Interpretable machine learning model for predicting in-hospital mortality in elderly acute pancreatitis: Development and validation in a multicenter cohort.

Abstract

Elderly acute pancreatitis (AP) patients face significantly higher in-hospital all-cause mortality, highlighting the need for effective risk stratification to support timely clinical decision-making. We conducted a multicenter retrospective study that enrolled 2,728 elderly AP patients, with which we developed and validated a robust machine learning (ML) model for predicting in-hospital all-cause mortality. We first selected predictors of mortality using LASSO regression and random forest-based Boruta algorithms. Then, seven ML models incorporating the selected predictors were trained and evaluated using the area under the receiver operating characteristic curve (AUC). XGBoost demonstrated the highest predictive performance, achieving an AUC of 0.884 (95% CI: 0.823-0.945) in the external validation test, outperforming the conventional Ranson score in predicting in-hospital mortality. Shapley additive explanations ranked vasoactive drug, hospital length of stay, leukocyte count, noninvasive ventilation, and invasive mechanical ventilation as five key predictors. An interactive web-based tool based on the optimal XGBoost model has been available at https://appredction.shinyapps.io/acutepancreatitis_xgb/ to generate real-time risk predictions. This study proposed a validated and interpretable ML model to support in-hospital risk stratification for elderly patients with AP, thereby facilitating clinical decision-making and optimizing intensive care unit resource allocation.