This preliminary study involving patients with HCV genotype 1 infection who had not had a response to prior therapy showed that a sustained virologic response can be achieved with two direct-acting antiviral agents only. In addition, a high rate of sustained virologic response was achieved when the two direct-acting antiviral agents were combined with peginterferon alfa-2a and ribavirin. (Funded by Bristol-Myers Squibb; ClinicalTrials.gov number, NCT01012895.).

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC.

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OBJECTIVES The aim of this study was to explore the association of serum fibrosis marker levels with the risk of clinical and histological disease progression in a large cohort of patients with chronic hepatitis C (CHC) DESIGN/SETTING 462 prior non-responders to peginterferon and ribavirin enrolled in the randomized phase of the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial had baseline and annual serum samples tested for hyaluronic acid (HA), n-terminal peptide of procollagen type 3, tissue inhibitor of matrix metalloproteinase-1, and YKL-40. OUTCOME MEASURES All patients underwent a pretreatment liver biopsy and follow-up biopsies at years 2 and 4. Histological progression was defined as a ≥ 2 point increase in Ishak fibrosis score in non-cirrhotic patients. Clinical outcomes included development of decompensation, hepatocellular cancer, death, or an increase in the CTP score to ≥ 7. RESULTS Mean patient age was 49.5 years and 39% had histological cirrhosis at entry. Baseline HA, YKL-40 and TIMP-1 levels combined with other laboratory parameters were all significantly associated with clinical outcomes in the 69 (15%) patients with disease progression (p< 0.0001). The best multivariate model to predict clinical outcomes included baseline bilirubin, albumin, INR, and YKL-40 levels. All of the baseline serum fibrosis marker levels were also significantly associated with histological fibrosis progression that developed in 70 (33%) of the 209 non-cirrhotic patients (p < 0.0001). However, baseline HA and platelet counts were best at predicting histological progression (AUC = 0.663). CONCLUSION Pretreatment serum fibrosis marker levels are significantly increased in CHC patients at risk of clinical and histological disease progression. If validated in additional cohorts, measurement of these markers could help identify CHC patients who would benefit from more frequent and intensive monitoring.

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