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Associations of the triglyceride-glucose index with short-term mortality in patients with cardiogenic shock: a cohort study

Lipids in Health and Disease volume 24, Article number: 130 (2025) Cite this article

Abstract

Background

Cardiogenic shock (CS) is a severe cardiac disorder with a high mortality rate. The triglyceride-glucose (TyG) index, a biomarker of insulin resistance, is associated with cardiovascular disease-related mortality. This study aimed to investigate the association between the TyG index and mortality in patients with CS.

Methods

This retrospective cohort study analyzed 727 patients with CS from the Medical Information Mart for Intensive Care IV database. The TyG index was calculated as follows: ln[triglycerides (mg/dL) × fasting blood glucose (mg/dL)/2]. Outcomes included 28-day intensive care unit (ICU) mortality and 28-day in-hospital mortality. Kaplan-Meier survival curve models and Cox proportional hazards regression models were used to evaluate the prognostic significance of the TyG index. Receiver Operating Characteristic (ROC) curve analysis was used to determine the predictive efficacy of the TyG index for mortality. Subgroup analyses were conducted to determine the association between the TyG index and mortality across different groups.

Results

Non-survivors had a significantly higher TyG index (ICU: 9.30 vs. 9.13, p = 0.008; in-hospital: 9.29 vs. 9.13, p = 0.004). Adjusted Cox models showed that each 1-unit increase in the TyG index increased ICU mortality risk by 24% (hazard ratio [HR] = 1.24, 95% confidence interval [CI]:1.04–1.48; p = 0.015) and in-hospital mortality by 44% (HR = 1.44, 95% CI:1.11–1.88; p = 0.007). The Quartile 4 TyG index ICU mortality was increased by 77% (HR = 1.77, 95% CI:1.09–2.89) compared to that for Quartile 1 and in-hospital mortality was increased by 61% (HR = 1.61, 95% CI:1.08–2.38). The area under the ROC curve (AUROC) showed a modest standalone predictive ability of 0.56, but when combined with clinical variables, the AUROC improved to 0.80 (ICU) and 0.78 (in-hospital). Subgroup analyses identified stronger associations in patients ≥ 60 years, females, non-septic, and those with acute myocardial infarction or heart failure.

Conclusions

The TyG index is significantly associated with short-term mortality in patients with CS and may serve as a useful biomarker for risk stratification.

Trial registration

Not applicable.

Introduction

Cardiogenic shock (CS) is a complex syndrome characterized in clinical practice as a cardiac disorder that results in clinical and biochemical evidence of sustained tissue hypoperfusion [1]. Recent studies have indicated that, although the prognosis for CS has improved relative to previous years, the in-hospital mortality rate remains approximately 30–40% [2]. Among patients with CS, 40% had mixed CS, characterized by heart failure (HF) and acute coronary syndrome (ACS), whereas 35% had HF alone and 17% had ACS alone [3]. Considering the high mortality rate of CS and its highly heterogeneous etiology, it is imperative to further investigate simple and validated bioindicators associated with the risk of CS-related mortality.

Insulin resistance (IR) refers to the diminished responsiveness of tissues to insulin, leading to an impaired capacity of insulin to facilitate cellular glucose uptake or inhibit hepatic glucose production [4]. IR is widely recognized as a contributor to metabolic disorders, including hypertension, hyperlipidemia, and hyperglycemia [5]. Moreover, hyperinsulinemia resulting from IR can lead to vascular injury and coronary atherosclerosis [6]. These factors collectively contribute to the initiation and progression of cardiovascular diseases (CVD), including HF and coronary heart disease (CHD) [7, 8]. The triglyceride-glucose (TyG) index, which can rapdily and effectively assess IR, is increasingly used [9, 10]. This assessment contrasts with the Homeostasis Model Assessment of IR (HOMA-IR), which depends on the measurement of insulin, yet still has high sensitivity and specificity for assessing IR [11,12,13]. A growing number of studies have indicated that the TyG index is strongly associated with mortality risk from CVD, including HF and CHD [14, 15]. Currently, only one study has reported that an elevated TyG index is associated with the incidence of CS following acute myocardial infarction (AMICS) and a higher mortality risk [16]. However, whether the TyG index is a effective predictor of short-term mortality in all patients with CS remains unclear.

