The predictive value of logistic model constructed by liver injury related index in biliary pancreatitis
10.3760/cma.j.cn113884-20240628-00194
- VernacularTitle:肝损伤相关指标构建的logistic预测模型在胆源性胰腺炎中的预测价值
- Author:
Jialong SUN
1
;
Tielong WU
1
;
Yuzheng XUE
1
;
Yusheng YU
1
;
Yilin REN
1
;
Tianhao LIU
1
;
Yuanyuan DAI
1
;
Zijun FAN
1
;
Yingyue SHENG
1
Author Information
1. 江南大学附属医院消化内科，无锡 214000
- Publication Type:Journal Article
- Keywords:
Pancreatitis;
Liver injury;
Logistic predicting model
- From:
Chinese Journal of Hepatobiliary Surgery
2025;31(3):167-171
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and evaluated a logistic regression model for predicting the acute biliary pancreatitis (ABP) based on liver-injury related indexes.Methods:Clinical data of 210 patients diagnosed with acute pancreatitis (AP) at the Affiliated Hospital of Jiangnan University from October 2020 to December 2022 were retrospectively analyzed, including 113 males and 97 females, with a median age of 52 years (range, 43 to 58). Among these, 88 were diagnosed with ABP and 122 with acute non-biliary pancreatitis (ANBP). Additionally, a test cohort was created using data from 101 AP patients diagnosed between January and December 2023, including 60 males and 41 females, with a median age of 53 years (range, 43 to 63). Based on the original dataset, univariate and multivariate logistic regression analyses were conducted to identify the factors influencing ABP. A prediction probability formula (Pre) was then established based on the multivariate results. The effectiveness of each indicator in predicting ABP was evaluated using the receiver operating characteristic (ROC) curve. The ROC curve analysis determined the optimal cutoff value of Pre, which was subsequently used to diagnose ABP and ANBP in the test cohort.Results:Multivariate logistic regression analysis showed the factors influencing ABP include direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), and fibrinogen (FIB). Based on the multivariate analysis results, the prediction probability formula (Pre) for ABP was established as follows: P=1/{1+ exp[-(4.807+ 0.134×DBIL-1.859×AST/ALT-0.0003×CHE-0.387×FIB)]}. ROC curve analysis revealed that the area under the curve (AUC) for Pre in predicting ABP was 0.858, with an optimal cutoff value of 0.56, at which the sensitivity was 69.3% and the specificity was 91.0%. Using the cutoff value of 0.56 for Pre, ABP was diagnosed when Pre≥0.56 and ANBP was diagnosed when Pre<0.56. This criterion was applied to diagnose patients in the test cohort, where the sensitivity and specificity of Pre for diagnosing ABP were 86.1% and 92.3%, respectively.Conclusion:The logistic regression model based on liver injury-related indicators is a valuable tool for clinically assessing the incidence of ABP.
