Dynamic personalized prediction of the individual liver-related risk after sustained viral response in HCV patients

Sustained viral response (SVR) significantly improves the prognosis in patients with hepatitis C virus (HCV) chronic infection but does not totally alleviate the risk of liver-related complications (LRC). We aimed to evaluate whether the dynamics of multiple measurements of simple parameters after SVR enable the development of a personalized prediction of prognosis in HCV patients. HCV mono-infected patients who experienced SVR in two prospective cohorts (ANRS CO12 CirVir cohort: derivation set; ANRS CO22 HEPATHER cohort: validation set) were included. The study outcome was LRC, a composite criterion including decompensation of cirrhosis and/or hepatocellular carcinoma. Joint latent class modelling accounting for both biomarker trajectory and event occurrence during follow-up was developed in the derivation set to compute individual dynamic predictions, with further evaluation in the validation set. In the derivation set (n = 695; 50 LRC during the median 3.8 [1.6-7.5] years follow-up), FIB4 was identified as a biomarker associated with LRC occurrence after SVR. Joint modelling used sex and the dynamics of FIB4 and diabetes status to develop a personalized prediction of LRC. In the validation set (n = 7064; 273 LRC during the median 3.6 [2.5-4.9] years follow-up), individual dynamic predictions from the model accurately stratified the risk of LRC. Time-dependent Brier Score showed good calibration that improved with the accumulation of visits, justifying our modelling approach considering both baseline and follow-up measurements. Dynamic modelling using repeated measurements of simple parameters predicts the individual residual risk of LRC and improves personalized medicine after SVR in HCV patients.