Consequently, we conducted a study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to investigate the association between the TyG index and the short-term prognosis of patients with CS. This study will provide insights into the metabolic direction of intervention strategies for patients with CS.

Methods

Study cohort

This study used data from the MIMIC-IV database (version 3.0), a well-known publicly available resource managed by the Computational Physiology Laboratory at the Massachusetts Institute of Technology. The database includes patient information collected from 2008 to 2022 of hourly physiological measurements recorded by bedside monitors, which were validated by intensive care unit (ICU) nursing staff. It features a diverse cohort of ICU patients and provides extensive details on demographic factors, medical history, laboratory test results, and prescribed medications (https://doi.org/10.13026/kpb9-mt58). Access to the database was granted upon successful completion of the National Institutes of Health online course titled “Protecting Human Research Participants”, which ensures ethical standards in research involving humans. Specifically, access was awarded to individuals who completed the Collaborative Institutional Training Initiative examination, including the author, Mo (Certification number: 65748833). In accordance with the International Classification of Diseases (ICD), 9th and 10th revisions, this study enrolled patients diagnosed with CS (ICD codes 4019 and E785). The exclusion criteria were: (1) patients < 18 years (2), patients with an ICU stay of < 24 h (3), patients with missing triglyceride data, and (4) patients with missing glucose data. As a result, 727 patients were included in this study (Fig. 1).

Fig. 1
figure 1

Study flowchart

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Data collection

PostgreSQL software (version 13.7.2) was used to extract information. The demographic characteristics included age, sex, and ethnicity. Anthropometric characteristic: body mass index (BMI). The types of admissions to the ICU included the Coronary Care Unit (CCU), Cardiovascular ICU, Medical ICU, and other specialized units. Vital signs, including temperature, heart rate, respiratory rate, systolic blood pressure (SBP), diastolic blood pressure, mean blood pressure, and peripheral capillary oxygen saturation (SpO₂) were recorded as the initial measurements at the time of admission. Comorbidities including type 2 diabetes mellitus (T2DM), hypertension, HF, acute myocardial infarction (AMI), cancer, chronic kidney disease (CKD), cirrhosis, pneumonia, stroke, hyperlipidemia, chronic obstructive pulmonary disease (COPD), acute renal failure (ARF), and sepsis were extracted from the database based on diagnostic codes. Scoring systems, including the Sequential Organ Failure Assessment (SOFA), the Simplified Acute Physiology Score II (SAPS II), and the Oxford Acute Severity of Illness Score (OASIS), were applied within 24 h of patient admission to the ICU. Laboratory blood indicators included white blood cell (WBC) count, red blood cell count, platelet (PLT) count, hemoglobin (Hb) level, blood urea nitrogen (BUN) level, creatinine level, glucose level, triglycerides (TG) level, and the triglyceride-glucose (TyG) index. All hematological indices were obtained during the first assessment following patient admission and adhered to clinical laboratory standards. Treatments included whether patients had continuous renal replacement therapy (CRRT) or mechanical ventilation during hospitalization.

TyG index calculation

The formula for calculating the TyG index is: TyG index = ln (TG (mg/dL) × fasting blood glucose (mg/dL) / 2) [17].

Outcomes

The outcomes of patients with CS in the MIMIC-IV database included the 28-day all-cause in-hospital mortality and 28-day all-cause mortality in the ICU.

