Development and validation of a practical clinical risk prediction model for post‐endoscopic retrograde cholangiopancreatography pancreatitis

Background Pancreatitis following endoscopic retrograde cholangiopancreatography (ERCP) can lead to significant morbidity and mortality. We aimed to develop an accurate post‐ERCP pancreatitis risk prediction model using easily obtainable variables. Methods Using prospective multi‐center ERCP data, we performed logistic regression using stepwise selection on several patient‐, procedure‐, and endoscopist‐related factors that were determined a priori. The final model was based on a combination of the Bayesian information criterion and Akaike's information criterion performance, balancing the inclusion of clinically relevant variables and model parsimony. All available data were used for model development, with subsequent internal validation performed on bootstrapped data using 10‐fold cross‐validation. Results Data from 3021 ERCPs were used to inform models. There were 151 cases of post‐ERCP pancreatitis (5.0% incidence). Variables included in the final model included female sex, pancreatic duct cannulation, native papilla status, pre‐cut sphincterotomy, increasing cannulation time, presence of biliary stricture, patient age, and placement of a pancreatic duct stent. The final model was discriminating, with a receiver operating characteristic curve statistic of 0.79, and well‐calibrated, with a predicted risk‐to‐observed risk ratio of 1.003. Conclusions We successfully developed and internally validated a promising post‐ERCP pancreatitis clinical prediction model using easily obtainable variables that are known at baseline or observed during the ERCP procedure. The model achieved an area under the curve of 0.79. External validation is planned as additional data becomes available.