating characteristic curve being 0.8486, 0.7785, and 0.752, respectively. The calibration curves exhibited stable capabilities in both cohorts. The stratification of the Kaplan-Meier curve was significant (P < 0.001).

Conclusion

The associated nomogram of the D-index demonstrated a powerful and accurate predictive ability for OS in patients with primary hepatocellular carcinoma.

Nomograms to predict 2-year overall survival and advanced schistosomiasis-specific survival after discharge: a competing risk analysis

Background

The prognosis of patients with advanced schistosomiasis is poor. Pre-existing prognosis studies did not differentiate the causes of the deaths. The objectives were to evaluate the 2-year overall survival (OS) and advanced schistosomiasis-specific survival (ASS) in patients with advanced schistosomiasis after discharge through competing risk analysis and to build predictive nomograms.

Methods

Data was extracted from a previously constructed database from Hubei province. Patients were enrolled from September 2014 to January 2015, with follow up to January 2017. OS and ASS were primary outcome measures. Nomograms for estimating 2-year OS and ASS rates after discharge were established based on univariate and multivariate Cox regression model and Fine and Gray’s model. Their predictive performances were evaluated using C-index and validated in both internal and external validation cohorts.

Results

The training cohort included 1487 patients with advanced schistosomiasis. Two-year mortality rate of the training cohort was 8.27% (123/1487). Competing events accounted for 26.83% (33/123). Older age, splemomegaly clinical classification, abnormal serum DBil, AST, ALP and positive HBsAg were significantly associated with 2-year OS. Older age, splemomegaly clinical classification, abnormal serum AST, ALP and positive HBsAg were significantly associated with 2-year ASS. The established nomograms were well calibrated, and had good discriminative ability, with a C-index of 0.813 (95% CI 0.803–0.823) for 2-year OS prediction and 0.834 (95% CI 0.824–0.844) for 2-year ASS prediction. Their predictive performances were well validated in both internal and external validation cohorts.

Conclusion

The effective predictors of 2-year OS and ASS were discovered through competing risk analysis. The nomograms could be used as convenient predictive tools in clinical practice to guide follow-up and aid accurate prognostic assessment.

Derivation and external validation of a model to predict 2-year mortality risk of patients with advanced schistosomiasis after discharge

To date, no risk prediction tools have been developed to identify high mortality risk of patients with advanced schistosomiasis within 2 years after discharge. We aim to derive and validate a risk prediction model to be applied in clinical practice. The risk prediction model was derived from 1487 patients from Jingzhou and externally validated by 723 patients of Huangshi, two prefecture-level cities in Hubei province, China (from September 2014 to January 2015, with follow-up to January 2017). The baseline variables were collected. The mean age [SD] was 62.89 [10.38] years for the derivation cohort and 62.95 [12.22] years for the external validation cohort. The females accounted for 36.3% and 43.7% of the derivation and validation cohorts, respectively. 8.27% patients (123/1487) in the derivation cohort and 7.75% patients (56/723) in the external validation cohort died within 2 years after discharge. We constructed 4 models based on the 7 selected variables: age, clinical classification, serum direct bilirubin (DBil), aspartate aminotransferase (AST), alkaline phosphatase (ALP), hepatitis B surface antigen (HBsAg), alpha fetoprotein (AFP) at admission. In the external validation cohort, the multivariate model including 7 variables had a C statistic of 0.717 (95% CI, 0.646–0.788) and improved integrated discrimination improvement (IDI) value and net reclassification improvement (NRI) value compared to the other reduced models.

Therefore, a multivariate model was developed to predict the 2-year mortality risk for patients with advanced schistosomiasis after discharge. It could also help guide follow-up, aid prognostic assessment and inform resource allocation.

A sample model established by S-index predicting overall survival after curative resection of primary hepatocellular carcinoma

Purpose

Prognostic prediction after curative resection of primary hepatocellular carcinoma (PHCC) remains an arduous task. The S-index calculated from γ-glutamyl transpeptidase, albumin, and platelets is reported to predict the severity of liver fibrosis. We constructed a nomogram for predicting the survival probability of PHCC based on a new indicator, the S-index, combined with other routine clinical parameters.

Patients and methods

We selected 490 patients with PHCC postradical surgery at the First Affiliated Hospital of Wenzhou Medical University between January 2007 and January 2014. The subjects were randomly allocated into the training cohort and the validation cohort in the ratio 7:3 by the digital method. Important variables screened by univariate analysis were included in multivariate analysis to obtain independent risk factors for predicting the prognosis of PHCC. The construction of the nomogram was based on Cox proportional hazard regression models. The concordance index (C-index) was used in the nomogram for evaluating the model performance for prognosis. We drew time-dependent receiver operating characteristic curves to compare our model with other staging systems.

Results

The nomogram based on six independent risk factors after multivariate analyses had good predictive power after radical surgery of PHCC. In the training and validation groups, the C-index of the nomogram was highly consistent for evaluating survival from PHCC. Compared with the traditional scoring system, the areas under time-dependent receiver operating characteristic curves were 0.7382, 0.7293, and 0.7520 for 1-, 3-, and 5-year overall survival, respectively. In summary, the nomogram showed excellent results in terms of prognosis of PHCC.

Conclusion

Based on the S-index and the other clinical indicators, we developed a precise nomogram that predicts the survival probability of patients with PHCC after radical surgery. This tool can provide effective information for surgeons and patients.