Kuo-Chin Chang1*, Tsung-Hui Hu1, Ming-Tsung Lin1, Yi-Ying Wu2, Yi-Hao Yen1, Ming-Chao Tsaia1, Chien-Hung Chen1 and Jing-HoungWang1
1Department of Internal Medicine, Chang Gung University College of Medicine, Taiwan
2Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan
Background: Chronic hepatitis C is a major health problem in Taiwan, treatment is costly and has multiple side effects, identifying the predictors of relapse of the disease after achieving Rapid Virological Responses (RVR) to PEG Interferon/Ribavirin (PEG-IFN/RBV) treatment will help in better selection of patients and avoidance of unnecessary side effects.
Methods: This retrospective study was done on 818 chronic HCV patients, treatment-naive patients receiving PEG-IFN/RBV. Demographic and laboratory data were analyzed against relapse by univariate and logistic regression analysis. The feasibility of predicting treatment failure was using the baseline to build a risk scoring model for HCV patients with a RVR.
Results: The multi-variable logistic regression analysis showed that independent predictors of relapse were AST ≤ 40 IU/L, low platelet count, HCV genotype 1, high viral load, and clinical liver cirrhosis. A scoring model for prediction of relapse was calculated based on the regression coefficients of each predictor. The ROC curve for prediction of relapse by the score showed that the Area under the Curve (AUC) is 71.2. A cut off value of 15% had 73.49% sensitivity, 60.62% specificity, 92.57% negative predictive value and 25.52% positive predictive value.
Conclusion: A scoring model using AST ≤ 40 IU/L, low platelet count, HCV genotype 1, high viral load, and clinical liver cirrhosis during therapy can efficiently predict relapse.
Risk scoring; Hepatitis C virus; PEG-interferon; Relapse; Rapid virological response
Chang K-C, Hu T-H, Lin M-T, Wu Y-Y, Yen Y-H, Tsaia M-C, et al. A Predictive Scoring Model for Relapse in Chronic HCV Patients with Rapid Virologic Response to PEG Interferon/Ribavirin Treatment. Ann Digest Liver Dis. 2019; 2(1): 1016.