Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: analysis of the MIMIC-IV database

• Published in: Cardiovascular diabetology Vol. 22; no. 1; pp. 1 - 307
• Main Authors: Zheng, Rui, Qian, Songzan, Shi, Yiyi, Lou, Chen, Xu, Honglei, Pan, Jingye
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.11.2023; BMC
• Subjects: Algorithms; Binomial distribution; Body mass index; Cardiac arrhythmia; Cardiovascular disease; Chronic obstructive pulmonary disease; Creatinine; Disease prevention; Feature selection; Gender; Glucose; Hemoglobin; Hospitals; In-hospital mortality; Insulin resistance; Kidneys; Learning algorithms; Machine learning; MIMIC-IV database; Mortality; Neutrophils; Patients; Regression analysis; Sensitivity analysis; Sepsis; Software; Standard scores; Structured Query Language-SQL; Triglyceride-glucose index; Triglycerides; Variables
• DOI: 10.1186/s12933-023-02041-w
• ISSN: 1475-2840

Abstract Background This study aimed to explore the association between the triglyceride-glucose (TyG) index and the risk of in-hospital mortality in critically ill patients with sepsis. Methods This was a retrospective observational cohort study and data were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC IV2.2) database. The participants were grouped into three groups according to the TyG index tertiles. The primary outcome was in-hospital all-cause mortality. Multivariable logistics proportional regression analysis and restricted cubic spline regression was used to evaluate the association between the TyG index and in-hospital mortality in patients with sepsis. In sensitivity analysis, the feature importance of the TyG index was initially determined using machine learning algorithms and subgroup analysis based on different subgroups was also performed. Results 1,257 patients (56.88% men) were included in the study. The in-hospital, 28-day and intensive care unit (ICU) mortality were 21.40%, 26.17%, and 15.43% respectively. Multivariate logistics regression analysis showed that the TyG index was independently associated with an elevated risk of in-hospital mortality (OR 1.440 [95% CI 1.106–1.875]; P = 0.00673), 28-day mortality (OR 1.391; [95% CI 1.52–1.678]; P = 0.01414) and ICU mortality (OR 1.597; [95% CI 1.188–2.147]; P = 0.00266). The restricted cubic spline regression model revealed that the risks of in-hospital, 28-day, and ICU mortality increased linearly with increasing TyG index. Sensitivity analysis indicate that the effect size and direction in different subgroups are consistent, the results is stability. Additionally, the machine learning results suggest that TyG index is an important feature for the outcomes of sepsis. Conclusion Our study indicates that a high TyG index is associated with an increased in-hospital mortality in critically ill sepsis patients. Larger prospective studies are required to confirm these findings.