Prediction of Hcv Load Using Genotype, Liver Biomarkers, and Clinical Symptoms by a Mathematical Model in Patients With Hcv Infection Publisher Pubmed



Basharkhah S¹ ; Sabet F² ; Ghezeldasht SA³ ; Mosavat A³ ; Jahantigh HR² ; Barati E⁴ ; Shamsian K³ ; Salehmoghaddam M¹ ; Sharebyani H² ; Hassannia T⁵ ; Shamsian SAA⁶ Show Affiliations
Source: Microbiology and Immunology Published:2019

Abstract

Hepatitis C virus (HCV) infection is a major public health problem with about 1.75 million new HCV cases and 71 million chronic HCV infections worldwide. The study aimed to evaluate clinical, serological, molecular, and liver markers to develop a mathematical predictive model for the quantification of the HCV viral load in chronic HCV infected patients. 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 the HCV-RNA load. Genotype 3 followed by genotype 1 were the most prevalent genotypes in Mashhad, Northeastern Iran. The maximum levels of viral load were detected in the mixed genotype group, and the lowest levels in the undetectable genotype group. The log of the HCV viral load was significantly associated with thrombocytopenia and higher serum levels of alanine transaminase (ALT). In addition, the 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 they were 83.51%, 81.11%, and 36%, and for ALP were 72.06%, 42.81%, and 8.3%, respectively. A mathematical regression model was developed that could estimate the HCV-RNA load. Regression model: log viral load = 7.69 − 1.01 × G3 − 0.7 × G1 + 0.002 × ALT − 0.86 × fatigue. © 2019 The Societies and John Wiley & Sons Australia, Ltd