摘要Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(Pl-12,Pl-12-2,and Pl-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56％(n=18)and 6.06％(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95％confidence interval[Cl]:0.71-0.96)for Pl-24.Pl-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.Pl-24 performed well in the outer validation cohort with an AUC value of 0.82(95％Cl:0.65-0.98),specificity of 0.97(95％Cl:0.91-1.00),and PPV of 0.67(95％Cl:0.00-1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.