Predictors for Early Identification of Hepatitis C Virus Infection.
BIOMED RESEARCH INTERNATIONAL 2015;
2015:429290. [PMID:
26413522 PMCID:
PMC4564624 DOI:
10.1155/2015/429290]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Accepted: 11/28/2014] [Indexed: 12/11/2022]
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.
Collapse