Establishment and Validation of Early Prediction Model for Hypertriglyceridemic Severe Acute Pancreatitis

Background: The prevalence of hypertriglyceridaemia-induced acute pancreatitis (HTG-AP) is increasing due to improvements in living standards and dietary changes. However, at present, there is no clinical multifactor scoring system specific to HTG-AP. This study aimed to screen the predictors of hypertriglyceridemia severe acute pancreatitis (HTG-SAP) and combined several indicators to establish and verify a visual model for the early prediction of HTG-SAP. Methods The clinical data of 266 patients with HTG-SAP were analysed. Patients were classified into severe (n = 42) and non-severe (n = 224) groups according to the Atlanta classification criteria. Several statistical analyses, including one-way analysis, least absolute shrinkage with selection operator (LASSO) regression model and binary logistic regression analysis were used to evaluate the data. Result The univariate analysis found that several factors showed no statistically significant differences, including number of episodes of pancreatitis, abdominal pain score and several blood diagnostic markers, such as lactate dehydrogenase (LDH), serum calcium (Ca 2+ ), C-reactive protein (CRP) and the incidence of pleural effusion, between the two groups (P < 0.000). LASSO regression analysis identified six candidate predictors: CRP, LDH, Ca 2+ , procalcitonin (PCT), ascites and Balthazar computed tomography (CT) grade. Binary logistic regression multivariate analysis showed that CRP, LDH, Ca 2+ , and ascites were independent predictors of HTG-SAP. The area under the curve (AUC) was 0.886, 0.893, 0.872, and 0.850, respectively. The AUC of the newly established HTG-SAP model was 0.960 (95% confidence interval: 0.936–0.983), which was higher than that of the bedside index for severity in acute pancreatitis, modified CT severity index, Ranson score and Japanese severity score (JSS) CT grade (AUC: 0.794, 0.796, 0.894 and 0.764, respectively). The differences were statistically significant (P  0.05). The decision curve analysis plot suggested that clinical intervention can benefit patients when the model predicts that they are at risk for developing HTG-SAP. Conclusions CRP, LDH, Ca 2+ and ascites are independent predictors of HTG-SAP. The prediction model constructed based on these indicators has a high accuracy, sensitivity, consistency and practicability in predicting HTG-SAP.