Time-dependent Cox regression model is superior in prediction of prognosis in primary sclerosing cholangitis

More precise prognostic models are needed for prediction of survival in patients with primary sclerosing cholangitis (PSC), particularly for the selection of candidates for liver transplantation. The aim of this study was to develop a time-dependent prognostic model for the calculation of updated short-term survival probability in PSC. Consecutive clinical and laboratory follow-up data from the time of diagnosis were collected from the files of 330 PSC patients from 5 European centers, followed for a median of 8.4 years since diagnosis. Time-fixed and time-dependent Cox regression analyses, as well as the additive regression model, were applied. The reliability of the models was tested by a cross-validation procedure. Bilirubin (on a logarithmic scale), albumin, and age at diagnosis of PSC were identified as independent prognostic factors in multivariate analysis of both the time-fixed and the time-dependent Cox regression models. The importance of bilirubin was more pronounced in the time-dependent model (hazard ratio [FIR], 2.84) than in the time-fixed analysis (hazard ratio, 1.51). The additive regression model indicated that once the patients survive beyond the first 5 years, the impact on prognosis of albumin at diagnosis ceases. The time-dependent prognostic model was superior to the time-fixed variant in assigning low 1-year survival probabilities to patients that actually survived less than 1 year. In conclusion, a time-dependent Cox regression model has the potential to estimate a more precise short-term prognosis in PSC compared with the traditional time-fixed models.