ObjectivesHyperlipidemic acute pancreatitis progresses rapidly to severe acute pancreatitis. Early identification of disease severity is critical for improving outcomes. This study aimed to investigate the risk factors associated with severe acute pancreatitis and to develop and validate a novel predictive model to support clinical decision making.MethodsThis retrospective cohort study included 502 patients with hyperlipidemic acute pancreatitis. A total of 502 patients with hyperlipidemic acute pancreatitis were retrospectively enrolled and randomly assigned to a training set (n = 351) and a validation set (n = 151) in a 7:3 ratio. Least absolute shrinkage and selection operator regression and multivariate logistic regression were used for model development. Model performance was comprehensively evaluated using the receiver operating characteristic curve, calibration curves, the Hosmer-Lemeshow test, Brier score, calibration slope, calibration-in-the-large, and decision curve analysis.ResultsMultivariate logistic regression confirmed the bedside index for severity in acute pancreatitis score (odds ratio = 7.042, 95% confidence interval: 3.850 to 14.145, p < 0.001) and metabolic score for insulin resistance (odds ratio = 1.053, 95% confidence interval: 1.023 to 1.087, p < 0.001) as independent risk factors for severe acute pancreatitis. The resulting predictive model demonstrated excellent discriminative ability in both the training set (area under the curve = 0.904, 95% confidence interval: 0.852 to 0.955) and the validation set (area under the curve = 0.885, 95% confidence interval: 0.812 to 0.958). In the training set, the area under the curve of the predictive model was significantly higher than those of the individual indicators, including metabolic score for insulin resistance, triglyceride-glucose index, triglyceride-glucose body mass index, triglycerides/high-density lipoprotein cholesterol, and the bedside index for severity in acute pancreatitis score (all p < 0.05). In the validation set, the model yielded only a minimal improvement in area under the curve over the bedside index for severity in acute pancreatitis score alone (difference = 0.020), which was not statistically significant (p = 0.326). The calibration slope and calibration-in-the-large were 1.00 and 0.00 in the training set and 0.82 and -0.54 in the validation set, respectively. Calibration curves, the Hosmer-Lemeshow test, and Brier scores collectively indicated good model fit and high predictive accuracy. Furthermore, decision curve analysis showed that the combined model provided superior net clinical benefit across a wide range of threshold probabilities.ConclusionThe combined model incorporating the bedside index for severity in acute pancreatitis score and metabolic score for insulin resistance serves as a preliminary risk stratification tool for the early identification of severe acute pancreatitis in patients with hyperlipidemic acute pancreatitis.