Objective:To establish an early prediction model with multiple indicators to predict the risk of severe acute pancreatitis (SAP) in hyperlipidemic acute pancreatitis (HLAP).Methods:The clinical data of 92 patients with HLAP admitted to the Emergency Department of our hospital from March 2018 to February 2020 were analyzed retrospectively. Among them, 29 cases deteriorated to SAP and 63 cases did not. Univariate analysis was used to screen predictive indicators related to hyperlipidemic severe acute pancreatitis (HL-SAP), and logistic regression analysis was used to screen independent predictive indicators related to HL-SAP. Then a prediction model was established. The area under (AUC) the receiver operating curve (ROC) was used to evaluate the predictive ability of each predictive indicator and the model for HL-SAP. Bootstrap resampling technology was used to validate the predictive ability of the model.Results:Univariate analysis showed that procalcitonin, D-dimer, C-reactive protein, albumin, cholesterol and CT grade had influence on the progression of HLAP to SAP ( P<0.05). Logistic regression analysis showed that D-dimer ( OR=2.112, 95% CI: 1.022-4.366; P<0.05), CT grade ( OR=5.818, 95% CI: 2.481-13.643; P<0.01) and cholesterol ( OR=1.146, 95% CI: 1.004-1.308; P<0.05) were independent risk factor of HL-SAP. The AUC of D-dimer, CT grade, cholesterol and the model were 0.802, 0.875, 0.665 and 0.927, respectively. Internal validation of the predictive ability of the model showed that the C-index was 0.927. Conclusions:In the early phase, application of the prediction model that composes D-dimer, CT grade and cholesterol has a good predictive effect on HL-SAP.