Author(s)

Jian Huang, Kathleen O’Sullivan, John Levis, Elizabeth Kenny-Walsh, Orla Crosbie, Liam Fanning

Department of Gastroenterology and Hepatology, University Hospital Cork.
Molecular Virology, Department of Medicine, Hospital Cork University.
Statistical Consultancy Unit, University College Cork.
Statistical Consultancy Unit, University College Cork, Ireland.

It is well known that viral load of the hepatitis C virus (HCV) is related to the efficacy of interferon therapy. The complex biological parameters that impact on viral load are essentially unknown. The current knowledge of the hepatitis C virus does not provide a mathematical model for viral load dynamics within untreated patients. We car-ried out an empirical modelling to investigate whether different fluctuation patterns exist and how these patterns (if exist) are related to host-specific factors. Data was prospectively col-lected from 147 untreated patients chronically infected with hepatitis C, each contributing be-tween 2 to 10 years of measurements. We pro-pose to use a three parameter logistic model to describe the overall pattern of viral load fluctua-tion based on an exploratory analysis of the data. To incorporate the correlation feature of longitu-dinal data and patient to patient variation, we introduced random effects components into the model. On the basis of this nonlinear mixed ef-fects modelling, we investigated effects of host-specific factors on viral load fluctuation by in-corporating covariates into the model. The pro-posed model provided a good fit for describing fluctuations of viral load measured with varying frequency over different time intervals. The aver-age viral load growth time was significantly dif-ferent between infection sources. There was a large patient to patient variation in viral load as-ymptote.

Logistic model, Viral load, Viral genotype, Mixed effects modelling

Huang, J. , O’Sullivan, K. , Levis, J. , Kenny-Walsh, E. , Crosbie, O. and Fanning, L. (2008) Retrospective analysis of chronic hepatitis C in untreated patients with nonlinear mixed effects model. Journal of Biomedical Science and Engineering, 1, 85-90. doi: 10.4236/jbise.2008.12014.