“…Currently, validated tools to identify patients at high risk of HCV treatment failure do not exist and although barriers to HCV treatment initiation have been explored, factors associated with unsuccessful treatment completion have not been elucidated (Morrill et al, 2005;Kattakuzhy et al, 2017). In light of evidence that a large loss of patients throughout the HCV treatment cascade occurs after treatment initiation, early identification and intervention of these patients who initiate but fail treatment may have significant positive economic and public health impact (DeBose-Scarlett et al, 2018). Therefore, in alignment with a broader movement toward cost-effective personalized HCV therapy and identification of valid predictors of response for a given patient (Ferenci, 2004;Ochi et al, 2012;Beinhardt et al, 2013;Petta and Craxi, 2013;Andriulli et al, 2014;Ansaldi et al, 2014;Mathes et al, 2014;Petta et al, 2014;Thompson et al, 2014;Iannazzo et al, 2015;Backus et al, 2016;Jansen et al, 2017;Su et al, 2017;Kouris et al, 2018), our aim was to develop and evaluate a prediction model of treatment failure in patients initiating DAA therapy using demographic and clinical characteristics measured before treatment initiation.…”