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

医学 重症监护室 败血症 回顾性队列研究 内科学 子群分析 多元分析 急诊医学 重症监护医学 荟萃分析
作者
Rui Zheng,Songzan Qian,Yiyi Shi,Chen Lou,Honglei Xu,Jingye Pan
出处
期刊:Cardiovascular Diabetology [BioMed Central]
卷期号:22 (1) 被引量:67
标识
DOI:10.1186/s12933-023-02041-w
摘要

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.
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