Triglyceride–glucose index as a key predictor of ARDS in acute pancreatitis: SHAP analysis reveals its critical role in risk stratification
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: The relationship between triglyceride-glucose (TyG) index and acute respiratory distress syndrome (ARDS) in acute pancreatitis (AP) patients is still lacking. This study aimed to explore the association between the TyG index and ARDS in AP patients using an 8-year retrospective dataset. Methods: This study was performed in Changsha Central Hospital from January 2015 to December 2022. Univariate analysis was done to discuss the relationship between different characteristics and ARDS in AP. Multivariate regression analysis was employed to investigate the relationship between the TyG index and ARDS in AP. Eight machine learning models were employed to assess the in-hospital ARDS risk in AP patients. The SHapley Additive exPlanations (SHAP) method was utilized to verify the importance of TyG in the models. Results: = 0.0001) after adjusting all potential confounders. The relationship between the TyG index and ARDS in AP was non-linear. The XGBoost (AUC = 0.857 ± 0.034) and Random Forest (AUC = 0.851 ± 0.045) algorithms were the best two performance methods. In the SHAP analysis, TyG was the second most important feature in the RF model and the seventh in the XGBoost model. Conclusion: TyG index was associated with in-hospital ARDS in AP. The XGBoost and Random Forest models based on the TyG index had the best performance for predicting ARDS in AP patients. The SHAP method further confirmed that the TyG index serves as a significant predictor for the development of ARDS in patients with acute pancreatitis.
Source: Frontiers in Nutrition
Authors: Ju Luo, Zhe Chen, Cuirong Guo, Yingjie Su, Ning Ding
Institutions: Changsha Central Hospital, University of South China
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: 脓毒症早期诊断和预测平台的构建与应用
OpenAlex ID: W4416506000
