Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study - Xie, Mingchen; Zhang, Yahui; Wu, Haitao; Wu, Zeyu; Han, Hao; Xie, Xun; Zhang, Rui; Cheng, Jianhua; Xu, Jian (Frontiers in Nutrition, 2025)
BackgroundHyperglycemic crisis events (HCEs)—encompassing diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS)—constitute lethal determinants for patients with diabetic mellitus (DM) in intensive care. The triglyceride-glucose (TyG) index, an emergent insulin resistance surrogate, lacks rigorous investigation regarding HCE occurrence trajectories and prognostic sequelae among critically ill diabetics. This study aims to evaluate the relationship between the TyG index and HCE incidence/clinical outcomes in critically ill patients with DM and to construct a risk prediction model using machine-learning algorithms.MethodsThis multi-center retrospective investigation leveraged clinical repositories from Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Inclusion criteria encompassed critically ill subjects with diabetes possessing computable TyG indices within 24 h post-admission. The main study endpoints included death occurring during hospitalization and death within the intensive care unit. TyG index-outcome interrelationships underwent interrogation via logistic regression, restricted cubic spline (RCS), correlation, and linear analytical methodologies. Overlap weighting (OW), inverse probability treatment weighting (IPTW), and propensity score matching (PSM) mitigated confounding influences. Stratified examinations occurred per determinant factors. Five machine-learning architectures constructed mortality prognostication frameworks, with SHapley Additive exPlanations (SHAP) delineating pivotal predictors.ResultsAmong 4,098 critically ill patients with DM, 328 developed HCE. Patients with HCE had significantly higher TyG levels [10.2 (9.6–11.0) vs. 9.4 (8.9–9.9)] than non-HCE patients, demonstrating TyG’s discriminative ability for HCE. Through multivariate logistic regression, TyG was pinpointed as a separate risk element for both in-hospital (OR 1.956) and ICU death (OR 2.260), linked to extended hospital stays. RCS established a direct positive correlation between increased TyG levels and death rates (nonlinear p = 0.161 and 0.457), continuing even after adjusting for PSM, OW, and IPTW. Subgroup analyses reinforced TyG’s consistent mortality correlation. Machine-learning models, particularly XGBoost, achieved higher predictive accuracy, with TyG as a key component.ConclusionElevated TyG index shows a notable correlation with the occurrence of HCE and negative results in critically ill patients with DM. Advanced multivariate machine-learning models are adept at pinpointing patients at high risk, thereby facilitating prompt clinical action.