External validation of the nomogram for individualized prediction of hepatocellular carcinoma occurrence in patients with hepatitis C virus–related compensated cirrhosis
Authors: Astrid Marot, Hélène Vandenbulcke, Jean‐François Knebel, Christopher Doerig, Christophe Moreno, Pierre Deltenre
Journal: Hepatology (2016)

Potential conflict of interest: Nothing to report. TO THE EDITOR: We read the article of Ganne et al. enabling an individualized prediction of the risk of hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV)‐related compensated cirrhosis with great interest. The authors proposed a score based on four variables (age, past excessive alcohol intake, platelet count, and gamma‐glutamyl transpeptidase [GGT] serum level) and a time‐dependent covariate (sustained virological response).1 External validation is mandatory before this score becomes widely applied. We took the opportunity of a recent prospective study in patients with HCV‐related compensated cirrhosis to evaluate the usefulness of such model to predict HCC occurrence.2 In our study, three variables were associated with risk of HCC using the Fine and Gray multivariate model (competing risk regression): age (hazard ratio [HR] = 1.06; 95% confidence interval [CI], 1.02‐1.09; P < 0.001); viral eradication (HR = 0.12; 95% CI, 0.02‐0.90; P = 0.04); and alcohol intake during follow‐up (HR = 3.43; 95% CI, 1.49‐7.92; P = 0.004). Of note, we identified current alcohol intake, rather than past alcohol intake, as an independent risk factor for HCC. Platelet count used as a continuous variable did not reach statistical significance (HR = 0.99; 95% CI, 0.99‐1.00; P = 0.065). GGT levels were not available. We categorized age using a cutoff of 50 years, and we included platelet count using two cutoffs (100 and 150 Giga/mm3). We built two models. In the first, the weight of each factor was given by the root of HR rounded off to the nearer integer. The root of HR was chosen to avoid giving too much weight for a given variable. In the second model, we used the same variables' weight as Ganne et al.'s model. The population was stratified into three groups according to risk of HCC at 5 years. In the first model, risks of HCC were 1.5% (95% CI, 0.0‐4.5), 12.3% (4.2‐20.4), and 50.1% (31.0‐69.2) in patients with the lowest, intermediate, and highest score (P < 0.001; Fig. 1A). In the second model, the corresponding risks were 2.9% (0.0‐8.5), 8.6% (2.8‐14.4), and 50.1% (31.0‐69.2), respectively (P < 0.001; Fig. 1B).Figure 1: Incidence of HCC according to the predictive score. (A) Model built with variables' weights derived from our multivariate analysis. (B) Model built with the same variables' weight as in Ganne et al.'s studyThus, we confirmed that a personalized prediction for risk of HCC occurrence can be made using easily available variables in an independent cohort of patients with HCV‐related compensated cirrhosis. However, the weight of each factor differed between Ganne et al.'s study and our own. Hence, the best model for predicting the occurrence of HCC has yet to be defined. Given that alcohol intake and viral eradication can change over time, it is likely that future models should be regularly updated for an accurate HCC risk prediction. Further studies should evaluate whether patients at the highest risk for HCC development could benefit from a more‐intensive surveillance.