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The glycemic gap as a prognostic indicator in cardiogenic shock: a retrospective cohort study

BMC Cardiovascular Disorders volume 24, Article number: 468 (2024) Cite this article

Abstract

Background

Stress-induced hyperglycemia (SIH) is associated with poor outcomes in cardiogenic shock (CS), and there have been inconsistent results among patients with or without diabetes mellitus (DM). The glycemic gap (GG) is derived by subtracting A1c-derived average glucose from blood glucose levels; it is a superior indicator of SIH. We aimed to explore the role of GG in the outcomes of patients with CS.

Methods

Data on patients diagnosed with CS were extracted from the MIMIC-IV v2.0 database to investigate the relationship between GG and 30-day mortality (Number of absolute GG subjects = 359; Number of relative GG subjects = 357). CS patients from the Second Affiliated Hospital of Wenzhou Medical University were enrolled to explore the correlation between GG and lactic acid (Number of absolute GG subjects = 252; Number of relative GG subjects = 251). Multivariate analysis, propensity score-matched (PSM) analysis, inverse probability treatment weighting (IPTW), and Pearson correlation analysis were applied.

Results

Absolute GG was associated with 30-day all-cause mortality in CS patients (HRadjusted: 1.779 95% CI: 1.137–2.783; HRPSM: 1.954 95% CI: 1.186–3.220; HRIPTW: 1.634 95% CI: 1.213–2.202). The higher the absolute GG level, the higher the lactic acid level (βadjusted: 1.448 95% CI: 0.474–2.423). A similar trend existed in relative GG (HRadjusted: 1.562 95% CI: 1.003–2.432; HRPSM: 1.790 95% CI: 1.127–2.845; HRIPTW: 1.740 95% CI: 1.287–2.352; βadjusted:1.294 95% CI: 0.369–2.219). Subgroup analysis showed that the relationship existed irrespective of DM. The area under the curve of GG combined with the Glasgow Coma Scale (GCS) for 30-day all-cause mortality was higher than that of GCS (absolute GG: 0.689 vs. 0.637; relative GG: 0.688 vs. 0.633). GG was positively related to the triglyceride-glucose index. Kaplan–Meier curves revealed that groups of higher GG with DM had the worst outcomes. The outcomes differed among races and GG levels (all P < 0.05).

Conclusions

Among patients with CS, absolute and relative GGs were associated with increased 30-day all-cause mortality, regardless of DM. The relationship was stable after multivariate Cox regression analysis, PSM, and IPTW analysis. Furthermore, they reflect the severity of CS to some extent. Hyperlactatemia and insulin resistance may underlie the relationship between stress-induced hyperglycemia and poor outcomes in CS patients. They both improve the predictive efficacy of the GCS.

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Background

Cardiogenic shock (CS) is common in critically ill patients with cardiovascular disease; it is caused by severe impairment of myocardial performance, resulting in diminished cardiac output, life-threatening end‐organ hypoperfusion, and hypoxia [1, 2]. Although reperfusion therapy has advanced, 30-day mortality following acute myocardial infarction remains high, at about 50% from 2010 to 2017 [3]. For these reasons, risk stratification of CS patients is essential.

Stress-induced hyperglycemia (SIH) is the relative transient increase in glucose during a critical illness such as CS [4]. Numerous studies found that SIH during critical illness is related to an increased risk of morbidity and mortality [5]. For example, Shen et al. found that elevated levels of stress hyperglycemia ratio, a biomarker for SIH, are associated with increased in-hospital mortality and prolonged length of stay in stroke patients [6]. Interestingly, this conclusion has inconsistent results among patients with or without diabetes mellitus (DM). Some studies report that high glucose levels on admission were associated with increased mortality in DM and non-DM CS patients [7, 8]. However, a study showed that serum glucose levels were not associated with mortality for CS patients with DM [9]. There may be several reasons behind this phenomenon. First, the blood glucose level of patients with DM on admission may be affected by hypoglycemic drugs and cannot reflect the proper level of SIH. Second, long-term exposure to hyperglycemia downregulates glucose transportation capacity for patients with DM and protects cells from glucose toxicity [10]. HbA1c reflects the glucose level in the preceding three months. A1c-derived average glucose (ADAG, mg/dl) was calculated by the formula (28.7 × HbA1c [%])–46.7), representing the chronic glycemia level [11]. In recent years, the glycemic gap (GG), derived by ADAG from blood glucose levels, has been proven to be a superior indicator of SIH as it improves the accuracy of assessment by removing the impact of chronic hyperglycemia on the evaluation of disease severity [12, 13].

To date, the predictive value of GG has not been determined in patients with CS. Therefore, the present study aimed to investigate whether GG is associated with 30-day all-cause death in a population with CS, and whether the relationship is the same in people with and without DM. Moreover, we intended to investigate the potential mechanisms linking GG to CS. The absolute GG represents the absolute increase in glycemia. Considering the magnitude of the absolute GG might represent a different risk depending on the average plasma glucose,we also assessed the impact of the relative GG on mortality risk [14]. We hypothesize that the relationship between absolute or relative GG and CS outcomes is irrespective of DM status.

Herein, we conducted a retrospective analysis using data from the Medical Information Mart for Intensive Care-IV database version 2.0 (MIMIC-IV v2.0) to examine the prognostic significance of GG in CS patients. Hyperlacticaemia is commonly seen in shock patients; it is an independent adverse prognostic factor for CS patients [15, 16]. We intended to explore the relationship between GG and lactic acid by collecting data from the Second Affiliated Hospital of Wenzhou Medical University (Fey), which may be a linking underlie the relationship between GG and prognosis in cardiogenic shock.

