Statistical Prediction Model for Relapse Rate in Chronic Hepatitis C Patients Treated with Conventional Interferon and Ribavirin Therapy

Objective: To determine the significantly associated factors with the relapse rate in chronic hepatitis C patients treated with conventional interferon and their predictive strength through the regression model.
Material & Methods: In a retrospective analysis of 244 patients, result of PCR, gender, fatty liver, diabetes, abnormal ALT at start and end of treatment were the qualitative variables. Age, weight, ALT at start and end of treatment, hemoglobin, platelets and WBC at start of treatment were quantitative variables. Bivariate, multivariate analysis and odds ratio were computed to verify statistically significant association with relapse rate by running binary logistic regression model.
Results: Out of total 244 patients there were 54.1% male and 45.9% female. Eighty two (33.6%) patients had weight > 70 Kg, 30 (12.3%) had fatty liver, 18 were (7.4%) diabetic, 12 (4.9%) had normal ALT at start of therapy and 140 (57.4%) had abnormal ALT at the end. Eighty four (34.4%) patients relapsed while 160 (65.6%) maintained SVR after 6 month to 2 years of completion. In bivariate analysis, age, weight, fatty liver, high fever, decrease and increase in Hb were found significant. The binary logistic regression revealed the significant association of weight (OR=84.813; p=0.000), high fever (OR=4.478; p= 0.038) and Hb increase at 1st month (OR=0.037; p=0.013) with relapse rate. Nagelkerke R Square and Cox & Snell R Square statistics explained 71.1% and 51.1% variation in the model respectively and 93.1% area under the curve gave it very good prediction strength.
Conclusion: The relapse rate to conventional interferon and ribavirin treatment is high in Pakistan. The assessment of predictors of response, like body weight may help in individualizing the treatment, patient selection and to decrease an ever expanding pool of non-responders and re-lapsers. Hence, our prediction model can help us to predict the chances of being relapse in advance