Establishment and Validation of an Early Predictive Model for Severe Acute Pancreatitis

Abstract

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 ChinaCorrespondence: 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 [email protected]; [email protected]: 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

Keywords

• acute pancreatitis
• severe
• risk factors
• predictive model
• nomogram.