A new In-Hospital mortality prediction nomogram for ICU patients with acute pancreatitis

Abstract

IntroductionAcute pancreatitis is a prevalent inflammatory disease that can lead to severe abdominal pain and multiple organ failure, potentially resulting in pancreatic necrosis and persistent dysfunction. A nomogram prediction model was developed to accurately evaluate prognosis and provide therapy guidance to AP patients.Material and methodsRetrospective data extraction was performed using MIMIC-IV, an open-source clinical database, to obtain 1344 AP patient records, of which the primary dataset included 1030 patients after the removal of repeated hospitalizations. The prediction of in-hospital mortality (IHM) used the LASSO regression model to optimize feature selection. A multivariate logistic regression analysis was used to build a prediction model incorporating the selected features, and the C-index, calibration plot, and decision curve analysis (DCA) were utilized to evaluate the discrimination, calibration, and clinical applicability of the prediction model.ResultsThe nomogram used a combination of indicators, including SAPS II score, RDW, MBP, RR, PTT, and fluid-electrolyte disorders. The model showed satisfactory diagnostic performance with AUC values of 0.892 and 0.856 for training and internal validation, respectively. Calibration plots and the HL test demonstrated a strong correlation between predicted and actual outcomes (P=0.73), confirming the model's reliability. Notably, DCA results indicated the superiority of our model over existing scoring methods in terms of net clinical benefit, reinforcing its value in clinical applications.ConclusionsOur novel nomogram is a simple tool for accurately predicting IHM in ICU patients with AP. Treatment methods that enhance the factors involved in the model may contribute to increased in-hospital survival for these ICU patients.