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

Abstract

AbstractSustained 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.