Predictors for Early Identification of Hepatitis C Virus Infection

Abstract EN

Hepatitis C virus (HCV) infection can cause permanent liver damage andhepatocellular carcinoma, and deaths related to HCV deaths have recentlyincreased.

Chronic HCV infection is often undiagnosed such that the virusremains infective and transmissible.

Identifying HCV infection early is essentialfor limiting its spread, but distinguishing individuals who require further HCVtests is very challenging.

Besides identifying high-risk populations, an optimalsubset of indices for routine examination is needed to identify HCV screeningcandidates.

Therefore, this study analyzed data from 312 randomly chosen blooddonors, including 144 anti-HCV-positive donors and 168 anti-HCV-negative donors.

The HCV viral load in each sample was measured by real-timepolymerase chain reaction method.

Receiver operating characteristic curveswere used to find the optimal cell blood counts and thrombopoietinmeasurements for screening purposes.

Correlations with values for key indicesand viral load were also determined.

Strong predictors of HCV infection werefound by using receiver operating characteristics curves to analyze the optimalsubsets among red blood cells, monocytes, platelet counts, platelet large cellratios, and mean corpuscular hemoglobin concentrations.

Sensitivity, specificity,and area under the receiver operator characteristic curve (P<0.0001) were75.6%, 78.5%, and 0.859, respectively.