Statistical analysis

Statistical analyses were performed using R software (version 4.3.0, Austria) and GraphPad Prism (version 9.5.1, USA). The Shapiro-Wilk test was used to assess the normality of the data. Continuous variables were identified as not normally distributed; therefore, they are presented as median (Quartile 1 - Quartile 3). The Mann-Whitney U test was used for comparisons between two groups of continuous variables, while the Kruskal-Wallis H test was used for comparisons among multiple groups. Categorical variables are expressed as n (%) and analyzed using the chi-square test or Fisher’s exact test. Missing values were addressed using multiple imputation, specifically employing the “mice” package. Variables with excessive missing values (> 30%) were excluded from the analysis [18]. Statistical significance was defined as a two-sided p-value < 0.05.

Box plots illustrate the differences in the TyG index between the deceased and survivor groups of patients with CS. Patients were categorized into four groups (Quartile 1-Quartile 4) based on the quartiles of the TyG index. Kaplan-Meier survival curves were generated for each TyG quartile, and differences in probabilities of survival between groups were assessed using the log-rank test. Subsequently, Cox proportional hazard regression models were used to determine the prognostic value of the TyG index. The TyG index was analyzed as a continuous variable (per 1-unit increase) and as a categorical variable (quartile groups). Three Cox regression models were used to analyze the data. Model 1 was an unadjusted model. Model 2 was adjusted for age, sex, and ethnicity. Model 3 was further adjusted for heart rate, SpO2, hypertension, HF, CKD, cirrhosis, COPD, ARF, sepsis, PLT count, Hb level, BUN level, creatinine level, CRRT, and ventilation status. To address potential multicollinearity among covariates, a bidirectional stepwise regression approach was applied using the Akaike Information Criterion for variable selection. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Receiver Operating Characteristic (ROC) curve analysis was used to assess the predictive capability of the TyG index for 28-day ICU mortality and 28-day in-hospital mortality in patients with CS. To further investigate the applicability of the TyG index across different patient subgroups, subgroup analyses were conducted based on age (< 60 years vs. ≥60 years), sex, race, BMI (< 30 kg/m² vs. ≥30 kg/m²), AMI, HF, and sepsis. In the subgroup analysis, the TyG index was categorized into a binary variable based on the optimal cutoff value from the ROC curve analysis. The variables adjusted in the subgroup analysis were the same as those in the Cox regression Model 3; however, the variables included in the subgroup stratification were excluded.

Results

Patient characteristics

The baseline characteristics of the 727 patients were stratified by the quartiles of the TyG index (Table 1). The median age of the study cohort was 67 years, predominantly male (63.14%), and mostly white ethnicity (56.95%). The distribution of the ICU types revealed that patients in the CCU accounted for the highest percentage. BMI was significantly different across quartiles. Vital signs revealed significant differences in temperature and SBP with minor variations across the quartiles. Comorbidities such as T2DM, HF, AMI, CKD, pneumonia, hyperlipidemia, and sepsis were significantly different across the quartiles (all p < 0.05). Notably, a higher percentage of patients with T2DM, pneumonia, and sepsis was observed in the higher quartiles. All scoring systems (SOFA, SAPS II, and OASIS) indicated worse conditions in the higher quartiles. Laboratory indicators, such as WBC count, PLT count, BUN level, glucose level, TG level, and the TyG index had significant variations across quartiles. Notably, the TyG index increased with increasing quartiles. Treatment interventions, including CRRT and mechanical ventilation, also differed significantly among the quartiles, with higher use in the higher quartiles.

Table 1 Baseline characteristics of individuals classified by quartiles of the TyG index
Full size table

Differences in the TyG index between deceased and surviving patients

Figure 2 illustrates the distribution of the TyG index among patients with CS who survived and those who died during the 28-day ICU period and the 28-day in-hospital period. For patients who died during the 28-day ICU period, the median TyG index was 9.30 (8.84, 9.89), which was significantly higher than that of the surviving patients; 9.13 (8.65, 9.66) (p = 0.008). Similarly, among patients who died during the 28-day in-hospital period, the TyG index was 9.29 (8.82, 9.89), which was significantly higher than that of the surviving patients; 9.13 (8.65, 9.63) (p = 0.004).

