A novel HCP (heparin-binding protein-C reactive protein-procalcitonin) inflammatory composite model can predict severe acute pancreatitis - Kong, Deshuai; Lei, Zhang; Wang, Zhenyong; Yu, Meng; Li, Jinchao; Chai, Wei; Zhao, Xiulei (Scientific reports, 2023)
Severe acute pancreatitis (SAP) presents with an aggressive clinical presentation and high lethality rate. Early prediction of the severity of acute pancreatitis will help physicians to further precise treatment and improve intervention. This study aims to construct a composite model that can predict SAP using inflammatory markers. 212 patients with acute pancreatitis enrolled from January 2018 to June 2020 were included in this study, basic parameters at admission and 24 h after hospitalization, and laboratory results such as inflammatory markers were collected. Pearson's test was used to analyze the correlation between heparin-binding protein (HBP), procalcitonin (PCT), and C-reactive protein (CRP). Risk factors affecting SAP were analyzed using multivariate logistic regression, inflammatory marker models were constructed, and subject operating curves were used to verify the discrimination of individual as well as inflammatory marker models and to find the optimal cut-off value based on the maximum Youden index. In the SAP group, the plasma levels of HBP, CRP, and PCT were 139.1 ± 74.8 ng/mL, 190.7 ± 106.3 mg/L and 46.3 ± 22.3 ng/mL, and 25.3 ± 16.0 ng/mL, 145.4 ± 67.9 mg/L and 27.9 ± 22.4 ng/mL in non-SAP patients, with a statistically significant difference between the two groups ( P < 0.001), The Pearson correlation analysis showed a positive correlation between the three values of HBP, CRP, and PCT. The results of the multivariate logistic regression analysis showed that HBP (OR = 1.070 [1.044–1.098], P < 0.001), CRP (OR = 1.010 [1.004–1.016], P = 0.001), and PCT (OR = 1.030[1.007–1.053], P < 0.001) were risk factors for SAP, and the area under the curve of the HBP-CRP-PCT model was 0.963 (0.936–0.990). The HCP model, consisting of HBP, CRP, and PCT; is well differentiated and easy to use and can predict the risk of SAP in advance.