Predicting Treatment Failure for Initiators of Hepatitis C Virus Treatment in the era of Direct-Acting Antiviral Therapy.

Introduction: Hepatitis C virus (HCV), the leading cause of advanced liver disease, has enormous economic burden. Identification of patients at risk of treatment failure could lead to interventions that improve cure rates.
Objectives: Our goal was to develop and evaluate a prediction model for HCV treatment failure.
Methods: We analyzed HCV patients initiating direct-acting antiviral therapy at four United States institutions. Treatment failure was determined by lack of sustained virologic response (SVR) 12 weeks after treatment completion. From 20 patient-level variables collected before treatment initiation, we identified a subset associated with treatment failure in bivariate analyses. In a derivation set, separate predictive models were developed from 100 bootstrap samples using logistic regression. From the 100 models, variables were ranked by frequency of selection as predictors to create four final candidate models, using cutoffs of ≥80%, ≥50%, ≥40%, and all variables. In a validation set, predictive performance was compared across models using area under the receiver operating characteristic curve.
Results: In 1,253 HCV patients, overall SVR rate was 86.1% (95% CI = 84.1%, 88.0%). The AUCs of the four final candidate models were: ≥80% = 0.576; ≥50% = 0.605; ≥40% = 0.684; all = 0.681. The best performing model (≥40%) had significantly better predictive ability than the ≥50% (p = 0.03) and ≥80% models (p = 0.02). Strongest predictors of treatment failure were older age, history of hepatocellular carcinoma, and private (vs. government) insurance.
Conclusion: This study highlighted baseline factors associated with HCV treatment failure. Treatment failure prediction may facilitate development of data-driven clinical tools to identify patients who would benefit from interventions to improve SVR rates.