Fig. 2
figure 2

Distribution of the triglyceride-glucose (TyG) index in survivors vs. non-survivors. Box plots display the TyG index for survivors and nonsurvivors during the 28-day intensive care unit (ICU) and in-hospital periods. Non-survivors had a higher median (Q1-Q3) TyG index: ICU (9.30 [8.84, 9.89] vs. 9.13 [8.65, 9.66], p = 0.008) and in-hospital (9.29 [8.82, 9.89] vs. 9.13 [8.65, 9.63], p = 0.004)

Full size image

Associations of the TyG index with 28-Day ICU mortality and in-hospital mortality

Kaplan-Meier survival curves (Fig. 3) show that a higher TyG index was associated with a lower 28-day ICU probability of survival (log-rank test: chi-square = 10.8322, p = 0.012) and lower 28-day in-hospital probability of survival (chi-square = 8.2280, p = 0.041).

Fig. 3
figure 3

Kaplan-Meier survival curves by triglyceride-glucose (TyG) index quartiles. Kaplan-Meier survival curves indicated that a higher TyG index is associated with a lower 28-day intensive care unit (ICU) probability of survival (log-rank test: chi-square = 10.8322, p = 0.012) and lower 28-day in-hospital probability of survival (chi-square = 8.2280, p = 0.041)

Full size image

Cox regression models revealed a significant association between a high TyG index and 28-day ICU mortality and 28-day in-hospital mortality (Table 2).

Table 2 Cox regression analysis of the association between the TyG index and mortality in cardiogenic shock
Full size table

Unadjusted model (Model 1): For each 1-unit increase in the TyG index, the risk of ICU mortality increased by 27% (HR = 1.27, 95% CI: 1.08–1.49, p = 0.003). Compared to Quartile 1, the risk of mortality in Quartiles 3 and 4 increased by 92% and 81%, respectively. For each 1-unit increase in the TyG index, the risk of in-hospital mortality increased by 25% (HR = 1.25, 95% CI: 1.08–1.44, p = 0.002), with Quartiles 3 and 4 showing increases in risk of 52% and 65%, respectively.

Adjusted for age, sex, and ethnicity (Model 2): For each 1-unit increase in the TyG index, the risk of mortality increased by 34% (HR = 1.34, 95% CI: 1.15–1.58, p < 0.001), with Quartiles 3 and 4 demonstrating increases in risk of 104% and 108%, respectively. Additionally, each 1-unit increase in the TyG index was associated with a 32% increase in the risk of in-hospital mortality (HR = 1.32, 95% CI: 1.15–1.53, p < 0.001), and Quartiles 3 and 4 showed increases in risk of 62% and 91%, respectively.

Further adjusted for multiple clinical variables (Model 3): for each 1-unit increase in the TyG index, the risk of mortality remained elevated by 24% (HR = 1.24, 95% CI: 1.04–1.48, p = 0.015). The risk of mortality in Quartile 3 increased by 100%, whereas that in Quartile 4 showed a 77% increase. For each 1-unit increase in the TyG index, the risk of in-hospital mortality increased by 44% (HR = 1.44, 95% CI: 1.11–1.88, p = 0.007) and that in Quartile 4 increased by 61% (HR = 1.61, 95% CI: 1.08–2.38, p = 0.018).

The predictive ability of the TyG index for 28-day ICU mortality and in-hospital mortality

Figure 4a illustrates the predictive ability of the TyG index as a singular indicator of 28-day ICU mortality and in-hospital mortality in patients with CS. The area under the ROC (AUROC) curve for the TyG index predicting 28-day ICU mortality was 0.56 (95% CI: 0.52–0.61), with an optimal cutoff value of 9.22, sensitivity of 0.55 (95% CI: 0.51–0.59), and specificity of 0.59 (95% CI: 0.52–0.66). Similarly, the AUROC curve for the TyG index predicting 28-day in-hospital mortality was also 0.56 (95% CI: 0.52–0.61), with the same optimal cutoff value of 9.22, sensitivity of 0.56 (95% CI: 0.51–0.60), and specificity of 0.56 (95% CI: 0.50–0.62).

