Abstract
BACKGROUND: To study the value of ensemble machine learning (EL) model in the prediction of infected pancreatic necrosis (IPN) among patients with acute necrotizing pancreatitis (ANP). METHODS: This study comprehensively analyzed 1073 acute necrotizing pancreatitis (ANP) patients admitted to Xiangya hospital from January 2011 to December 2023. The patients were divided into IPN group and sterile pancreatic necrosis (SPN) group based on IPN occurrence. All ANP patients were randomly divided into training dataset and validation dataset with a ratio of 7:3. The EL model was built by integrating multiple machine learning models (LASSO, random forest, and SVM). To verify the stability of the EL model, 78 ANP patients from the Third Xiangya hospital were included for external validation, and a Fagan nomogram was constructed to assess the posterior probability. RESULTS: The EL model was constructed with 31 risk factors identified through LASSO regression. The prediction accuracy of the EL model in the training dataset was 92.6%. In the validation dataset, the prediction accuracy was 91.5%. Compared with the LR model, the EL model demonstrated higher AUC values (training dataset: 0.916 vs. 0.744; validation dataset: 0.919 vs. 0.742) and net benefit rate. The AUC of the EL model for predicting IPN within 7 days, 7-14 days, and after 14 days were 0.888, 0.906, and 0.901, respectively. In addition, the external validation results further indicated the accuracy of the EL model (AUC: 0.883). An EL model-based Fagan nomogram could be used to estimate the accuracy of IPN predictions. CONCLUSION: The EL model demonstrates superior predictive efficiency for IPN compared to the LR model, offering greater predictive value and potential clinical benefits. Furthermore, the EL model shows stable performance across different stages of IPN onset, enabling clinicians to make timely adjustments to treatment strategies and ultimately improve patient outcomes. TRIAL REGISTRATION: The study is registered at www.researchregistry.com (Unique Identifying number: researchregistry10652).