Assessment of HCV viral load, genotype, liver biomarkers and clinical symptoms: Developing a mathematical model for prediction of HCV load

Background Hepatitis C virus infection is a major worldwide public health problem with about 1.75 million new HCV cases and 71 million HCV chronic infections around the world. The aim of study was to evaluate clinical, serological, molecular, and liver markers to develop a mathematical predictive model for HCV viral load quantification in chronic HCV infected patients. Methods In this cross-sectional study, blood samples were taken from 249 recently diagnosed HCV-infected subjects and were tested for liver condition, viral genotype, and HCV RNA load. Receiver operating characteristics (ROC) curves and multiple linear regression analysis were used to predict HCV viral load. Results The genotype 3 followed by genotype 1 were the most prevalent genotypes in Mashhad, Northeastern Iran. HCV viremia was significantly associated with genotyping (p=0.04). The maximum levels of viral load were detected in the mixed genotype group, and the lowest levels in the undetectable genotype group. Log of HCV viral load was significantly associated with thrombocytopenia and higher serum levels of alanine transaminase (ALT). In addition, log HCV RNA was significantly higher in patients with arthralgia, fatigue, fever, vomiting or dizziness. Moreover, genotype 3 was significantly associated with icterus. A ROC curve analysis revealed that the best cut-off points for serum levels of aspartate aminotransferase (AST), ALT, and alkaline phosphatase (ALP) were >31, >34, and ≤246 IU/L, respectively. Sensitivity, specificity and positive predictive values for AST were 87.7%, 84.36% and 44.6%, for ALT; 83.51%, 81.11% and 36%, and for ALP were 72.06%, 42.81% and 8.3%, respectively. Conclusions A mathematical regression model was developed that can estimate the HCV viral load. Regression model: log viral load=7.69-1.01×G3-0.7×G1+0.002×ALT-0.86× fatigue.