Fig. 4
figure 4

Predictive ability of the triglyceride-glucose (TyG) index for mortality. Figure 4a Receiever operating characteristic (ROC) curve for the TyG index alone. The area under the ROC curve (AUROC) for predicting 28-day intensive care unit (ICU) mortality was 0.56 (95% condifendence interval [CI]: 0.52–0.61) with an optimal cutoff value of 9.22, sensitivity of 0.55 (95% CI: 0.51–0.59), and specificity of 0.59 (95% CI: 0.52–0.66). For in-hospital mortality, the AUROC curve was also 0.56 (95% CI: 0.52–0.61) with the same cutoff value, sensitivity of 0.56 (95% CI: 0.51–0.60), and specificity of 0.56 (95% CI: 0.50–0.62). Figure 4b: ROC curve for the combined model (Model 3) incorporating the TyG index and multiple clinical variables. The AUROCcurve for predicting 28-day ICU mortality was 0.80 (95% CI: 0.76–0.83), and for in-hospital mortality, it was 0.78 (95% CI: 0.74–0.81)

Full size image

Figure 4b shows the results off Model 3 in the Cox regression analysis, demonstrating that the predictive performance significantly improved when multiple variables (age, sex, ethnicity, heart rate, SpO2, hypertension, HF, CKD, cirrhosis, COPD, ARF, sepsis, PLT count, Hb level, BUN level, creatinine level, CRRT, and ventilation status) were considered. The AUROC curve for the model predicting 28-day ICU mortality was 0.80 (95% CI: 0.76–0.83), while the AUROC curve for the model predicting 28-day in-hospital mortality was 0.78 (95% CI: 0.74–0.81).

Subgroup analysis

For patients with CS, a high TyG index (> 9.22) was associated with a significantly increased risk of ICU mortality. Subgroup analyses indicated that this association was particularly pronounced among patients ≥ 60 years, females, individuals with white ethnicity, individuals with a BMI < 30 kg/m², those with AMI, patients with HF, and patients without sepsis. Interaction analyses revealed that the interaction among the different subgroups was significant only within the sepsis subgroup (Fig. 5).

Fig. 5
figure 5

Subgroup analysis of the triglyceride-glucose (TyG) index for intensive care unit (ICU) mortality

Full size image

Subgroup analysis of the TyG index with regard to hospital mortality indicated that a high TyG index was more strongly associated with an increased risk of mortality in patients > 60 years, females, individuals of white ethnicity, those with a BMI < 30 kg/m², and patients without sepsis. The interaction analysis revealed that interaction among the different subgroups was significant only within the sepsis subgroup (Fig. 6).

Fig. 6
figure 6

Subgroup analysis of the triglyceride-glucose (TyG) index for in-hospital mortality

Full size image

Discussion

In this study, the MIMIC-IV database was used to investigate the relationship between the TyG index and mortality in patients with CS. Our findings show that the TyG index is significantly associated with 28-day ICU mortality and 28-day in-hospital mortality among patients with CS. Although the standalone predictive capacity of the TyG index is modest, combining it with clinical parameters significantly enhanced its prognostic accuracy. Our study also found that this association was particularly pronounced among patients ≥ 60 years, females, individuals with white ethnicity, individuals with a BMI < 30 kg/m², and those with AMI and HF. These results underscore the importance of the TyG index as a simple and effective biomarker for identifying patients with CS at an elevated risk of mortality, highlighting its broad applicability in clinical practice.

