Objective Most prognostic models for primary sclerosing cholangitis (PSC) are based on patients referred to tertiary care and may not be applicable for the majority of patients with PSC. The aim of this study was to construct and externally validate a novel, broadly applicable prognostic model for transplant-free survival in PSC, based on a large, predominantly populationbased cohort using readily available variables. Design T he derivation cohort consisted of 692 patients with PSC from the Netherlands, The validation cohort of 264 patients with PSC from the UK. Retrospectively, clinical and biochemical variables were collected. We derived the prognostic index from a multivariable Cox regression model in which predictors were selected and parameters were estimated using the least absolute shrinkage and selection operator. The composite end point of PSC-related death and liver transplantation was used. To quantify the models' predictive value, we calculated the C-statistic as discrimination index and established its calibration accuracy by comparing predicted curves with Kaplan-Meier estimates. Results T he final model included the variables: PSC subtype, age at PSC diagnosis, albumin, platelets, aspartate aminotransferase, alkaline phosphatase and bilirubin. The C-statistic was 0.68 (95% CI 0.51 to 0.85). Calibration was satisfactory. The model was robust in the sense that the C-statistic did not change when prediction was based on biochemical variables collected at followup. Conclusion T he Amsterdam-Oxford model for PSC showed adequate performance in estimating PSCrelated death and/or liver transplant in a predominantly population-based setting. The transplant-free survival probability can be recalculated when updated biochemical values are available.
De Vries, E. M., Wang, J., Williamson, K. D., Leeflang, M. M., Boonstra, K., Weersma, R. K., … Ponsioen, C. Y. (2018). A novel prognostic model for transplant-free survival in primary sclerosing cholangitis. Gut, 67(10), 1864–1869. https://doi.org/10.1136/gutjnl-2016-313681