已发表论文

重症急性胰腺炎早期预测模型的建立及验证

 

Authors Yang K, Song Y, Su Y, Li C, Ding N 

Received 29 December 2023

Accepted for publication 14 May 2024

Published 4 June 2024 Volume 2024:17 Pages 3551—3561

DOI https://doi.org/10.2147/JIR.S457199

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Ning Quan

Kongzhi Yang, Yaqin Song, Yingjie Su, Changluo Li, Ning Ding

Department of Emergency Medicine, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, People’s Republic of China

Correspondence: Ning Ding, Department of Emergency Medicine, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, Hunan, 410004, People’s Republic of China, Tel + (86)731-8566-7935, Email doctordingning@sina.com; doctordingning@163.com

Objective: The purpose of this study is to establishment and validation of an early predictive model for severe acute pancreatitis (SAP).
Methods: From January 2015 to August 2022, 2986 AP patients admitted to Changsha Central Hospital were enrolled in this study. They were randomly divided into a modeling group (n = 2112) and a validation group (n = 874). In the modeling group, identify risk factors through logistic regression models and draw column charts. Use internal validation method to verify the accuracy of column chart prediction. Apply calibration curves to evaluate the consistency between nomograms and ideal observations. Draw a DCA curve to evaluate the net benefits of the prediction model.
Results: Nine variables including respiratory rate, heart rate, WBC, PDW, PT, SCR, AMY, CK, and TG are the risk factors for SAP. The column chart risk prediction model which was constructed based on these 9 independent factors has high prediction accuracy (modeling group AUC = 0.788, validation group AUC = 7.789). The calibration curve analysis shows that the prediction probabilities of the modeling and validation groups are consistent with the observation probabilities. By drawing a DCA curve, it shows that the model has a wide threshold range (0.01– 0.88).
Conclusion: The study developed an intuitive nomogram containing readily available laboratory parameters to predict the incidence rate of SAP.

Keywords: acute pancreatitis, severe, risk factors, predictive model, nomogram