Application of a Machine Learning Predictive Model for Recurrent Acute Pancreatitis.

Abstract

Background and aim: Acute pancreatitis is the main cause of hospitalization for pancreatic disease. Some patients tend to have recurrent episodes after experiencing an episode of acute pancreatitis. This study aimed to construct predictive models for recurrent acute pancreatitis (RAP).

Methods: A total of 531 patients who were hospitalized for the first episode of acute pancreatitis at the Affiliated Hospital of Southwest Medical University from January 2018 to December 2019 were enrolled in the study. We confirmed whether the patients had a second episode until December 31, 2021, through an electronic medical record system and telephone or WeChat follow-up. Clinical and follow-up data of patients were collected and randomly allocated to the training and test sets at a ratio of 7:3. The training set was used to select the best model, and the selected model was tested with the test set. The area under the receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, decision curve, and calibration plots were used to assess the efficacy of the models. Shapley additive explanation values were used to explain the model.

Results: Considering multiple indices, XGBoost was the best model. The area under the receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model in the test set were 0.779, 0.763, 0.883, 0.647, 0.341, and 0.922, respectively. According to the Shapley additive explanation values, drinking, smoking, higher levels of triglyceride, and the occurrence of ANC are associated with RAP.

Conclusion: The XGBoost model shows good performance in predicting RAP, which may help identify high-risk patients.