Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes in PSC: A Derivation & Validation Study Using Machine Learning.

Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a prediction model and compare its performance to existing surrogate markers. The model was derived using 509 subjects from a multicenter North American cohort and validated in an international multicenter cohort (n = 278). Gradient boosting, a machine-based learning technique, was used to create the model. The endpoint was hepatic decompensation (ascites, variceal hemorrhage, or encephalopathy). Subjects with advanced PSC or cholangiocarcinoma (CCA) at baseline were excluded. The PSC risk estimate tool (PREsTo) consists of nine variables: bilirubin, albumin, serum alkaline phosphatase (SAP) times the upper limit of normal (ULN), platelets, aspartate aminotransferase (AST), hemoglobin, sodium, patient age, and number of years since PSC was diagnosed. Validation in an independent cohort confirms that PREsTo accurately predicts decompensation (C-statistic, 0.90; 95% confidence interval [CI], 0.84-0.95) and performed well compared to Model for End-Stage Liver Disease (MELD) score (C-statistic, 0.72; 95% CI, 0.57-0.84), Mayo PSC risk score (C-statistic, 0.85; 95% CI, 0.77-0.92), and SAP <1.5 x ULN (C-statistic, 0.65; 95% CI, 0.55-0.73). PREsTo continued to be accurate among individuals with a bilirubin <2.0 mg/dL (C-statistic, 0.90; 95% CI, 0.82-0.96) and when the score was reapplied at a later course in the disease (C-statistic, 0.82; 95% CI, 0.64-0.95). Conclusion: PREsTo accurately predicts hepatic decompensation (HD) in PSC and exceeds the performance among other widely available, noninvasive prognostic scoring systems.