Methods

Data source

In this retrospective study, we used data from MIMIC-IV v2·0, a publicly available relational database provided by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology, which includes information on critical care patients who were admitted to the intensive care unit (ICU) at the Beth Israel Deaconess Medical Center from 2008 to 2019. Patient identifiers were removed according to the Health Insurance Portability and Accountability Act Safe Harbor provision [17]. Details of the MIMIC-IV database have been described elsewhere [18]. One of the authors (Xu) completed the Collaborative Institutional Training Initiative examination and had access to the database (Certificate number 40053337).

The Institutional Research and Ethics Institute of Fey approved this study involving human subjects data. The data collected was anonymous, so no informed consent was necessary. The study was conducted per the Declaration of Helsinki, and an ethical lot number of 2023-K-136–01 was assigned.

Population selection criteria

We included all patients diagnosed with CS using the International Classification of Diseases (ICD)-9/10 diagnosis codes in the MIMIC-IV v2.0 database from 2008 to 2019. The ICD-9 codes for CS are as follows: 78,551 and 99,801. The ICD-10 codes for CS are T8111, T8111XA, T8111XD, T8111XS and R570. To rule out the effects of multiple previous ICU stays, we only included patients admitted to the hospital and ICU for the first time. The target population is the adult population and thus we removed patients with age < 16. Considering that there are human factors beyond our control, we only collected patients who stayed longer than 24 h. HbA1c and blood glucose are necessary items for calculating the GG and thus we excluded patients who missed either one. To avoid the influence of outliers of GG, we have eliminated them. The method of defining outliers (GG exceeds the mean ± three standard deviations [SD]) applies to any distributed data.

For data from Fey, we included adult patients with discharge diagnoses from February 2012 to May 2023: cardiogenic shock or Killip IV. We excluded patients whose HbA1c, blood glucose, or lactic acid values were missing. We also excluded patients with outliers whose absolute/relative GG values exceeded the mean ± three SD. Patients with baseline information missing by more than 10% were also excluded.

Data extraction

First, we extracted patients with CS according to ICD-9/10 codes from MIMIC-IV using Structured Query Language (inclusion number = 2547). Then, we only included patients admitted to the hospital and ICU for the first time (exclusion number = 790). Next, we excluded patients younger than 16 (N = 0). And we excluded patients who stayed in the ICU for less than 24 h (N = 212). We also collected the baseline data on admission to the ICU on the study population, including demographic data, primary vital signs, comorbidities, basic laboratory parameters, Glasgow coma score (GCS), and some therapeutic measures. The initial measured value was used when there were multiple records for an indicator. Then, we excluded patients who lacked values of HbA1c (N = 1178) or blood glucose (N = 4). The absolute GG had four outliers. The relative GG had six outliers. Finally, 359 patients were included in the group of absolute GG (Fig. 1a), and 357 patients were included in the group of relative GG (Fig. S1a). If the missing variables were more significant than 10%, the variable would not be included. We analyzed the variables included in the MIMIC-IV v2.0 cohort. Only two albumin values were missing in the absolute GG cohort and one in the relative GG cohort. None of the other variables were missing (Table S1). Given the small amount missing, we set the missing part to null for a missing variable of less than 10%. Demographic information included age, gender, and ethnicity. Vital signs on admission included systolic blood pressure, diastolic blood pressure (DBP), mean arterial pressure, heart rate, respiratory rate, temperature, and pulse oximetry-derived oxygen saturation (SpO2). Complications included myocardial infarction, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, DM, chronic kidney disease (CKD), and cancer. Laboratory parameters included HbA1c, blood glucose, hemoglobin, platelet, white blood cell count (WBC), international normalized ratio, prothrombin time (PT), activated partial thromboplastin time, albumin, serum urea nitrogen, and creatinine. We also collected whether insulin, vasopressin, and renal replacement treatment (RRT) were used during hospitalization. The baseline data obtained from Fey were nearly identical to the collected data from MIMIC-IV cohort.Besides,we collected variables, including ejection fraction, N-terminal pro-brain natriuretic peptide, cardiac troponin I, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, lactic acid, intra-aortic balloon pump (IABP), mechanical ventilation (MV), which missed greater than 10% in MIMIC-IV database. ADAG (mg/dl) was calculated by the formula ((28.7 × HbA1c [%])–46.7) [11]. Absolute GG (mg/dl) was calculated as the first admittance of blood glucose – ADAG. Relative GG was calculated as absolute glycemic gap/ADAG × 100%.

Fig. 1
figure 1

Flow chart of study population of absolute glycemic gap. Notes: (a) Flow chart of study population of absolute glycemic gap from MIMIC-IV v2.0; (b) Flow chart of study population of absolute glycemic gap from the Second Affiliated Hospital of Wenzhou Medical University. Abbreviations: ICD, International Classification of Diseases; ICU, intensive care unit; IPTW, inverse probability treatment weight; MIMIC-IV v2.0, medical information mart for intensive care-IV database version 2.0; PSM, propensity score match

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Endpoint and follow-up

The outcome of MIMIC-IV cohort was 30-day all-cause mortality, followed by the patient’s admission. The date of death in MIMIC-IV was obtained from Social Security Death Index records from the U.S. government. And the outcome of Fey cohort was serum lactic acid, which generated for the first time during hospitalization.

