Abstract
INTRODUCTION: Severe acute pancreatitis (SAP) is a life-threatening condition requiring early risk stratification. Although the Bedside Index for Severity in Acute Pancreatitis (BISAP) is widely used, its reliance on complex parameters limits its applicability in resource-constrained settings. This study introduces a decision tree model based on Classification and Regression Tree (CART) analysis, using neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP), as a simpler alternative for early SAP prediction. METHODS: In a prospective cohort of 340 patients at National Hospital, Vietnam (November 2022-September 2023), NLR, CRP, and BISAP scores were assessed on admission. CART analysis was used to develop a decision tree, and model performance was compared with BISAP using receiver operating characteristic curves, decision curve analysis. RESULTS: The CART model identified NLR ≥11.4 and CRP ≥173.3 mg/L as optimal thresholds for SAP prediction. The model achieved an area under the curve 0.866 in the validation cohort, statistically comparable with BISAP (area under the curve = 0.900, P = 0.286). The model demonstrated high sensitivity (90.9%), specificity (84.5%), and accuracy (86.25%), confirming its robustness. Decision curve analysis highlighted similar clinical benefits with BISAP, but the CART-based model offered greater simplicity, making it ideal for resource-limited settings. DISCUSSION: The CART-derived decision tree using NLR and CRP provides an accessible and reliable tool for early SAP prediction. With performance comparable with BISAP but requiring fewer resources, this model supports rapid, evidence-based decision-making in clinical practice.