Author:
Hu Juanjuan,Sun Yuansong,Hua Tianfeng,Xiao Wenyan,Yang Min
Abstract
Abstract
Background
Compared with other types of acute pancreatitis (AP), hypertriglyceridemic acute pancreatitis (HTG-AP) is younger, recurrent and more prone to exacerbation. Severe HTG-AP has a high fatality rate. Early and accurate prediction of the severity is crucial. However, there is currently a lack of a specific scoring system for the severity of HTG-AP.
Aim/Purpose
To construct a risk prediction model that can accurately predict severe HTG-AP in the early stage and evaluate its clinical value.
Methods
The clinical data of 1768 patients with AP admitted to the Second Affiliated Hospital of Anhui Medical University from January 2020 to May 2023 were analyzed retrospectively, and 136 HTG-AP patients were finally selected. Univariate and multivariate analysis were performed for the early onset indicators to identify the independent risk factors for developing SAP in the patients of HTG-AP. Logistic regression was then utilized to establish a risk prediction model for the severity of HTG-AP, which was subsequently evaluated for its performance through discrimination and calibration analysis.
Results
Of the 136 patients with HTG-AP, 39 patients (28.7%) progressed to severe acute pancreatitis (SAP). Multivariate analysis revealed that CRP, RDW/SC, and D-dimer were independent risk factors for developing SAP in the patients of HTG-AP. The logistic regression analysis to establish prediction model was: Logit P = − 8.101 + 0.008 × CRP + 0.425 × D-dimer + 0.743 × RDW/SC. The receiver-operating characteristics (ROC) curve showed that area under curve (AUC) value of CRP, RDW/SC, D-dimer, and the prediction model were 0.831, 0.843, 0.874, and 0.915, respectively. Moreover, the AUC value of the prediction model and commonly used scoring systems of AP were compared: prediction model (AUC = 0.915) > Ranson (AUC = 0.900) > SOFA (AUC = 0.899) > CTSI (AUC = 0.889) > BISAP (AUC = 0.887).
Conclusion
CRP, RDW/SC and D-dimer were independent risk factors for SAP in the patients of HTG-AP. Compared with commonly used scoring systems of AP, the prediction model had good clinical prediction ability, providing reference for early identification of the patients developing severe HTG-AP and active intervention.
Funder
Anhui Medical University
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC