A new in-hospital mortality prediction nomogram for intensive care unit patients with acute pancreatitis.

Introduction: Acute pancreatitis (AP) 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 the prognosis and provide therapy guidance to AP patients. Material and methods: Retrospective 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 least absolute shrinkage and selection operator (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. Results: The nomogram utilized a combination of indicators, including the SAPS II score, RDW, MBP, RR, PTT, and fluid-electrolyte disorders. Impressively, the model exhibited a satisfactory diagnostic performance, with area under the curve values of 0.892 and 0.856 for the training cohort and internal validation, respectively. Moreover, the calibration plots and the Hosmer-Lemeshow goodness-of-fit (HL) test revealed a strong correlation between the predicted and actual outcomes ( Conclusions: Our 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.