Prediction of Early Clinical Deterioration in Acute Pancreatitis

INTRODUCTION: Early identification of clinical deterioration in acute pancreatitis patients is critical for improving outcomes in emergency settings. This study aimed to develop a machine learning model using only admission data to predict intensive care unit (ICU) needs within the first 72 hours and to compare its diagnostic performance with conventional scoring systems. METHODS: This retrospective, single-center study included 448 patients with acute pancreatitis admitted to the emergency department. The dataset was randomly split into 70% training and 30% test sets. After SMOTE-based class balancing and 5-fold cross-validation, a Random Forest model was developed using 35 routinely collected variables. Model performance was evaluated using AUC, sensitivity, specificity, F1 score, and calibration. . The DeLong test compared diagnostic performance with CTSI, HAPS, Ranson, and Glasgow-Imrie scores. RESULTS: The Random Forest model achieved an AUC of 0.974 (95% CI: 0.940-1.000), sensitivity of 92.9%, and specificity of 94.2% in the test set. It significantly outperformed all conventional scoring systems (P