Statistical analysis

First, we conducted a smooth curve fitting to examine the relationship between CS patients’ GG and 30-day all-cause mortality. Four knots were used in the restricted cubic spline analysis (Fig. S2). We can see an evident inflection point between CS patients’ relative GG and 30-day all-cause mortality (Fig. S2). Then, we apply segmented (piece-wise) regression that uses a separate line segment to fit each interval. A log-likelihood ratio test comparing a one-line (non-segmented) model to a segmented regression model was used to determine whether a threshold exists. The inflection point that connecting the segments was based on the model gives maximum likelihood, and it was determined using the two-step recursive method. Table S2 shows that the P-value of the logarithmic likelihood ratio tests is both > 0.05, indicating a linear relationship between the GG and 30-day all-cause mortality of CS patients. The GG values were categorized into dichotomous groups, and the maximum value of the lower absolute/relative GG group was equal to 32.59 mg/dl and 24.77, respectively, in the MIMIC-IV v2.0 cohort. In the Fey cohort, the lower absolute/relative GG group’s maximum value was 50.00 mg/dl and 43.23, respectively. The following analysis set the lower absolute/relative GG level groups as the reference groups. Categorical variables were expressed by frequency (percentage), and the Chi-square test or Fisher’s exact test was used to compare the different groups. Continuous variables were represented by the mean (SD) and compared with the Kruskal–Wallis H-test or variance analysis. We established models 1–3 to investigate the association between GG and 30-day all-cause mortality among CS patients using COX’s proportional hazards regression analysis.The results were expressed as hazard ratios [HR] (95% confidence interval [95% CI]). And models 4–6 were established to evaluate the relationship between GG and lactic acid among CS patients using linear regression analysis, with the results expressed as β (95% CI).The selection of covariates was based on the estimated value of impact > 10% [19] or were considered meaningful by clinicians in our model.Model1 and model4 were adjusted for nothing. Model 2 was adjusted for age, gender, and ethnicity. For absolute GG, model 3 was adjusted for model2 + hemoglobin BUN, DM, vasopressin, SpO2, albumin, creatinine, CKD, RRT, and GCS. For relative GG, model 3 was adjusted for model2 + BUN, DM, CKD, SpO2, hemoglobin, albumin, creatinine, vasopressin, and RRT. Model 5 was adjusted for age and gender. For absolute GG, model6 was adjusted for model5 + IABP, MV, vasoactive agent, CRRT, white blood count, PT, glutamic-pyruvic transaminase (ALT), glutamic oxaloacetic transaminase (AST), uric acid, cholesterol, kidney dysfunction, hyperlipemia, temperature, and SpO2. For relative GG, model6 was adjusted for model5 + IABP, MV, vasoactive agent, continuous renal replacement therapy(CRRT), white blood count, neutrophil percentage, PT, ALT, AST, serum urea nitrogen, creatine, uric acid, cholesterol, kidney dysfunction, chronic obstructive pulmonary disease, hypertension, heart rate, DBP, SpO2. Table S3 summarized variables used in modeling. Propensity score matching (PSM) and inverse probability treatment weighting (IPTW) were also used to adjust the covariates to ensure the robustness of our findings [20, 21]. PSM was performed at a ratio of 1:1 using a caliper width of 0.05 of the SD of the logit of the propensity score. Then, we established two PSM-adjusted and IPTW-adjusted Cox proportional-hazards models separately to explore whether the relationship between GG and 30-day all-cause mortality still existed stably. Risks of 30-day all-cause mortality in different subgroups (low GG, high GG) (non-DM/low GG, non-DM/high GG, DM/low GG, DM/high GG) (White/low GG, White/high GG, Black/low GG, Black/high GG,Others/low GG, Others/high GG) were presented by Kaplan–Meier survival curves and compared by log-rank test. The subgroup analysis was performed to explore if the association differed among subgroups classified using different parameters, including age, gender, ethnicity, DM, and insulin usage. A receiver operating characteristic (ROC) curve was used to estimate the predictive value of the GCS and GCS plus absolute/relative GG for 30-day all-cause mortality in patients with cardiogenic shock. The areas under the ROC curves (AUCs) were compared using the DeLong test. The predictive capabilities of the models were further assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Pearson analysis was adopted to analyze the relationship between absolute or relative GG and triglyceride-glucose index (TyG). All the analyses were conducted using R software (Version 3.6.1, http://www.r-project.org). A two-tailed P-value of < 0.05 indicated a significant difference.

Results

Characteristics of the study population

According to inclusion and exclusion criteria, 359 patients were included for analysis in the group of the absolute glycemic gap(Fig. 1a), and 357 patients were included for analysis in the group of the relative glycemic gap in MIMIC-IV v2.0 (Fig.S1a). The baseline characteristics of the study population of the absolute GG were shown in Table 1. The baseline characteristics of the study population of the relative GG are shown in Table S4. In the absolute glycemic gap cohort, 119 women and 240 men have a mean age of 66.29 ± 14.13 years and a mean GG level of 52.02 ± 87.57mg/dl. In relative glycemic gap cohort, there are 116 women and 241 men with a mean age of 66.31 ± 14.10 years and a mean GG level of 38.86 ± 57.73. According to the GG level, the eligible participants were divided into two groups (≤ 32.59 mg/dl, > 32.59 mg/dl; Table 1) (≤ 24.77, > 24.77; Table S4). The platelet and WBC levels were significantly higher in the high GG group. Moreover, they are more likely to suffer from myocardial infarction. Insulin use was also higher. The study subjects also comprised patients with CS between February 2012 and May 2023 from the Fey cohort (Fig. 1b and Fig.S1b). In the Fey cohort, 94 women and 158 men with a mean age of 70.36 ± 14.39 years and a mean GG level of 70.44 ± 91.11mg/dl were included for analysis in group of absolute glycemic gap (Table 2), 95 women and 156 men with a mean age of 70.51 ± 14.23 years, and a mean GG level of 67.50 ± 157.34 were included for analysis in group of relative glycemic gap (Table S5). According to the GG level, the eligible participants were divided into two groups (absolute GG: ≤ 50.00 mg/dl, > 50.00 mg/dl; Table 2) (relative GG: ≤ 43.23, > 43.23; Table S5). Similar to the cohort from MIMIC-IV v2.0, the level of WBC was significantly higher in the high GG group. The level of lactic acid was also higher in the high GG group. And they were likelier to use IABP and MV (all P < 0.05).

