Development and validation of an early prediction model for hypertriglyceridaemic severe acute pancreatitis: a...

The incidence rate of hypertriglyceridaemic acute pancreatitis (HTG-AP) has been steadily increasing due to changes in lifestyle and dietary patterns. Moreover, HTG-AP tends to be more severe than pancreatitis caused by other aetiologies, which may be related to pancreatic steatosis (PS). However, currently, no universally accepted multifactorial clinical scoring system specifically for predicting the severity of HTG-AP exists. This study aimed to identify predictors of hypertriglyceridaemic severe acute pancreatitis (HTG-SAP) and specifically incorporated PS into a visual model for predicting HTG-SAP early.

A total of 346 patients with HTG-AP were included. These patients were classified into HTG-SAP (n = 94) and hypertriglyceridaemic non-severe acute pancreatitis (HTG-NSAP, n = 252) groups. An additional 51 patients were included for prospective internal validation of the predictive model. SPSS 29.0 and R version 4.4 software programs were used for statistical data analysis and for establishing and validating the predictive model, employing various methods, including univariate analysis, binary logistic regression, calibration curve analysis, and decision curve analysis (DCA).

Eight variables, namely, respiratory rate (RR), D-dimer (D-D), blood urea nitrogen (BUN), serum calcium (Ca²⁺), potential of hydrogen (pH), and the presence of pancreatic necrosis (PN), pleural effusion (PE) and PS, were identified as independent predictors for HTG-SAP via multivariate binary logistic analysis. The AUC of the new HTG-SAP model was 0.937 (95% CI [0.908-0.966]), which was greater than those of the modified CT severity index (MCTSI), the Bedside Index for Severity in Acute Pancreatitis (BISAP) score, and the Sequential Organ Failure Assessment (SOFA) score (AUC: 0.832, 0.784, and 0.782, respectively) (P < 0.001). The calibration curve strongly aligned the predicted outcomes and the actual observations. DCA indicated that clinical intervention would be beneficial for patients who are predicted to be at risk of developing HTG-SAP.

RR; D-D, BUN, and Ca²⁺ levels; pH, and the presence of PN, PE, and PS are independent predictors of HTG-SAP. The prediction model developed based on these predictors highly consistent and practical for predicting HTG-SAP.