To analyze the risk factors for recurrence in patients with hyperlipidemic acute pancreatitis (HLAP) and to construct a nomogram based on these factors.

A total of 500 patients with HLAP treated in Hanzhong Central Hospital from January 2020 to December 2023 were selected retrospectively and followed up until February 28, 2025. According to the recurrence status during the follow-up period, the patients were divided into non-recurrence group and recurrence group. Baseline data, laboratory indicators, chronic disease history, and HLAP onset information were compared between the two groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for HLAP recurrence, and a nomogram prediction model was established.

The non-recurrence group consisted of 325 patients, while the recurrence group comprised 175 patients. Multivariate logistic regression analysis indicated that body mass index (BMI) >28.24 kg/m² (OR=3.263), C-reactive protein (CRP) >44.12 mg/L (OR=9.215), neutrophil-to-lymphocyte ratio (NLR) >8.93 (OR=8.100), severe acute pancreatitis (SAP) (OR=12.213), and serum calcium (Ca) ≤2.38 mmol/L (OR=8.574) were independent risk factors for HLAP recurrence (P<0.05). The receiver operating characteristic (ROC) curve showed that the area under the curve of this prediction model for HLAP recurrence was 0.992 in the training set and 0.961 in the validation set. The calibration curve indicated that the C-index of the prediction model for HLAP recurrence was 0.788 in the training set and 0.813 in the validation set, and the Hosmer-Lemeshow goodness-of-fit test values were 0.930 (P=0.335) and 0.930 (P=0.628), respectively. The decision curve analysis (DCA) suggested that the model was far from the extreme curves in both the training and validation sets. Sensitivity analysis confirmed that when continuous variables were included in the model in their original form, its predictive performance showed no significant difference from that of the original model (DeLong test P>0.05).

The nomogram prediction model constructed based on the five risk factors (BMI, CRP, NLR, SAP, and Ca) has been validated by ROC curve, calibration curve, and DCA curve, demonstrating significant application value. Additionally, sensitivity analysis confirms that the model is robust and reliable, offering promising clinical benefits.