Table 1 Baseline characteristics of participants based on absolute glycemic gap in MIMIC-IV v2.0 before and after PSM and IPTW
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Table 2 Baseline characteristics of participants based on absolute glycemic gap in the Second Affiliated Hospital of Wenzhou Medical University
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The association between GG and mortality

During the 30-day follow-up period, 102 deaths were recorded (Table 1 and S4). Three regression models were presented after adjusting for possible confounding variables (Table 3 and S6). When the absolute glycemic gap was analyzed as a categorical variable, the HR (95% CI) of the high absolute GG level group (> 32.59 mg/dl) was 1.803 (1.209, 2.688) in model 1, compared with the low GG level group (≤ 32.59 mg/dl). After adjusting for age, gender, and ethnicity in model 2, HR (95% CI) for 30-day all-cause mortality was 1.796 (1.196, 2.697). After adjusting for age, gender, ethnicity, hemoglobin, BUN, DM, vasopressin, SpO2, albumin, creatinine, and CKD, RRT, and GCS in model 3, HR (95% CI) for 30-day all-cause mortality was 1.779 (1.137, 2.783), which was still statistically significant. When the absolute GG was analyzed as a continuous variable, the HR was more than one in all three models (Table 3). A similar correlation was observed between 30-day all-cause mortality and relative GG (HR (95% CI) were 1.623 (1.092, 2.411) in model, 1.581 (1.059, 2.362) in model 2 and 1.562 (1.003, 2.432) in model 3) (Table S6). In addition, we divided the absolute/relative GG into three groups and four groups, respectively, and three models were also established to evaluate the relationship between the GG and the outcomes of CS patients (Table 3 and S6). For 30-day all-cause mortality, the HR of the group with the highest GG level was more than one in all three models, compared with the group with the lowest GG level (P < 0.05), irrespective of GG being divided into three or four groups. Above all, both absolute and relative GG were risk prognostic factors for 30-day all-cause mortality in CS patients.

Table 3 Association between absolute glycemic gap and 30-day all-cause mortality among patients with cardiogenic shock using COX’s proportional hazards regression model
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PSM and IPTW

After PSM, 133 pairs of patients are included in the absolute glycemic gap cohort, and 137 pairs of patients are included in the relative glycemic gap cohort (Fig. 1a and S1a). The baseline characteristics of patients after PSM and IPTW are shown in Table 1 and S4, respectively. There were no significant differences between the two groups. After PSM and IPTW, Cox proportional hazard regression analysis revealed that high absolute GG level was independently related to worse outcomes (Table 4)(PSM: HR: 1.954; 95% CI: 1.186–3.220; P = 0.009) (IPTW: HR: 1.634; 95% CI: 1.213–2.202; P = 0.001). A similar correlation was observed between 30-day all-cause mortality and relative GG (Table S7) (PSM: HR: 1.790; 95% CI: 1.127–2.845; P = 0.014) (IPTW: HR: 1.740; 95% CI: 1.287–2.352; P < 0.001). From the above we can know that the relationship between GG and 30-day all-cause mortality among CS patients remained stable after PSM and IPTW.

Table 4 Association between absolute glycemic gap and 30-day all-cause mortality among patients with cardiogenic shock using COX’s proportional hazards regression model after PSM and IPTW
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Kaplan–Meier analysis

The GG was significantly associated with the incidence of 30-day all-cause mortality in all participants, as shown in Fig. 2(a) and S3(a). The DM/high GG combination group had the highest risk of 30-day all-cause death compared with the other groups no matter the absolute GG (P = 0.0130) (Fig. 2b) or relative GG (P = 0.0292) (Fig. S3b). There existed statistic difference of survival analysis across populations among different level of the GG with other ethnicities (log-rank P for absolute GG = 0.0278 in Fig. 2(c); log-rank P for relative GG = 0.0025 in Fig.S3c).

Fig. 2
figure 2

Survival analysis. Notes: (a) Survival analysis across populations among different level of absolute glycemic gap; (b) Survival analysis across populations among different level of absolute glycemic gap with different glucose status; (c) Survival analysis across populations among different level of absolute glycemic gap with different ethnics. Abbreviations: DM, diabetes mellitus; GG, glycemic gap

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Subgroup analyses

Upon analyzing subgroups stratified by various factors, including age, sex, race, glucose status (DM or non-DM) and usage of insulin or not, all interaction P-values exceeded 0.05, underscoring the stability and reliability of our findings. And the results remained stable either GG analyzed as a continuous variable (Fig. 3a and S4a) or categorical variable (Fig. 3b and S4b).

Fig. 3
figure 3

Subgroup analysis. Notes: (a) Subgroup analysis of the associations between absolute glycemic gap as a continuous variable and 30-day all-cause mortality among patients with cardiogenic shock; (b) Subgroup analysis of the associations between absolute glycemic gap as a categorical variable and 30-day all-cause mortality among patients with cardiogenic shock. Abbreviations: CI, confidence interval; DM, diabetes mellitus; HR, hazard ratio

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ROC curve analysis

ROC curve analysis was conducted to examine further the possible predictive effect of the GG in predicting 30-day all-cause mortality of CS patients. The GCS incorporated with GG could increase the predictive capacity for 30-day all-cause mortality and increase the area under the ROC from 0.637 to 0.689 (absolute GG, Fig. 4), 0.633 to 0.688 (relative GG, Fig. S5) (P all < 0.05). The inclusion of GG into the GCS model also increased the values of NRI and IDI, all of which were statistically significant (absolute GG, Table 5) (relative GG, Table S8).

