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Profibrogenic chemokines and viral evolution predict rapid progression of hepatitis C to cirrhosis.

ABSTRACT: Chronic hepatitis C may follow a mild and stable disease course or progress rapidly to cirrhosis and liver-related death. The mechanisms underlying the different rates of disease progression are unknown. Using serial, prospectively collected samples from cases of transfusion-associated hepatitis C, we identified outcome-specific features that predict long-term disease severity. Slowly progressing disease correlated with an early alanine aminotransferase peak and antibody seroconversion, transient control of viremia, and significant induction of IFN-? and MIP-1?, all indicative of an effective, albeit insufficient, adaptive immune response. By contrast, rapidly progressive disease correlated with persistent and significant elevations of alanine aminotransferase and the profibrogenic chemokine MCP-1 (CCL-2), greater viral diversity and divergence, and a higher rate of synonymous substitution. This study suggests that the long-term course of chronic hepatitis C is determined early in infection and that disease severity is predicted by the evolutionary dynamics of hepatitis C virus and the level of MCP-1, a chemokine that appears critical to the induction of progressive fibrogenesis and, ultimately, the ominous complications of cirrhosis.

SUBMITTER: Farci P

PROVIDER: S-EPMC3437836 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Profibrogenic chemokines and viral evolution predict rapid progression of hepatitis C to cirrhosis.

Farci Patrizia P Wollenberg Kurt K Diaz Giacomo G Engle Ronald E RE Lai Maria Eliana ME Klenerman Paul P Purcell Robert H RH Pybus Oliver G OG Alter Harvey J HJ

Proceedings of the National Academy of Sciences of the United States of America 20120724 36

Chronic hepatitis C may follow a mild and stable disease course or progress rapidly to cirrhosis and liver-related death. The mechanisms underlying the different rates of disease progression are unknown. Using serial, prospectively collected samples from cases of transfusion-associated hepatitis C, we identified outcome-specific features that predict long-term disease severity. Slowly progressing disease correlated with an early alanine aminotransferase peak and antibody seroconversion, transien ...[more]

PMID: 22829669

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Project description:ImportanceDeep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.ObjectiveTo examine whether deep learning recurrent neural network (RNN) models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC).Design, setting, and participantsThis prognostic study included 48 151 patients with hepatitis C virus (HCV)-related cirrhosis in the national Veterans Health Administration who had at least 3 years of follow-up after the diagnosis of cirrhosis. Patients were identified by having at least 1 positive HCV RNA test between January 1, 2000, to January 1, 2016, and were followed up from the diagnosis of cirrhosis to January 1, 2019, for the development of incident HCC. A total of 3 models predicting HCC during a 3-year period were developed and compared, as follows: (1) logistic regression (LR) with cross-sectional inputs (cross-sectional LR); (2) LR with longitudinal inputs (longitudinal LR); and (3) RNN with longitudinal inputs. Data analysis was conducted from April 2018 to August 2020.ExposuresDevelopment of HCC.Main outcomes and measuresArea under the receiver operating characteristic curve, area under the precision-recall curve, and Brier score.ResultsDuring a mean (SD) follow-up of 11.6 (5.0) years, 10 741 of 48 151 patients (22.3%) developed HCC (annual incidence, 3.1%), and a total of 52 983 samples (51 948 [98.0%] from men) were collected. Patients who developed HCC within 3 years were older than patients who did not (mean [SD] age, 58.2 [6.6] years vs 56.9 [6.9] years). RNN models had superior mean (SD) area under the receiver operating characteristic curve (0.759 [0.009]) and mean (SD) Brier score (0.136 [0.003]) than cross-sectional LR (0.689 [0.009] and 0.149 [0.003], respectively) and longitudinal LR (0.682 [0.007] and 0.150 [0.003], respectively) models. Using the RNN model, the samples with the mean (SD) highest 51% (1.5%) of HCC risk, in which 80% of all HCCs occurred, or the mean (SD) highest 66% (1.2%) of HCC risk, in which 90% of all HCCs occurred, could potentially be targeted. Among samples from patients who achieved sustained virologic response, the performance of the RNN models was even better (mean [SD] area under receiver operating characteristic curve, 0.806 [0.025]; mean [SD] Brier score, 0.117 [0.007]).Conclusions and relevanceIn this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.

Dataset Information

Profibrogenic chemokines and viral evolution predict rapid progression of hepatitis C to cirrhosis.

Publications

Profibrogenic chemokines and viral evolution predict rapid progression of hepatitis C to cirrhosis.

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