Multiple studies have demonstrated that the TyG index serves as an independent predictor of mortality among patients in the ICU, those with AMI, HF, or ischemic stroke [19,20,21,22,23]. To the best of our knowledge, only one study has reported a relationship between the TyG index and CS. Liu et al. [16] reported that an elevated TyG index is associated with and an increased risk of mortality in patients with AMICS. The current study and that by Liu et al. [16] examined the relationship between the TyG index and CS, highlighting the potential role of the TyG index in predicting risk of mortality among patients with CS. However, there is a notable difference between our study and that of Liu et al. Their study focused specifically on AMICS and explored the association between the TyG index and the incidence and prognosis of AMICS [16]. In contrast, our study investigated the broader context of CS, encompassing various etiologies beyond AMI, such as HF and sepsis. Additionally, our study demonstrated that the TyG index has a stronger association with mortality in patients without sepsis, a finding that warrants further discussion. However, the role of the TyG index in the prognosis of patients with sepsis remains controversial. Some studies have indicated that an elevated TyG index is associated with poor outcomes [24, 25], whereas others have suggested that an elevated TyG index may serve as a protective prosnostic factor [26]. The controversy may have arisen due to differences in cohort selection or the complex relationship between the TyG index and patients with sepsis. Future studies involving larger-scale clinical cohorts are necessary to fully elucidate the relationship between the TyG index and sepsis.

The significant association between the TyG index and mortality in patients with CS indicates that IR, as assessed using the TyG index, may play a crucial role in the pathophysiology and prognosis of CS. This relationship can be elucidated through several potential mechanisms, including impaired myocardial metabolism, endothelial dysfunction, inflammation, hyperglycemia, autonomic dysfunction, and the presence of comorbidities. IR reduces the ability of insulin to promote glucose uptake and utilization in myocardial cells, leading to a metabolic shift from glucose to fatty acid oxidation [27, 28]. This shift reduces the efficiency of energy production in the heart during periods of increased demand, such as ischemia or HF, resulting in greater vulnerability of myocardial cells to ischemic injury and dysfunction, which can worsen clinical outcomes in patients with CS [29]. IR is closely linked to endothelial dysfunction, characterized by reduced nitric oxide production and increased oxidative stress [30]. This dysfunction can result in decreased coronary blood flow, increased vascular stiffness, and impaired microcirculatory function, all of which contribute to the preogression of CVD [31,32,33]. IR is associated with inflammation [34, 35], which fosters the development and progression of atherosclerosis. This inflammation can result in instability and rupture of plaque instability, potentially triggering ACS and leading to CS [36]. Furthermore, the pro-inflammatory environment associated with IR can intensify myocardial injury and compromise cardiac function [37], further worsening the prognosis of patients with CS. An increased TyG index is frequently associated with hyperglycemia, a common characteristic among critically ill patients [38], including those with CS. Stress-induced hyperglycemia and an elevated blood glucose level due to acute illness are associated with increased mortality and adverse outcomes in critically ill patients [39, 40]. Hyperglycemia can impair immune function, elevate the oxidative stress level, and aggravate myocardial injury [41], thereby contributing to the higher mortality observed in patients with CS and an elevated TyG index. IR can also affect cardiac autonomic function, resulting in increased sympathetic activity and diminished parasympathetic tone [42, 43]. The TyG index is strongly correlated with numerous comorbidities, such as hypertension, T2DM, and obesity, which are well-established risk factors for CVD and adverse outcomes in CS [44, 45]. An elevated TyG index may signify the presence of multiple comorbidities [44]. The TyG index affects the prognosis of patients with CS through multiple mechanisms; thus, further studies are essential for a comprehensive exploration.

This study makes several innovative contributions to the prediction of mortality in patients with CS using the TyG index. First, a diverse cohort of patients with CS with various etiologies was investigated, rather than limiting the focus to AMI. This broad scope allowed exploration of the generalizability of the TyG index across different underlying conditions, thereby enhancing its applicability in clinical practice. Second, this study revealed an independent association between the TyG index and short-term mortality in patients with CS, providing detailed subgroup specificity. Finally, the findings of this study have significant practical implications for clinical practice. As a simple and easy to calculate biomarker, the TyG index can be readily implemented in clinical settings to provide early identification of patients at high-risk and guide targeted interventions aimed at improving outcomes for patients with CS.