Fig. 4
figure 4

ROC curve analysis. Notes: Model1 only for GCS.Model2 was for GCS plus absolute glycemic gap. The area under the curve of GCS + glycemic gap was higher than that of GCS (0.689 vs. 0.637,P = 0.0221). Abbreviations: AUC, area under curve; GCS, Glasgow coma scale; ROC, receiver operating characteristic

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Table 5 The discrimination ability of absolute glycemic gap plus GCS versus GCS for 30-day all-cause mortality in cardiogenic shock patients
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Analysis of the relationship between the absolute or relative GG and TyG

Scatter plots revealed a positive correlation between absolute or relative GG and TyG (Fig. 5: Rab = 0.5900 in the total study population, Rab = 0.4817 in DM, Rab = 0.6965 in non-DM; Fig.S6: Rre = 0.1689 in the total study population, Rre = 0.2597 in DM, and Rre = 0.2223 in non-DM). Kaplan–Meier analysis showed that the high GG group with DM had the worst prognosis. To explore the potential mechanisms behind it, we investigated the TyG levels among different levels of glycemic gap with varying glucose statuses. And the results showed that the group with high glycemic gap with DM mellitus had the highest level of TyG (Table 6 and Table S9).

Fig. 5
figure 5

Pearson analysis between absolute glycemic gap and TyG. Notes: Scatter plot showed a positive correlation between absolute glycemic gap and TyG in Fig. 5. [Rab = 0.5900 in total study population (a), Rab = 0.4817 in diabetes (b), Rab = 0.6965 in non-diabetes (c)]. Abbreviations: TyG, triglyceride-glucose index

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Table 6 TyG among different level of absolute glycemic gap with different glucose status
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Association between the GG and lactic acid

In the Fey cohort, we can find that serum lactic acid level was higher in groups with a higher glycemic gap either in absolute GG (Table 2) or relative GG (Table S5). After adjusting for possible confounding variables, three regression models are displayed in Table 7 and S10. When the glycemic gap was analyzed as a continuous variable, the β was more than zero in all three models. When the absolute glycemic gap was interpreted as a categorical variable, β (95% CI) of the high absolute GG level group (> 50.00 mg/dl) was 1.689 (0.679, 2.698) in model 4, compared with the low GG level group (≤ 50.00 mg/dl). After adjusting for age and gender in model 5, β (95% CI) was 1.688 (0.671, 2.705). After adjusting for age, gender, IABP, MV, vasoactive agent, CRRT, white blood count, PT, ALT, AST, uric acid, cholesterol, kidney dysfunction, hyperlipemia, temperature, SpO2 in model6, β (95% CI) was 1.448 (0.474, 2.423), which was still statistically significant. A similar correlation was observed between lactic acid and relative GG (β [95% CI] were 1.804 [0.793, 2.815] in model 4, 1.811 [0.790, 2.832] in model 5 and 1.294 [0.369, 2.219] in model 6).Consequently,we can conclude that GG reflected severity of CS patients to some extent.

Table 7 Association between absolute glycemic gap and lactic acid among patients with cardiogenic shock using linear regression model
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Discussion

First, whether analyzed as a continuous or categorical variable, absolute glycemic and relative glycemic gaps were both stable and independent risk factors for increased 30-day all-cause mortality in patients with CS before and after adjusting the covariates by multivariate Cox proportional-hazards analysis. Next, the results remained stationary after PSM and IPTW analysis; subgroup analysis indicated that this conclusion has consistent results among patients with or without DM. DM patients with higher values of glycemic gap have the worst outcomes. Moreover, ROC curve analysis revealed that absolute and relative glycemic gaps can increase the predictive value of GCS in patients with cardiogenic shock. Furthermore, GG was positively associated with TyG, irrespective of DM. Finally, linear regression analysis revealed that a higher level of lactic acid accompanies a higher absolute or relative GG.

The GG has been extensively studied as an indicator of stress-induced hyperglycemia (SIH). Zhang et al. [13] found that GG was independently associated with 30-day all-cause mortality and major cardiovascular adverse events in patients with acute ST-segment elevation myocardial infarction, which was not affected by DM status. Afshin et al. [22] demonstrated that admission GG is associated with the risk of in-hospital mortality and can potentially represent a valuable prognostic biomarker for ICH patients with DM. Our study shows that absolute glycemic gap and relative glycemic gap are independent adverse prognostic factors for CS patients, whether with DM or not. Hence, the GG may be a better indicator of SIH than glucose in CS patients with DM, which was consistent with previous studies. However, Guo et al. revealed the presence of a U-shaped relationship between the GG and mortality in critically ill patients (Number of study population = 935) [23]. Jensen, Andreas Vestergaard, et al. found that the highest and lowest GG may increase the risk of 90-day mortality in patients with community-acquired pneumonia (Number of study population = 1933) [14]. This finding suggests that the relationship between GG and outcomes in patients with CS may also be non-linear. Our statistical analysis supported a linear relationship between GG and outcomes in patients with CS. This finding may be due to the small sample size of the MIMIC cohort in our study (Number of study population = 359/357). However, it is difficult for us to expand our sample size in the MIMIC-IV database. Future research should determine whether there is a non-linear relationship by expanding the sample size.

