Machine learning model for early prediction of acute respiratory failure in acute pancreatitis: Multicenter...

Acute respiratory failure (ARF) is a common organ failure in acute pancreatitis (AP) with high mortality. This study used machine learning to predict ARF risk in AP patients through a retrospective, multicenter cohort analysis involving MIMIC-IV and Chinese hospital data (2010-2025). Variable selection combined SHapley Additive exPlanations values and Lasso regression, employing five machine learning algorithms for modeling. The dataset identified nine most influential features, including temperature, SpO₂, and seven serum indicators, with the logistic regression (LR) model achieving the highest areas under the curve (AUCs) (0.9048 internal, 0.8717 external, and 0.8557 cross-specialty) and top sensitivity (0.8947 internal, 0.7361 external, and 0.9 cross-specialty). The LR model proved to be the most effective classifier for early ARF prediction in AP. A cross-specialty validation of pregnancy patients delineated the model's boundary condition. An online computing platform for this LR model was publicly available and free to use.