Predictors for early identification of hepatitis C virus infection

Abstract

Hepatitis C virus (HCV) infection can cause permanent liver damage and hepatocellular carcinoma, and deaths related to HCV deaths have recently increased. Chronic HCV infection is often undiagnosed such that the virus remains infective and transmissible. Identifying HCV infection early is essential for limiting its spread, but distinguishing individuals who require further HCV tests is very challenging. Besides identifying high-risk populations, an optimal subset of indices for routine examination is needed to identify HCV screening candidates. Therefore, this study analyzed data from 312 randomly chosen blood donors, including 144 anti-HCV-positive donors and 168 anti-HCV-negative donors. The HCV viral load in each sample was measured by real-time polymerase chain reaction method. Receiver operating characteristic curves were used to find the optimal cell blood counts and thrombopoietin measurements for screening purposes. Correlations with values for key indices and viral load were also determined. Strong predictors of HCV infection were found by using receiver operating characteristics curves to analyze the optimal subsets among red blood cells, monocytes, platelet counts, platelet large cell ratios, and mean corpuscular hemoglobin concentrations. Sensitivity, specificity, and area under the receiver operator characteristic curve (P < 0. 0001) were 75.6%, 78.5%, and 0.859, respectively.