The predominant cause of SIH is the intense counterregulatory hormone and cytokine responses of critical illness [24]. In particular, sympathetic nervous system activation [25] and cytokines such as tumor necrosis factor-α (TNFα) [26] stimulate glucagon release, together with other anti-insulin hormones, including cortisol and growth hormone, leading to hyperglycemia. Furthermore, a reduction of pancreatic β-cell function and growth of insulin resistance both contribute to SIH in non-DM [27]. Additionally, SIH makes a vicious cycle by inducing an increase in oxidative stress [28], inflammation [29], endothelial cell dysfunction [30], and amplifying gluconeogenesis and insulin resistance [31]. The TyG index has been proposed as a reliable alternative marker of insulin resistance, which is superior to HOMA-IR in assessing insulin resistance in individuals with and without DM [32]. In our study, GG was positively related to the TyG index, an evaluating indicator for insulin resistance. Consequently, GG may be partly mediated by the insulin resistance conditions.Zhang et al. found that a high level of stress hyperglycemia ratio without DM has worse outcomes in CICU patients [33]. However, we found that DM patients with higher values of GG have the worst outcomes. How does one explain the discrepancy? Our findings show that the group with a high-level glycemic gap and DM had the highest level of TyG (Table 6 and S9). In our study, it is reasonable to assume that DM patients with high GG levels have higher insulin resistance. However, we cannot get the information on insulin resistance from Q. Zhang’s research. Further research is needed to confirm our hypothesis. Besides, the level of platelet and WBC were significantly higher in the high GG group, which can be explained by the conclusion that stress exposure could be a risk factor for abnormal WBC, RBC, and platelets [34, 35].

The GCS was introduced by Teasdale and Jennett [36, 37] in 1974 and 1976 as a means for serial assessment of patients with traumatic brain injury who were admitted to ICU, with the advantage of the convention. Changes in the level of consciousness may also occur in patients with cardiogenic shock. In recent years, a lower GCS score was a significant predictor for in-hospital mortality in CS patients [38]. ROC curve analysis revealed that absolute and relative glycemic gaps can increase the predictive value of GCS in patients with cardiogenic shock. We can combine these two indicators in the future practice of predicting the short-term outcomes of CS.

The first demonstration of lactic acid in human blood in shock was described by Johann Joseph Scherer (1814–1869) in January 1843. Increased lactic acid in the blood (hyperlactatemia) reflects severe illness, in which the increased blood lactate levels may result from both anaerobic and aerobic production or a decreased clearance [39]. Our study shows that a higher level of lactic acid often accompanies a higher absolute or relative GG level. There are several explanations. Firstly, stressful stimuli will likely increase lactate production from glucose in muscles [40]. A prospective observational study also indicated that hyperglycemia and increased glucose turnover substantially influence lactate metabolism in severe septic and CS [41]. Moreover, Weekers et al. reported that lactic acidosis lasted longer in the hyperglycemic group as compared with a normoglycemic group in an animal model of severe injury [42], which may result from a deterioration of cardiac output [43], mitochondrial function [44], or microcirculation [45]. Richards et al.noted that the relationship between hyperglycemia and multiple organ failure in patients with blunt trauma was mediated by lactate level [46]. A similar correlation has been seen in other settings, such as critically ill patients [47] and sepsis [48]. Hyperlactatemia is an independent indicator of outcomes in CS patients [49]. These findings suggest that GG reflects the severity of CS, and hyperlactatemia may underlie the relationship between SIH and poor outcomes in CS patients.

Unfortunately, fixed guidelines to manage SIH have yet to be studied in detail. The SIH needs more in-depth studies to construct a more organized and direct approach to diagnosing and managing such a condition. Although there are no guidelines to manage SIH yet, a study showed that the likelihood for favorable outcome was lowest among patients with undiagnosed DM compared to acute ischemic stroke patients with true non-DM in (adjusted relative risk, 0.42 [99% CI, 0.19–0.94]) [50]. Our study shows that DM CS patients with higher values of GG have the worst outcomes (Fig. 2b and S3b). HBA1c is not routinely tested in critical patients. Consequently, it is necessary to detect HbA1c for CS patients. It can not only help diagnose DM but also be used to calculate the glycemic gap.

Strengths and limitations

The present study has several strengths. To our knowledge, we are the first study to evaluate the predictive value of the GG in patients with CS, with subgroup analysis according to the status of DM. Additionally, we used three methods (Cox proportional-hazards analysis, PSM, and IPTW) to adjust for confounding factors, which made our conclusions more credible. However, our study has some limitations. One is that the GG in the study was calculated from random blood glucose. It took a lot of work for us to eliminate the confounding effect of food in observational studies, which may affect the accuracy of the results. Consequently, future research should use fasting glucose rather than admission random glucose. Furthermore, this was an observational retrospective study. Although we have conducted PSM and IPTW, residual confounding is still possible. In addition, a reduction in the sample size after PSM analysis may reduce the study’s statistical power and lead to instability in the results. Ethnic disparities between HbA1c and mean glucose levels have been shown in several studies [51]. We found that the outcomes of GG differ across races. We’ve only done preliminary validation in Chinese populations. Further research on diverse populations should be conducted in the future. The fluctuation of blood glucose can change the GG in the short term. It is one-sided to evaluate the impact of the GG on outcomes in CS patients using a single value. And whether hypoglycemic agents will affect the relationship between GG and outcomes by controlling the blood glucose still remains unknown. Consequently, dynamic blood glucose and HbA1c monitoring and studies of hypoglycemic agents influence are also necessary in the future. Based on monitoring the GG, we can study the influence of the change of GG on the outcomes to guide the treatment. In addition, we only investigate the impact on short-term outcomes, whether it affects long-term outcomes remains unknown. Our results need to be verified by prospective studies, and more research should be done.

