Establishment and evaluation of early prediction models for severe acute pancreatitis
10.3760/cma.j.issn.1671-0282.2024.10.009
- VernacularTitle:重症急性胰腺炎早期预测模型的建立与评价
- Author:
Mei WANG
1
;
Yu XIA
;
Changmei WU
;
Lianghui MA
;
Yanyan CHEN
;
Wenjun ZHU
;
Xingyu WANG
Author Information
1. 安徽医科大学第一附属医院急诊医学科,合肥 230032
- Keywords:
Acute pancreatitis;
LASSO regression;
Logistic regression;
Decision tree;
Prediction model
- From:
Chinese Journal of Emergency Medicine
2024;33(10):1398-1406
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore a simplified and efficient early prediction model for severe acute pancreatitis (SAP) using the least absolute shrinkage and selection operator (LASSO) regression, and to construct both logistic regression and decision tree models. The aim is to identify high-risk individuals, guide clinical treatment, and improve patient outcomes.Methods:A retrospective analysis was conducted on the clinical data of 412 patients with acute pancreatitis admitted to the Emergency and Gastroenterology Departments of the First Affiliated Hospital of Anhui Medical University and its High-tech Branch from November 2020 to September 2023. LASSO regression was employed to identify factors significantly associated with SAP, followed by the construction of a multivariate logistic regression model and a decision tree model. The predictive performance of these models was evaluated and compared to the bedside index for severity in acute pancreatitis (BISAP).Results:Among the 412 patients, the incidence of SAP was 12.14% ( n=50). Seven variables significantly associated with SAP severity were identified by LASSO regression, including respiratory rate at admission, pain score at admission, pleural effusion, fibrin degradation products, C-reactive protein, serum creatinine, and serum albumin. The logistic regression model incorporated four variables: pleural effusion, pain score at admission, serum creatinine, and serum albumin. In the training set, the model demonstrated a sensitivity of 0.528, specificity of 0.984, accuracy (95% CI) of 0.928 (0.892-0.955), Kappa value of 0.606, and AUC (95% CI) of 0.920 (0.862-0.979). In the testing set, the model showed a sensitivity of 0.643, specificity of 0.925, accuracy (95% CI) of 0.891 (0.822-0.941), Kappa value of 0.519, and AUC (95% CI) of 0.923 (0.861-0.985). The decision tree model comprised three branches and four terminal nodes, indicating that serum creatinine, serum albumin, and pleural effusion could effectively predict SAP occurrence. In the training set, the decision tree model had a sensitivity of 0.500, specificity of 0.973, accuracy (95% CI) of 0.914 (0.876-0.944), Kappa value of 0.544, and AUC (95% CI) of 0.812 (0.731-0.894). In the testing set, the model exhibited a sensitivity of 0.500, specificity of 0.925, accuracy (95% CI) of 0.875 (0.802-0.928), Kappa value of 0.412, and AUC (95% CI) of 0.709 (0.565-0.853). The DeLong test revealed that in the training set, the AUC of the logistic regression model was significantly greater than that of the decision tree model ( P<0.01) and the BISAP score ( P<0.001), while the AUC difference between the decision tree model and the BISAP score was not statistically significant ( P=0.762). In the testing set, the AUC of the logistic regression model was again greater than that of the decision tree model ( P<0.01) and the BISAP score ( P=0.018), whereas the AUC of the decision tree model was lower than that of the BISAP score ( P=0.017). Conclusions:Both the logistic regression and decision tree models demonstrate good predictive value for SAP, and their combined use may provide valuable guidance for clinical practice.
