A Novel NPR48-Based Model for Early Prediction of Severe Acute Pancreatitis: Development and Multicenter Validation in Comparison with Traditional Clinical Scores.

Background: Acute pancreatitis (AP) is associated with significant morbidity and mortality when progressing to severe acute pancreatitis (SAP). Timely and accurate prediction of SAP remains challenging due to the limitations of existing clinical scoring systems in terms of sensitivity, specificity, and operational practicality. Inflammatory indices derived from routine blood tests have emerged as promising alternatives, though dynamic monitoring beyond admission remains underexplored. Purpose: This study aimed to evaluate dynamic changes in inflammatory indices at admission and 48 hours post-admission in AP patients, investigate their predictive capacity for SAP development, and establish a novel predictive model. Patients and Methods: A retrospective analysis was conducted on 343 AP patients, including 76 SAP and 267 non-SAP cases. Blood routine parameters were collected at admission and 48 hours thereafter. The roles of the neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), systemic inflammation response index (SIRI), and other inflammatory indices in AP were analyzed and compared with six traditional clinical scores. Feature selection was performed using LASSO regression, followed by the construction of a multivariate logistic regression model. The model was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis. Results: The SAP group showed significant elevations in NLR0, NLR48, NPR0, NPR48, NPR48/NPR0, SIRI48, and SIRI48/SIRI0. Among these, NPR48 exhibited the highest predictive performance for SAP, comparable to the Ranson score. The NPR48-based nomogram achieved an AUC of 0.905, with 86.8% sensitivity and 82.4% specificity, significantly outperforming all clinical scores. Conclusion: Dynamic monitoring of NPR, particularly its value at 48 hours post-admission (NPR48), significantly improves early SAP detection. Through LASSO regression for feature selection, we developed and validated a novel NPR48-based nomogram that combines NPR48 with other relevant clinical variables. This tool is efficient, cost-effective, and readily applicable in clinical settings for warning of SAP.