Prediction of Health Utility Scores in Patients with Chronic Hepatitis C Using the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV)

The Global Burden of Liver Disease

Chronic liver disease (CLD) and its associated complications (cirrhosis and liver cancer) cause significant mortality, morbidity, and economic burden. Published data from the World Health Organization and/or the Global Burden of Disease show that the burden of CLD is large and increasing, primarily owing to the increasing burden of nonalcoholic fatty liver disease and alcohol-related liver disease (ALD). Middle Eastern, Northern African, and Asian regions of the globe are most affected by hepatitis B and hepatitis C virus. Furthermore, Middle Eastern and North African regions also are affected by nonalcoholic fatty liver disease, and Eastern European, West African, and Central Asian regions are affected by ALD. In this context, the rate of increase for cirrhosis is highest in the Middle East, as well as in middle high and high sociodemographic index (SDI) regions. On the other hand, the highest SDI countries are experiencing increasing rates of hepatocellular carcinoma (HCC). Assessing HCC burden based on country and etiology shows that China, Korea, India, Japan, and Thailand have the highest hepatitis B virus-related HCC cases, while China, Japan, and the United States have the highest hepatitis C virus-related HCC cases. Additionally, the United States has the highest ALD-related HCC cases, while India, the United States, and Thailand have the highest nonalcoholic steatohepatitis-related HCC cases. Although the burden of CLD is increasing globally, regions of the world are impacted differently as a result of a number of sociodemographic factors.

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

Cost-utility analysis of four WHO-recommended sofosbuvir-based regimens for the treatment of chronic hepatitis C in sub-Saharan Africa

Background

Although direct-acting antivirals (DAA) have become standard care for patients with chronic hepatitis C worldwide, there is no evidence for their value for money in sub-Saharan Africa. We assessed the cost-effectiveness of four sofosbuvir-based regimens recommended by the World Health Organization (WHO) in Cameroon, Côte d’Ivoire and Senegal.

Methods

Using modelling, we simulated chronic hepatitis C progression with and without treatment in hypothetical cohorts of patients infected with the country’s predominant genotypes (1, 2 and 4) and without other viral coinfections, history of liver complication or hepatocellular carcinoma. Using the status-quo ‘no DAA treatment’ as a comparator, we assessed four regimens: sofosbuvir-ribavirin, sofosbuvir-ledipasvir (both recommended in WHO 2016 guidelines and assessed in the TAC pilot trial conducted in Cameroon, Côte d’Ivoire and Senegal), sofosbuvir-daclatasvir and sofosbuvir-ledipasvir (two pangenotypic regimens recommended in WHO 2018 guidelines). DAA effectiveness, costs and utilities were mainly estimated using data from the TAC pilot trial. Secondary data from the literature was used to estimate disease progression probabilities with and without treatment. We considered two DAA pricing scenarios: S1) originator prices; S2) generic prices. Uncertainty was addressed using probabilistic and deterministic sensitivity analyses and cost-effectiveness acceptability curves.

Results

With slightly higher effectiveness and significantly lower costs, sofosbuvir/velpatasvir was the preferred DAA regimen in S1 with incremental cost-effectiveness ratios (ICERs) ranging from US$526 to US$632/QALY. At the cost-effectiveness threshold (CET) of 0.5 times the 2017 country’s per-capita gross domestic product (GDP), sofosbuvir/velpatasvir was only cost-effective in Senegal (probability > 95%). In S2 at generic prices, sofosbuvir/daclatasvir was the preferred regimen due to significantly lower costs. ICERs ranged from US$139 to US$216/QALY according to country i.e. a 95% probability of being cost-effective. Furthermore, this regimen was cost-effective (probability> 95%) for all CET higher than US$281/QALY, US$223/QALY and US$195/QALY in Cameroon, Côte d’Ivoire and Senegal, respectively, corresponding to 0.14 (Côte d’Ivoire and Senegal) and 0.2 (Cameroon) times the country’s per-capita GDP.

Conclusions

Generic sofosbuvir/daclatasvir is very cost-effective for treating chronic hepatitis C in sub-Saharan Africa. Large-scale use of generics and an increase in national and international funding for hepatitis C treatment must be priorities for the HCV elimination agenda.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07289-0.

Long-term Patient-Centered Outcomes in Cirrhotic Patients With Chronic Hepatitis C After Achieving Sustained Virologic Response

BACKGROUND & AIMS

Achieving sustained virologic response (SVR) among patients with hepatitis C virus (HCV) leads to patient reported outcome (PRO) improvement. We aimed to assess the long-term post-SVR PRO trends in HCV patients with cirrhosis.

METHODS

Patients with HCV and cirrhosis treated in clinical trials with direct acting antiviral agents (DAAs) who achieved SVR-12 were prospectively enrolled in a long-term registry (clinicaltrials.gov #NCT02292706). PROs were collected every 24 weeks using the Short Form-36v2 (SF-36), CLDQ-HCV, and WPAI-HCV.

RESULTS

Pre-treatment baseline data were available for 854 cirrhotic patients who achieved SVR after DAAs. Of these, 730 had compensated (CC) and 124 had decompensated cirrhosis (DCC) before treatment- patients with DCC reported severe impairment in their PROs in comparison to CC patients (by mean -5% to -16% of a PRO range size; p < .05 for 16 out of 20 studied PROs]. After achieving SVR and registry enrollment, significant PRO improvements were noted from pre-treatment levels in 11/20 domains for those with DCC (+4% to +21%) and 19/20 PRO domains in patients with CC (+3% to +17%). Patients with baseline DCC had higher rates of hepatocellular carcinoma and mortality (P < .05). In patients with CC, the PRO gains persisted up to 168 weeks (3.5 years) of registry follow-up. In patients with DCC, the improvements lasted for at least 96 weeks but a declining trend after year 2.

CONCLUSIONS

Patients with HCV cirrhosis experience severe PRO impairment at baseline with sustainable improvement after SVR. Though those with DCC experience improvement, there is a decline after 2 years.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

A Comprehensive Review of Patient-reported Outcomes in Patients With Chronic Liver Diseases

Chronic liver disease (CLD) is a global health problem with chronic viral hepatitis, alcohol-related liver disease, and nonalcoholic fatty liver disease being important causes of mortality. Besides its clinical burden, patients with CLD also suffer from impairment of their health-related quality of life and other patient-reported outcomes (PRO). In this context, a combination of both clinical and PROs will allow assessment of the comprehensive burden of liver disease on patients. PROs cannot be observed directly and must be assessed by validated questionnaires or tools. Various tools have been developed to accurately measure PROs in patients with CLD, including generic and disease-specific questionnaires such as Short Form-36, Chronic Liver Disease Questionnaire and its subtypes. It is important to note that PRO instruments can be used to appreciate the impact of the natural history of CLD or of treatment on patients' experiences. This review summarizes PRO assessment in different types of liver disease and different tools useful to investigators and clinicians who are interested in this aspect of patients' experience.