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 (p = 0.73), further confirming the reliability of our model. Notably, the results of the decision curve analysis (DCA) highlighted the superiority of our model over previously described scoring methods in terms of net clinical benefit, solidifying its value in clinical applications. 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.