OBJECTIVE

To develop contrast-enhanced CT-based nomograms for predicting early intervention efficacy and in-hospital mortality in acute necrotizing pancreatitis (ANP) with persistent organ failure (POF).

MATERIALS AND METHODS

This retrospective study analyzed 164 ANP patients with POF (110 in the training cohort, 54 in the validation cohort). The Sequential Organ Failure Assessment (SOFA) score was used to evaluate organ dysfunction severity. Contrast-enhanced CT parameters included mean and range CT numbers (HU) of acute necrotic collections (ANC) across anatomical regions, as well as pancreatic necrosis volume (PNV). LASSO regression identified predictors for early intervention efficacy and mortality. Nomograms were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

Early intervention efficacy predictors included intra-abdominal pressure, cardiovascular hemodynamic changes, and PNV increase. The model demonstrated good predictive performance, with an area under the ROC curve (AUC) of 0.848 (95% CI: 0.769-0.927) in the training cohort and 0.796 (95% CI: 0.644-0.947) in the validation cohort. In-hospital mortality predictors were SOFA score, cardiovascular hemodynamic changes, mean CT number of ANC at the right anterior pararenal space, and CT number range at the left paracolic gutter. The model showed AUCs of 0.918 (training cohort, 95% CI: 0.864-0.971) and 0.860 (validation cohort, 95% CI: 0.801-0.919).

CONCLUSION

ANP patients with intra-abdominal hypertension or significant PNV increase who maintain cardiovascular hemodynamic stability are more likely to benefit from early intervention. An elevated SOFA score, persistent cardiovascular failure, and ANC with poor homogeneity or drainage difficulty are risk factors for in-hospital mortality.

KEY POINTS

Question The optimal timing for early invasive intervention remains controversial in ANP with POF. Findings Nomogram models integrating organ dysfunction severity and contrast-enhanced CT imaging features can predict treatment response and clinical outcomes in ANP patients with POF. Clinical relevance Our prediction models can identify patients who may benefit from early invasive intervention and assess in-hospital mortality risk for the entire cohort, providing a practical tool to guide clinical decision-making.