The knowledge of fibrosis progression in chronic hepatitis C and the impact of new treatments on progression is limited by the number of available liver biopsies per patient. Moreover, liver biopsies identify a patient's stage of fibrosis at a given point in time, but cannot quantify the time spent in that stage nor the date of transition to that stage. This paper assesses the potential of Markov modelling to overcome these difficulties. The data from interferon-treated (n=185) and untreated patients (n=102) are analysed to illustrate the power of this technique.
The model accurately reproduced the distributions of patients in the different fibrosis stages at two subsequent biopsies. A quantification of the role of cofactors in the progression of the disease, and the impact of interferon treatment are given. In subjects who are 40 years old and have been infected for 10 years, the model predicted that 274 of 1000 untreated patients, but only 42 of 1000 treated patients, would progress from F0 or F1 to F3 or F4 fibrosis over the next 5 years. The model also confirms that as age and duration of infection increase, the risk of fibrosis progression increases, while the impact of treatment with interferon decreases.
Hence Markov modelling is an accurate tool in the analysis of fibrosis progression in chronic hepatitis C. It will be valuable for the quantification of the effect of new treatments on fibrosis progression in hepatitis C.