RESUMO
BACKGROUND AND AIMS: Accurate risk stratification for hepatocellular carcinoma (HCC) after achieving a sustained viral response (SVR) is necessary for optimal surveillance. We aimed to develop and validate a machine learning (ML) model to predict the risk of HCC after achieving an SVR in individual patients. METHODS: In this multicenter cohort study, 1742 patients with chronic hepatitis C who achieved an SVR were enrolled. Five ML models were developed including DeepSurv, gradient boosting survival analysis, random survival forest (RSF), survival support vector machine, and a conventional Cox proportional hazard model. Model performance was evaluated using Harrel' c-index and was externally validated in an independent cohort (977 patients). RESULTS: During the mean observation period of 5.4 years, 122 patients developed HCC (83 in the derivation cohort and 39 in the external validation cohort). The RSF model showed the best discrimination ability using seven parameters at the achievement of an SVR with a c-index of 0.839 in the external validation cohort and a high discriminative ability when the patients were categorized into three risk groups (P <0.001). Furthermore, this RSF model enabled the generation of an individualized predictive curve for HCC occurrence for each patient with an app available online. CONCLUSIONS: We developed and externally validated an RSF model with good predictive performance for the risk of HCC after an SVR. The application of this novel model is available on the website. This model could provide the data to consider an effective surveillance method. Further studies are needed to make recommendations for surveillance policies tailored to the medical situation in each country. IMPACT AND IMPLICATIONS: A novel prediction model for HCC occurrence in patients after hepatitis C virus eradication was developed using machine learning algorithms. This model, using seven commonly measured parameters, has been shown to have a good predictive ability for HCC development and could provide a personalized surveillance system.
RESUMO
BACKGROUND AND AIMS: It remains unclear whether obesity increases the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis C who achieved a sustained virological response (SVR) with antiviral therapy. METHODS: In this multicenter cohort study, we enrolled patients with chronic hepatitis C who achieved SVR with interferon (IFN)-based therapy (IFN group) or direct-acting antiviral (DAA) therapy (DAA group) between January 1, 1990, and December 31, 2018. The patients underwent regular surveillance for HCC. Cumulative incidence of and the risk factors for HCC development after SVR were assessed using the Kaplan-Meier method and Cox proportional hazard regression analysis, respectively. RESULTS: Among 2,055 patients (840 in the IFN group and 1,215 in the DAA group), 75 developed HCC (41 in the IFN group and 34 in the DAA group) during the mean observation period of 4.1 years. The incidence rates of HCC at 1, 2, and 3 years were 1.2, 1.9, and 3.0%, respectively. Multivariate analysis revealed that in addition to older age, lower albumin level, lower platelet count, higher alpha-fetoprotein level, and absence of dyslipidemia, obesity (body mass index ≥25 kg/m2) and heavy alcohol consumption (≥60 g/day) were independent risk factors for HCC development, with adjusted hazard ratio (HR) of 2.53 (95% confidence interval [CI]: 1.51-4.25) and 2.56 (95% CI: 1.14-5.75), respectively. The adjusted HR was not significant between the 2 groups (DAA vs. IFN; HR 1.19, 95% CI: 0.61-2.33). CONCLUSIONS: Obesity and heavy alcohol consumption increased the risk of HCC development after SVR.