Conclusions

Among patients with cardiogenic shock, absolute glycemic gap and relative glycemic gap, whether analyzed as a continuous variable or a categorical variable, were both associated with increased 30-day all-cause mortality, regardless of DM. The relationship was stable after multivariate Cox regression analysis, PSM, and IPTW analysis. They reflect the severity of CS to some extent. Hyperlactatemia and insulin resistance may underlie the relationship between SIH and poor outcomes in CS patients. Additionally, they can both improve the prognostic efficacy of GCS. It can aid in risk stratification and shape future therapeutic strategies in CS patients.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SIH:

stress-induced hyperglycemia

CS:

cardiogenic shock

GG:

glycemic gap

PSM:

propensity score-matched

IPTW:

inverse probability treatment weighting

TyG:

triglyceride-glucose index

DM:

diabetes mellitus

ADAG:

A1c-derived average glucose

MIMIC-IV v2.0:

Medical Information Mart for Intensive Care-IV database version 2.0

Fey:

The Second Affiliated Hospital of Wenzhou Medical University

ICU:

intensive care unit

CITI:

Collaborative Institutional Training Initiative

ICD:

International Classification of Diseases

SD:

standard deviation

GCS:

Glasgow coma score

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

MAP:

mean arterial pressure

SpO2 :

pulse oximetry-derived oxygen saturation

CKD:

chronic kidney disease

WBC:

white blood cell

INR:

international normalized ratio

PT:

prothrombin time

APTT:

activated partial thromboplastin time

RRT:

renal replacement treatment

EF:

ejection fraction

NT-proBNP:

N-terminal pro-brain natriuretic peptide

cTn-I:

cardiac troponin I

IABP:

intra-aortic balloon pump

MV:

mechanical ventilation

HR:

hazard ratios

CI:

confidence interval

ALT:

glutamic-pyruvic transaminase

AST:

glutamic oxaloacetic transaminase

CRRT:

continuous renal replacement therapy

COPD:

chronic obstructive pulmonary disease

MACEs:

major cardiovascular adverse events

STEMI:

acute ST-segment elevation myocardial infarction

TNF-α:

tumor necrosis factor-α

References

  1. Reynolds H, Hochman J. Cardiogenic shock: current concepts and improving outcomes. Circulation. 2008;117(5):686–97.

    Article  PubMed  Google Scholar 

  2. Thiele H, Zeymer U, Neumann F, Ferenc M, Olbrich H, Hausleiter J, et al. Intraaortic balloon support for myocardial infarction with cardiogenic shock. N Engl J Med. 2012;367(14):1287–96.

    Article  CAS  PubMed  Google Scholar 

  3. Helgestad O, Josiassen J, Hassager C, Jensen L, Holmvang L, Sørensen A, et al. Temporal trends in incidence and patient characteristics in cardiogenic shock following acute myocardial infarction from 2010 to 2017: a Danish cohort study. Eur J Heart Fail. 2019;21(11):1370–8.

    Article  PubMed  Google Scholar 

  4. Ye X, Cai X, Nie D, Chen Y, Li J, Xu X, et al. Stress-Induced Hyperglycemia and Remote Diffusion-Weighted Imaging Lesions in Primary Intracerebral Hemorrhage. Neurocrit Care. 2020;32(2):427–36.

    Article  CAS  PubMed  Google Scholar 

  5. Vanhorebeek I, Langouche L, Van den Berghe G. Tight blood glucose control with insulin in the ICU: facts and controversies. Chest. 2007;132(1):268–78.

    Article  CAS  PubMed  Google Scholar 

  6. Di S, Xintian C, Qing Z, Mulalibieke H, Junli H, Shuaiwei S, et al. Increased stress hyperglycemia ratio at hospital admission in stroke patients are associated with increased in-hospital mortality and length of stay. Diabetol Metab Syndr. 2024;16(1):69.

    Article  Google Scholar 

  7. Abdin A, Pöss J, Fuernau G, Ouarrak T, Desch S, Eitel I, et al. Prognostic impact of baseline glucose levels in acute myocardial infarction complicated by cardiogenic shock—a substudy of the IABP-SHOCK II-trial [corrected]. Clinical research in cardiology : official journal of the German Cardiac Society. 2018;107(6):517–23.

    Article  CAS  PubMed  Google Scholar 

  8. Thoegersen M, Josiassen J, Helgestad O, Berg Ravn H, Schmidt H, Holmvang L, et al. The association of diabetes and admission blood glucose with 30-day mortality in patients with acute myocardial infarction complicated by cardiogenic shock. Eur Heart J Acute Cardiovasc Care. 2020;9(6):626–35.

    Article  PubMed  Google Scholar 

  9. Choi S, Yoon G, Lee M, Park S, Ko Y, Ahn C, et al. Prognostic Impact of Plasma Glucose on Patients With Cardiogenic Shock With or Without Diabetes Mellitus from the SMART RESCUE Trial. Am J Cardiol. 2022;175:145–51.

    Article  CAS  PubMed  Google Scholar 

  10. Yang J, Song P, Song Y, Hahn J, Choi S, Choi J, et al. Prognostic value of admission blood glucose level in patients with and without diabetes mellitus who sustain ST segment elevation myocardial infarction complicated by cardiogenic shock. Critical care (London, England). 2013;17(5):R218.

    Article  PubMed  Google Scholar 

  11. Nathan D, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine R. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sun P, Poblete R, Nguyen P, Bulic S, Kim-Tenser M, Marehbian J, et al. Association Between Glycemic Gap and In-hospital Outcomes in Aneurysmal Subarachnoid Hemorrhage. Front Neurol. 2021;12: 714341.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wu S, Yang Y, Zhu J, Xu W, Wang L, Lyu S, et al. Impact of glycemic gap on 30-day adverse outcomes in patients with acute ST-segment elevation myocardial infarction. Atherosclerosis. 2022;360:34–41.

