PURPOSE: This study aimed to develop a nomogram for predicting acute kidney injury (AKI) in patients with moderate severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP). METHODS: This study enrolled a total of 1,077 patients with MSAP and SAP, categorizing them into three groups: training (n = 646), internal validation (n = 278), and external validation (n = 153). In the training cohort, logistic regression analysis identified independent predictors of AKI in patients with MSAP and SAP. A nomogram was developed based on these independent predictors. The model's performance was assessed using the receiver operating characteristics (ROC) curve, precision-recall (PR) curve, calibration curve, and decision curve analysis (DCA). RESULTS: The incidence rates of AKI in the training set, internal validation set, and external validation set were 32.82%, 32.01%, and 27.45%, respectively. Independent predictors of AKI in patients with MSAP and SAP included: shock index (odds ratio [OR] = 7.42, 95% confidence interval [CI] 2.18-25.19), blood urea nitrogen (OR = 1.32, 95% CI 1.22-1.43), uric acid (OR = 1.002, 95% CI 1.000-1.003), serum calcium (OR = 0.38, 95% CI 0.18-0.79), triglycerides (OR = 1.02, 95% CI 1.004-1.041), hematocrit >
0.5 (OR = 3.24, 95% CI 1.10-9.59), serum sodium <
135 mmol/L (OR = 2.01, 95% CI 1.15-3.49), creatine kinase isoenzyme >
4 ng/mL (OR = 2.61, 95% CI 1.48-4.61), and thrombin time <
14 s (OR = 2.83, 95% CI 1.28-6.27). In the training, internal validation, and external validation sets, the areas under the ROC curves for the nomogram were 0.841, 0.789, and 0.853, respectively. Similarly, the areas under the PR curves were 0.807, 0.733, and 0.770. The calibration curves demonstrated that the predicted outcomes were well-aligned with the actual results. The decision curve analysis (DCA) indicated that the model had satisfactory clinical applicability. CONCLUSIONS: Nine indicators have been identified as independent predictors of AKI in patients with MSAP and SAP. The developed nomogram exhibits robust predictive capability and shows promise for clinical application.