Deciding on Interferon-Free Treatment for Chronic Hepatitis C: Updating Liver Stiffness Cut-Off Values to Maximize Benefit.

• Main Authors: Agostino Colli, Mirella Fraquelli, Daniele Prati, Alessia Riva, Alessandra Berzuini, Dario Conte, Alessio Aghemo, Massimo Colombo, Giovanni Casazza
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2016-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC5056756?pdf=render

In a perspective of economic constraints the prioritizing of patients to IFN-free regimens is mainly based on the determination of liver stiffness by transient elastography (TE). Being a continuous variable the interpretation of TE results requires the identification of cut-off values, to date set to maximize diagnostic accuracy even if such values should be better based on more helpful outcome prediction endpoints.To define the TE cut-off values in different clinical scenarios, including new IFN-free regimens, and to balance the clinical benefits versus harms in treated and untreated patients.We assessed the accuracy of TE in staging 728 consecutive HCV patients and the distribution of TE values in 1,001 blood donors. Ten experts quantified the expected harm/benefit ratio for 6 scenarios resulting from 2 stages of liver disease (F≥2 or F≥3) and 3 treatment regimens: PEGIFN+ribavirin, PEGIFN+RBV+first-generation protease inhibitor, and IFN-free regimens. The optimal TE cut-off values were identified using the Metz equation.The estimated mean expected harm/benefit ratio for IFN-free regimens was 1/8.3 in patients with F≥2 and 1/10 in those with F≥3. The resulting optimal cut-off values were respectively 4.5 kPa with sensitivity at 99% and specificity at 12%, and 6.8 kPa with sensitivity at 94% and specificity at 41%. These cut-off values are lower than those maximizing accuracy and allow to reduce the number of false negative results.The optimal TE cut-off values to prioritize patients for IFN-free regimens, are sensibly lower than those used to maximize diagnostic accuracy.