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Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

ABSTRACT: Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort.Adults with CHC with ?3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years.The external cohort had a median age of 49.4 years (IQR 44.3-54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2-7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2-7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73-0.83) and 0.76 (95% CI 0.69-0.81).Accurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.

SUBMITTER: Konerman MA

PROVIDER: S-EPMC5673203 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

Konerman Monica A MA Lu Dongxia D Zhang Yiwei Y Thomson Mary M Zhu Ji J Verma Aashesh A Liu Boang B Talaat Nizar N Balis Ulysses U Higgins Peter D R PDR Lok Anna S F ASF Waljee Akbar K AK

PloS one 20171106 11

<h4>Objective</h4>Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort.<h4>Design</h4>Adults with CHC with ≥3 years follow-up and ...[more]

PMID: 29108017

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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:Background & aimsNoninvasive tests cannot differentiate between adjacent stages of fibrosis, which limits assessment of disease progression and regression during therapy. We investigated whether levels of cytokines and extracellular matrix proteins in serum and biopsy samples can be used to determine actual stage of liver fibrosis in patients with chronic hepatitis C (CHC) and in prognosis.MethodsWe collected data from 383 treatment-naive patients with CHC from the Duke Hepatology Clinical Research Database and Biorepository, from 2006 through 2009, for use in the training set. Serum samples were obtained from 100 individuals without CHC (controls). We selected 37 serum biomarkers for customized array analysis by using the SearchLight multiplex sandwich enzyme-linked immunosorbent assay. Data from 434 treatment-naive patients with CHC, which were obtained from the Trent HCV cohort, were used in the validation analysis. Multivariable modeling, marker selection, and validation included randomForest and Obuchowski measures, with independent comparison with FibroSURE.ResultsFour serum markers (levels of hyaluronic acid, vascular cell adhesion molecule 1, alpha-2 macroglobulin, and retinol-binding protein 4) and age associated with fibrosis stage (F0-1, F2-3, or F4); these had Obuchowski measures of 0.85-0.89, with misclassification rates of 38% and 29% in training and validation sets, compared with 50% for the FibroSURE test. In the training set, area under the curve values for the multiplex markers were similar to those from the FibroSURE test: stages F0 vs F1 (0.51 vs 0.53), F1 vs F2 (0.60 vs 0.59), F2 vs F3 (0.69 vs 0.72), and F3 vs F4 (0.51 vs 0.52). Area under the curve values were similar in the validation cohort. In longitudinal analyses of 133 paired biopsies, 9 markers (level of alanine aminotransferase, γ-glutamyltransferase, hyaluronic acid, intracellular adhesion molecule 1, interleukin 4, CXCL10, CXCL9, and vascular cell adhesion molecule 1) were associated with change in the histologic activity index (P values ranging from .000 to .049), and 4 (granulocyte-macrophage colony-stimulating factor, interleukin 12, interleukin 2, and matrix metalloproteinase 13) were associated with a change in fibrosis stage (P values ranging from .001 to .042).ConclusionsWe identified serum biomarkers that can be measured by multiplex enzyme-linked immunosorbent assay to determine levels of fibrosis in patients with CHC, although misclassification is frequent and results are comparable with those from the FibroSURE test. Changes in protein levels in biopsy samples were associated with progression of fibrosis in patients.

Dataset Information

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

Publications

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

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