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Observational Study
. 2023 Nov 8;22(1):307.
doi: 10.1186/s12933-023-02041-w.

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

Rui Zheng #  1 , Songzan Qian #  2 , Yiyi Shi  3 , Chen Lou  4 , Honglei Xu  5 , Jingye Pan  6   7   8
Affiliations
Observational Study

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

Rui Zheng et al. Cardiovasc Diabetol. .

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.

Keywords: In-hospital mortality; MIMIC-IV database; Sepsis; Triglyceride-glucose index.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Inclusion/exclusion criteria. MIMIC: Medical Information Mart for Intensive Care
Fig. 2
Fig. 2
Application of Machine Learning in Feature Selection. A Shapley Additive Explanations (SHAP) for the random forest model. A Distribution of the impact of each feature on the model output. Each dot represents a patient in a row. The colors of the dots represent the feature values: red represents larger values and blue represents lower values. B Feature selection for the relationship between various TyG indices and in-hospital mortality was analyzed using the Boruta algorithm. B The horizontal axis shows the name of each variable, whereas the vertical axis represents the Z-value of each variable. The box plot depicts the Z-value of each variable in the model calculation, with green boxes representing important variables, blue boxes representing tentative attributes, and yellow boxes representing unimportant variables. SOFA: Sequential Organ Failure Assessment; Activated Partial Thromboplastin Time WBC: White Blood Cell Count BMI: Body Mass Index LDL: Low-Density Lipoprotein PT: Prothrombin Time ALP: Alkaline Phosphatase RDW: Red Cell Distribution Width HDL: High-Density Lipoprotein. TyG: triglyceride glucose index ALT: Alanine Aminotransferase; COPD: Chronic Obstructive Pulmonary Disease CAD: Coronary Artery Disease; CHF: Congestive Heart Failure; HTN: hypertension
Fig. 3
Fig. 3
The relationship for the levels of TyG index with in-hospital mortality, ICU mortality, and 28-day mortality. a–c OR (95% CIs) for in-hospital, in-ICU, and 28-day mortality according to TyG index tertile after adjusting for Adjusted for Age; Gender; BMI; SOFA score; Hemoglobin; Sodium; WBC; RDW; LDL; PT; PTT; ALT; ALP; AST; CRP; NLR; Atrial Fibrillation; Hypertension; Myocardial Infarction; CHF; COPD; CAD; AKI; Low HDL. Error bars indicate 95% CIs. The first tertile is the reference. d Restricted cubic spline for hospital mortality. e Restricted cubic spline for ICU mortality. f Restricted cubic spline for 28-day mortality. OR: odds ratio; CI: confidence interval; ICU: intensive care unit; TyG: triglyceride-glucose
Fig. 4
Fig. 4
Subgroup analyses for the association of TyG index with in-hospital mortality, ICU mortality, and 28-day mortality. OR: odds ratio, CI: confidence interval
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