Despite providing valuable insights into the relationship between the TyG index and mortality in patients with CS, this study has several limitations that should be acknowledged. First, although there was adjustment for multiple confounders in the statistical models, unmeasured or residual confounding factors may have influenced the observed associations. Second, due to the limitations of the database, the TyG index was exclusively used as an indicator of IR and other biomarkers related to IR, such as HOMA-IR, were not investigated. Third, the TyG index was only measured at the time of patient admission and dynamic monitoring was not performed. Future studies should incorporate dynamic monitoring of the TyG index to evaluate its effects on mortality in patients with CS. Finally, the MIMIC-IV database only records all-cause mortality, which limits the ability to conduct stratified analyses.

Conclusion

In conclusion, our study demonstrated that the TyG index is significantly associated with short-term mortality in patients with CS, highlighting its potential as a useful biomarker for risk stratification in clinical practice.

Data availability

The datasets produced and analyzed in this research are accessible in the MIMIC-IV database (https://doi.org/10.13026/kpb9-mt58).

Abbreviations

CS:

Cardiogenic Shock

HF:

Heart Failure

ACS:

Acute Coronary Syndrome

IR:

Insulin Resistance

CVD:

Cardiovascular Diseases

CHD:

Coronary Heart Disease

TyG:

Triglyceride-Glucose

HOMA-IR:

Homeostasis Model Assessment of IR

ICU:

Intensive Care Unit

MIMIC-IV:

Medical Information Mart for Intensive Care IV

ICD:

International Classification of Diseases

BMI:

Body Mass Index

SBP:

Systolic Blood Pressure

SpO₂:

Peripheral Capillary Oxygen Saturation

T2DM:

Type 2 Diabetes Mellitus

AMI:

Acute Myocardial Infarction

CKD:

Chronic Kidney Disease

COPD:

Chronic Obstructive Pulmonary Disease

ARF:

Acute Renal Failure

SOFA:

Sequential Organ Failure Assessment

SAPS II:

Simplified Acute Physiology Score II

OASIS:

Oxford Acute Severity of Illness Score

WBC:

White Blood Cell

PLT:

Platelet

Hb:

Hemoglobin

BUN:

Blood Urea Nitrogen

TG:

Triglycerides

CRRT:

Continuous Renal Replacement Therapy

AUROC:

Area under the Receiver Operating Characteristic Curve

HR:

Hazard Ratio

CI:

Confidence Interval

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Acknowledgements

We express our gratitude to all participants in the MIMIC-IV study and the project team. We would like to thank Editage (www.editage.cn) for English language editing.

Funding

This work was supported by the Shandong Provincial Natural Science Foundation (Grant ID: ZR2024MH214) and Qingdao Outstanding Health Professional Development Fund.

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Authors and Affiliations

  1. School of Medicine, Qingdao University, Qingdao, 266000, China

    Degang Mo, Peng Zhang & Mengmeng Wang

  2. Department of Cardiology, Qingdao Municipal Hospital, No.5 Donghai Middle Road, Qingdao, 266071, China

    Degang Mo, Peng Zhang, Jun Guan & Hongyan Dai

Authors
  1. Degang Mo
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  2. Peng Zhang
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  3. Mengmeng Wang
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Contributions

D.M. was responsible for the design and conceptualization of the study, in addition to drafting and revising the manuscript. D.M. and P.Z. contributed to data collecting, statistical analysis, and result interpretation. M.W. was responsible for the data results visualization. H.D. and J.G. read and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jun Guan or Hongyan Dai.

Ethics declarations

Ethics approval and consent to participate

The study was performed according to the guidelines of the Helsinki Declaration. The use of the MIMIC-IV database was approved by the review committee of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The data is publicly available (in the MIMIC-IV database), therefore, the ethical approval statement and the requirement for informed consent were waived for this study.

Competing interests

The authors declare no competing interests.

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Mo, D., Zhang, P., Wang, M. et al. Associations of the triglyceride-glucose index with short-term mortality in patients with cardiogenic shock: a cohort study. Lipids Health Dis 24, 130 (2025). https://doi.org/10.1186/s12944-025-02548-z

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X