Endoscopic sphincterotomy for delaying choLecystectomy in mild acute biliarY pancreatitis (EMILY study): Protocol of a multicentre randomised clinical trial

Introduction: According to the literature, early cholecystectomy is necessary to avoid complications related to gallstones after an initial episode of acute biliary pancreatitis (ABP). A randomised, controlled multicentre trial (the PONCHO trial) revealed that in the case of gallstone-induced pancreatitis, early cholecystectomy was safe in patients with mild gallstone pancreatitis and reduced the risk of recurrent gallstone-related complications, as compared with interval cholecystectomy. We hypothesise that carrying out a sphincterotomy (ES) allows us to delay cholecystectomy, thus making it logistically easier to perform and potentially increasing the efficacy and safety of the procedure. Methods/Design: EMILY is a prospective, randomised, controlled multicentre trial. All patients with mild ABP, who underwent ES during the index admission or in the medical history will be informed to take part in EMILY study. The patients will be randomised into two groups: (1) early cholecystectomy (within 6 days after discharge) and (2) patients with delayed (interval) cholecystectomy (between 45 and 60 days after discharge). During a 12-month period, 93 patients will be enrolled from participating clinics. The primary endpoint is a composite endpoint of mortality and recurrent acute biliary events (that is, recurrent ABP, acute cholecystitis, uncomplicated biliary colic and cholangitis). The secondary endpoints are organ failure, biliary leakage, technical difficulty of the cholecystectomy, surgical and other complications

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

BACKGROUND: Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. METHODS: The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). RESULTS: The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). CONCLUSIONS: The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model