Development of a Joint Prediction Model for Assessing the Severity of Hypertriglyceridemia-Induced Acute Pancreatitis

Abstract

Background

Patients with hypertriglyceridemia-induced acute pancreatitis (HTG-AP) have a high incidence of severe disease and a poor prognosis. This study aimed to construct a joint prediction model using multiple clinical and imaging indicators to assess the severity of HTG-AP.

Methods

A retrospective analysis was conducted on 165 patients with HTG-AP, categorized into non-mild (n = 84) and mild (n = 81) groups. Clinical parameters were compared, and logistic regression was used to identify independent predictors. A joint prediction model was constructed and validated for stability and performance using receiver operating characteristic analysis, the bootstrap sampling method, the Hosmer–Lemeshow test, and the Z-test.

Results

Significant intergroup differences were observed in lipid metabolism markers (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]), pancreatic injury indicators (amylase [AMY] and lipase [LPS]), imaging characteristics (modified computed tomography severity index [MCTSI] score and liver computed tomography [CT] value), and hospitalization duration (p < 0.05). The MCTSI score, liver CT value, TC level, and LDL-C level were identified as independent risk factors for non-mild HTG-AP. The joint model demonstrated superior performance (area under the curve [AUC] = 0.841) compared with individual predictors (p < 0.05), with good calibration according to the Hosmer–Lemeshow test (p = 0.914) and stable performance validated by bootstrap sampling (ΔAUC = 0.001, p = 0.1531).

Conclusion

The joint prediction model outperformed individual indicators such as the TC level, LDL-C level, MCTSI score, and liver CT value in assessing non-mild HTG-AP, offering enhanced clinical utility.