PT  - JOURNAL ARTICLE
AU  - Li, Fuyuan
AU  - Wang, Zhanjin
AU  - Bian, Ruiling
AU  - Xue, Zhangtuo
AU  - Cai, Junjie
AU  - Zhou, Ying
AU  - Wang, Zhan
TI  - Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database
AID  - 10.1136/bmjopen-2024-087427
DP  - 2025 Feb 01
TA  - BMJ Open
PG  - e087427
VI  - 15
IP  - 2
4099  - http://bmjopen.bmj.com/content/15/2/e087427.short
4100  - http://bmjopen.bmj.com/content/15/2/e087427.full
SO  - BMJ Open2025 Feb 01; 15
AB  - Objective This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.Design A retrospective study based on patient data from public databases.Participants This study analysed 1295 patients with acute pancreatitis complicated by septicaemia from the US Intensive Care Database.Methods From the MIMIC database, data of patients with acute pancreatitis and sepsis were obtained to construct machine learning models, which were internally and externally validated. The Boruta algorithm was used to select variables. Then, eight machine learning algorithms were used to construct prediction models for acute kidney injury (AKI) occurrence in intensive care unit (ICU) patients. A new stacked ensemble model was developed using the Stacking ensemble method. Model evaluation was performed using area under the receiver operating characteristic curve (AUC), precision-recall (PR) curve, accuracy, recall and F1 score. The Shapley additive explanation (SHAP) method was used to explain the models.Main outcome measures AKI in patients with acute pancreatitis complicated by sepsis.Results The final study included 1295 patients with acute pancreatitis complicated by sepsis, among whom 893 cases (68.9%) developed acute kidney injury. We established eight base models, including Logit, SVM, CatBoost, RF, XGBoost, LightGBM, AdaBoost and MLP, as well as a stacked ensemble model called Multimodel. Among all models, Multimodel had an AUC value of 0.853 (95% CI: 0.792 to 0.896) in the internal validation dataset and 0.802 (95% CI: 0.732 to 0.861) in the external validation dataset. This model demonstrated the best predictive performance in terms of discrimination and clinical application.Conclusion The stack ensemble model developed by us achieved AUC values of 0.853 and 0.802 in internal and external validation cohorts respectively and also demonstrated excellent performance in other metrics. It serves as a reliable tool for predicting AKI in patients with acute pancreatitis complicated by sepsis.Data are available upon reasonable request. Datasets generated and analysed during the current study may be obtained upon reasonable request.