Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

BACKGROUND: The precision of current prognostic models in primary biliary cirrhosis (PBC) is rather low, partly because they are based on data from just one time during the course of the disease. The aim of this study was to design a new, more precise prognostic model by incorporating follow-up data in the development of the model. METHODS: We have performed Cox regression analyses with time-dependent variables in 237 PBC patients followed up regularly for up to 11 years. The validity of the obtained models was tested by comparing predicted and observed survival in 147 independent PBC patients followed for up to 6 years. RESULTS: In the obtained model the following time-dependent variables independently indicated a poor prognosis: high bilirubin, low albumin, ascites, gastrointestinal bleeding, and old age. When including histological variables, cirrhosis, central cholestasis, and low immunoglobulin (Ig)M also indicated a poor prognosis. The survival predicted by the models agreed well with the survival observed in the independent PBC patients. The time-dependent models predicted better than our previously published time-fixed model. CONCLUSIONS: Using the time-dependent Cox models, one can estimate a more precise probability of surviving the next 1, 3, or 6 months for any given patient at any time during the course of the disease. This may improve monitoring of PBC patients.


Journal article



Publication Date





1865 - 1876


Azathioprine, Humans, Liver Cirrhosis, Biliary, Liver Transplantation, Models, Statistical, Probability, Prognosis, Regression Analysis, Survival Rate