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 population-based cohort using readily available variables. Design The 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 The 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 follow-up. Conclusion The Amsterdam-Oxford model for PSC showed adequate performance in estimating PSC-related 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.

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原发性硬化性胆管炎无移植生存的新型预后模型

目的 原发性硬化性胆管炎 (PSC) 的大多数预后模型均基于转诊至三级医疗机构的患者，可能不适用于大多数 PSC 患者。本研究的目的是构建和外部验证一个新的、广泛适用的 PSC 无移植生存预后模型，该模型基于一个大型的、主要基于人群的队列，使用现成的变量。设计 衍生队列由来自荷兰的 692 名 PSC 患者组成，验证队列由来自英国的 264 名 PSC 患者组成。回顾性收集临床和生化变量。我们从多变量 Cox 回归模型中得出预后指数，在该模型中选择预测因子并使用最小绝对收缩和选择算子估计参数。使用 PSC 相关死亡和肝移植的复合终点。为了量化模型的预测值，我们计算了 C 统计量作为区分指数，并通过将预测曲线与 Kaplan-Meier 估计值进行比较来确定其校准精度。结果最终模型包括变量：PSC 亚型、PSC 诊断年龄、白蛋白、血小板、天冬氨酸转氨酶、碱性磷酸酶和胆红素。C 统计量为 0.68（95% CI 0.51 至 0.85）。校准令人满意。当预测基于随访收集的生化变量时，该模型是稳健的，因为 C 统计量没有改变。结论 PSC 的阿姆斯特丹-牛津模型在主要基于人群的环境中估计 PSC 相关的死亡和/或肝移植方面具有足够的性能。