Prediction models for feverishness developed during interferon therapy of chronic hepatitis C patients.

Pegylated interferon (peginterferon) and ribavirin combination therapy is used extensively for therapy of chronic hepatitis C. Most patients that receive this therapy are known to develop influenza-like symptoms with fever and headache. Therefore, we attempted to construct a multiple-regression model to predict the intensity of feverishness from the profiles of such patients. A retrospective survey of the medical charts of patients with chronic hepatitis C that have been on peginterferon-alpha-2b and ribavirin combination therapy was performed. Body temperatures of patients at 8.5 h after receiving interferon injection on day one of therapy were the objective variables. Patients' profiles such as sex, age, and blood test results before the injection were defined as explanatory variables. Genetic algorism with leave-one-out cross-validation as selection pressure was applied in the selection of variables. The final model for prediction was determined by bootstrap validation. As a result, a significant multiple-regression model including sex, BUN, and leukocyte count as descriptors was constructed. The prediction of patients with severe fever in the model equation is of some help regarding the proper use of antipyretics in interferon therapy.