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. 2024 Oct 15;14(1):24081.
doi: 10.1038/s41598-024-75050-8.

A retrospective study utilized MIMIC-IV database to explore the potential association between triglyceride-glucose index and mortality in critically ill patients with sepsis

Jiaqi Lou #  1 , Ziyi Xiang #  2 , Xiaoyu Zhu #  3 , Youfen Fan  1 , Jingyao Song  4 , Shengyong Cui  1 , Jiliang Li  1 , Guoying Jin  1 , Neng Huang  5
Affiliations

A retrospective study utilized MIMIC-IV database to explore the potential association between triglyceride-glucose index and mortality in critically ill patients with sepsis

Jiaqi Lou et al. Sci Rep. .

Abstract

Triglyceride-glucose (TyG) index has emerged as a novel biomarker for detecting insulin resistance (IR) and has been proven to be associated with various diseases. However, its correlation with the prognosis of severe sepsis remains unraveled. This retrospective cohort study utilized patient records from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) to examine the outcomes of patients with sepsis. The primary outcomes were hospital mortality and intensive care unit (ICU) mortality. The correlation between the TyG index and outcomes was evaluated through the Kaplan-Meier method, the Log-rank test, and univariate and multivariate Cox regression analyses. Additionally, restricted cubic spline (RCS) regression analysis was employed to delve into the nonlinear relationship between baseline TyG index and outcomes, with trend significance assessed through quartile levels. Subgroup analyses were conducted to evaluate the consistency of the TyG index's prognostic value across various influencing factors. The study included 1,742 patients with sepsis requiring intensive care. The in-hospital mortality rate was 19.75% (344/1,742), and the ICU mortality rate was 14.75% (257/1,742). Cox regression analysis revealed that, in comparison to the first quartile (Q1), patients in the fourth quartile (Q4) had a 63% higher risk of in-hospital mortality (HR 1.63 [95% CI 1.22 to 2.18], P < 0.01) and a 79% higher risk of ICU mortality (HR 1.79 [95% CI 1.28 to 2.51], P < 0.001). Model 3 showed that ICU mortality risks for Q4, Q3, and Q2 were 240%, 75%, and 33% higher, respectively (HR 3.40 [95% CI 2.24 to 5.16], P < 0.001; HR 1.75 [95% CI 1.16 to 2.63], P = 0.007; HR 1.33 [95% CI 1.20 to 1.53], P < 0.001). RCS regression analysis identified a nonlinear association between the TyG index and mortality (overall P < 0.001; P for nonlinearity < 0.001, with an inflection point at 8.9). Subgroup analysis showed that the effect size and direction were consistent across different subgroups, suggesting the stability of the results. This study demonstrates that a higher TyG index is significantly associated with increased in-hospital and ICU mortality risk in critically ill sepsis patients, with evidence of non-linear correlation. Therefore, the TyG index helps identify the mortality prognosis of sepsis patients in the ICU.

Keywords: Intensive care unit; MIMIC; Mortality; Sepsis; Triglyceride glucose index.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Selection of the study population from the MIMIC-IV database.
Fig. 2
Fig. 2
Kaplan-Meier survival curve of cumulative survival rate during hospitalization and ICU stay. (A): Kaplan-Meier survival curve of cumulative survival rate during hospitalization. (B): Kaplan-Meier survival curve of cumulative survival rate during ICU stay.
Fig. 3
Fig. 3
RCS regression for TyG and mortality. (a) RCS regression for in-hospital mortality in univariate analysis. (b) RCS regression for in-hospital mortality in multivariate analysis. (c) RCS regression for ICU mortality in univariate analysis. (d) RCS regression for ICU mortality in multivariate analysis. The p-values presented in the figures were derived from a likelihood ratio test comparing the spline model to the null model. All P-values for nonlinearity were less than 0.001.
Fig. 4
Fig. 4
Forest plots for different subgroup analyses of HRs for the association between TyG index and in-hospital mortality and ICU mortality. (A) Forest plots for different subgroup analysis of HRs for the association between TyG index and in-hospital mortality and ICU mortality before covariates adjustment; (B) Forest plots for different subgroup analysis of HRs for the association between TyG index and in-hospital mortality and ICU mortality after covariates adjustment. BMI, Body Mass Index; diaht, Diagnosed as Hypertension; diadm2, Diagnosed as Diabetes mellitus type 2; diahf, Diagnosed as Heart failure; crrt, Continuous renal replacement therapy; HR, Hazard Risk.
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