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Systematic review: identifying patients with chronic hepatitis C in need of early treatment and intensive monitoring--predictors and predictive models of disease progression.

ABSTRACT: Advances in hepatitis C therapies have led to increasing numbers of patients seeking treatment. As a result, logistical and financial concerns regarding how treatment can be provided to all patients with chronic hepatitis C (CHC) have emerged.To evaluate predictors and predictive models of histological progression and clinical outcomes for patients with CHC.MEDLINE via PubMed, EMBASE, Web of Science and Scopus were searched for studies published between January 2003 and June 2014. Two authors independently reviewed articles to select eligible studies and performed data abstraction.Twenty-nine studies representing 5817 patients from 20 unique cohorts were included. The outcome incidence rates were widely variable: 16-61% during median follow-up of 2.5-10 years for fibrosis progression; 13-40% over 2.3-14.4 years for hepatic decompensation and 8-47% over 3.9-14.4 years for overall mortality. Multivariate analyses showed that baseline steatosis and baseline fibrosis score were the most consistent predictors of fibrosis progression (significant in 6/21 and 5/21, studies, respectively) while baseline platelet count (significant in 6/13 studies), aspartate and alanine aminotransferase (AST/ALT) ratio, albumin, bilirubin and age (each significant in 4/13 studies) were the most consistent predictors of clinical outcomes. Five studies developed predictive models but none were externally validated.Our review identified the variables that most consistently predict outcomes of patients with chronic hepatitis C allowing the application of risk based approaches to identify patients in need of early treatment and intensive monitoring. This approach maximises effective use of resources and costly new direct-acting anti-viral agents.

SUBMITTER: Konerman MA

PROVIDER: S-EPMC4167918 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Systematic review: identifying patients with chronic hepatitis C in need of early treatment and intensive monitoring--predictors and predictive models of disease progression.

Konerman M A MA Yapali S S Lok A S AS

Alimentary pharmacology & therapeutics 20140828 8

<h4>Background</h4>Advances in hepatitis C therapies have led to increasing numbers of patients seeking treatment. As a result, logistical and financial concerns regarding how treatment can be provided to all patients with chronic hepatitis C (CHC) have emerged.<h4>Aim</h4>To evaluate predictors and predictive models of histological progression and clinical outcomes for patients with CHC.<h4>Methods</h4>MEDLINE via PubMed, EMBASE, Web of Science and Scopus were searched for studies published bet ...[more]

PMID: 25164152

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Project description:UNLABELLED:Existing predictive models of risk of disease progression in chronic hepatitis C have limited accuracy. The aim of this study was to improve upon existing models by applying novel statistical methods that incorporate longitudinal data. Patients in the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis trial were analyzed. Outcomes of interest were (1) fibrosis progression (increase of two or more Ishak stages) and (2) liver-related clinical outcomes (liver-related death, hepatic decompensation, hepatocellular carcinoma, liver transplant, or increase in Child-Turcotte-Pugh score to ?7). Predictors included longitudinal clinical, laboratory, and histologic data. Models were constructed using logistic regression and two machine learning methods (random forest and boosting) to predict an outcome in the next 12 months. The control arm was used as the training data set (n?=?349 clinical, n?=?184 fibrosis) and the interferon arm, for internal validation. The area under the receiver operating characteristic curve for longitudinal models of fibrosis progression was 0.78 (95% confidence interval [CI] 0.74-0.83) using logistic regression, 0.79 (95% CI 0.77-0.81) using random forest, and 0.79 (95% CI 0.77-0.82) using boosting. The area under the receiver operating characteristic curve for longitudinal models of clinical progression was 0.79 (95% CI 0.77-0.82) using logistic regression, 0.86 (95% CI 0.85-0.87) using random forest, and 0.84 (95% CI 0.82-0.86) using boosting. Longitudinal models outperformed baseline models for both outcomes (P?<?0.0001). Longitudinal machine learning models had negative predictive values of 94% for both outcomes. CONCLUSIONS:Prediction models that incorporate longitudinal data can capture nonlinear disease progression in chronic hepatitis C and thus outperform baseline models. Machine learning methods can capture complex relationships between predictors and outcomes, yielding more accurate predictions; our models can help target costly therapies to patients with the most urgent need, guide the intensity of clinical monitoring required, and provide prognostic information to patients.

Project description:BACKGROUND: Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42). METHODS: A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP. RESULTS: Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients. CONCLUSIONS: The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings.

Project description:As alternative indexes of hepatitis B virus (HBV), covalently closed circular DNA (cccDNA) transcriptional activity, hepatitis B surface antigen (HBsAg), hepatitis B core-related antigen (HBcrAg), and peripheral blood RNA known as pgRNA, have been advocated as novel serum markers for prediction of prognosis and treatment response in chronic hepatitis B (CHB). Since the availability of commercial quantitative assays of HBsAg in 2011, HBsAg has been widely used for predicting treatment response of patients with CHB. Patients who received interferon therapy have shown a sharper reduction of HBsAg level than those who received nucleoside drug (NAs) therapy. Upon peginterferon treatment, sustained responders have presented a larger reduction of HBsAg level than the non-responders. An absence of HBsAg decline, together with < 2log reduction in HBV DNA at week 12, can serve as a stopping rule in HBsAg-negative patients infected with genotype D HBV. A sharp reduction of HBsAg titer in the NAs therapy is a predictor of HBsAg clearance in long-term treatment. HBcrAg, which consists of three species of related proteins sharing an identical 149 amino acid sequence, including HbcAg, hepatitis B e antigen (HBeAg), and a truncated 22-kDa precore protein, is still detectable in situations where serum HBV DNA levels become undetectable or HBsAg loss is achieved. Therefore, HBcrAg remains a measurable serum marker to correlate with cccDNA in this situation. The decline in HBcrAg has been observed with NAs therapy and the pattern of decline might provide prognostic information on the risk of HBV post-treatment reactivation. Peripheral blood RNA, which is known as pgRNA, directly derives from cccDNA and reflects intrahepatic cccDNA level. Quantitative pgRNA has been suggested to be helpful in CHB management. However, commercial quantitative assays are lacking. Additionally, the use of simultaneous and continuous clearance of HBV RNA and HBV DNA in serum has been suggested to be a safe stopping rule of NAs therapy for patients with CHB. However, clinical studies of large sample sizes are needed to prove the feasibility and significance of using serum HBV RNA as the assessment standard of antiviral therapy in CHB and the safety of the stopping rule in clinics.

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Systematic review: identifying patients with chronic hepatitis C in need of early treatment and intensive monitoring--predictors and predictive models of disease progression.

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Systematic review: identifying patients with chronic hepatitis C in need of early treatment and intensive monitoring--predictors and predictive models of disease progression.

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