    Article  CAS  PubMed  Google Scholar 

  14. Jensen A, Baunbæk Egelund G, Bang Andersen S, Petersen P, Benfield T, Witzenrath M, et al. The Glycemic Gap and 90-Day Mortality in Community-acquired Pneumonia. A Prospective Cohort Study. Ann Am Thoracic Soc. 2019;16(12):1518–26.

    Article  Google Scholar 

  15. Wei-Chieh L, Chih-Yuan F, Huang-Chung C, Chien-Jen C, Cheng-Hsu Y, Chi-Ling H, et al. Associations with 30-day survival following extracorporeal membrane oxygenation in patients with acute ST segment elevation myocardial infarction and profound cardiogenic shock. Heart Lung. 2016;45(6):532–7.

    Article  Google Scholar 

  16. Fernando Luís S, Daniel S, Débora Vacaro F, Miguel G, Marciane Maria R, Marcely Gimenes B, et al. Association between serum lactate levels and mortality in patients with cardiogenic shock receiving mechanical circulatory support: a multicenter retrospective cohort study. BMC Cardiovasc Disord. 2020;20(1):496.

    Article  Google Scholar 

  17. Brittany K, Steven M M. Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward. JMIR Med Inform. 2022;10(8):e37756.

  18. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov P, Mark R, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20.

    Article  CAS  PubMed  Google Scholar 

  19. Jaddoe V, de Jonge L, Hofman A, Franco O, Steegers E, Gaillard R. First trimester fetal growth restriction and cardiovascular risk factors in school age children: population based cohort study. BMJ (Clinical research ed). 2014;348:g14.

    PubMed  PubMed Central  Google Scholar 

  20. Zhang Z. Propensity score method: a non-parametric technique to reduce model dependence. Annals of translational medicine. 2017;5(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Grafféo N, Latouche A, Le Tourneau C, Chevret S. ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials. Comput Biol Med. 2019;111:103339.

    Article  PubMed  Google Scholar 

  22. Zarean E, Lattanzi S, Looha M, Napoli M, Chou S, Jafarli A, et al. Glycemic Gap Predicts in-Hospital Mortality in Diabetic Patients with Intracerebral Hemorrhage. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association. 2021;30(5):105669.

    Article  PubMed  Google Scholar 

  23. Guo J, Chou R, Kuo C, Chao T, Wu C, Tsai Y, et al. The paradox of the glycemic gap: Does relative hypoglycemia exist in critically ill patients? Clinical nutrition (Edinburgh, Scotland). 2021;40(7):4654–61.

    Article  CAS  PubMed  Google Scholar 

  24. McCowen K, Malhotra A, Bistrian B. Stress-induced hyperglycemia. Crit Care Clin. 2001;17(1):107–24.

    Article  CAS  PubMed  Google Scholar 

  25. Dünser M, Hasibeder W. Sympathetic overstimulation during critical illness: adverse effects of adrenergic stress. J Intensive Care Med. 2009;24(5):293–316.

    Article  PubMed  Google Scholar 

  26. Blumberg D, Hochwald S, Burt M, Donner D, Brennan M. Tumor necrosis factor alpha stimulates gluconeogenesis from alanine in vivo. J Surg Oncol. 1995;59(4):220–4 discussion 4–5.

    Article  CAS  PubMed  Google Scholar 

  27. Kruyt N, van Westerloo D, DeVries J. Stress-induced hyperglycemia in healthy bungee jumpers without diabetes due to decreased pancreatic β-cell function and increased insulin resistance. Diabetes Technol Ther. 2012;14(4):311–4.

    Article  CAS  PubMed  Google Scholar 

  28. Papachristoforou E, Lambadiari V, Maratou E, Makrilakis K. Association of Glycemic Indices (Hyperglycemia, Glucose Variability, and Hypoglycemia) with Oxidative Stress and Diabetic Complications. J Diabetes Res. 2020;2020:7489795.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ray K, Cannon C, Morrow D, Kirtane A, Buros J, Rifai N, et al. Synergistic relationship between hyperglycaemia and inflammation with respect to clinical outcomes in non-ST-elevation acute coronary syndromes: analyses from OPUS-TIMI 16 and TACTICS-TIMI 18. Eur Heart J. 2007;28(7):806–13.

    Article  CAS  PubMed  Google Scholar 

  30. Qiu C, Sui J, Zhang Q, Wei P, Wang P, Fu Q. Relationship of Endothelial Cell-Specific Molecule 1 Level in Stress Hyperglycemia Patients With Acute ST-Segment Elevation Myocardial Infarction: A Pilot Study. Angiology. 2016;67(9):829–34.

    Article  CAS  PubMed  Google Scholar 

  31. Barth E, Albuszies G, Baumgart K, Matejovic M, Wachter U, Vogt J, et al. Glucose metabolism and catecholamines. Crit Care Med. 2007;35:S508–18.

    Article  CAS  PubMed  Google Scholar 

  32. Simental-Mendía L, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.

    Article  PubMed  Google Scholar 

  33. Qianqian Z, Huaqiang X, Qianqian X, Zesong H, Yangjing X, Jie W, et al. Stress hyperglycemia ratio linked to worse prognosis in Cardiac Intensive Care Unit patients: A retrospective observational study. Diabetes Res Clin Pract. 2024;209(0):111598.

  34. Gharibi V, Khanjani N, Heidari H, Ebrahimi M, Hosseinabadi M. The effect of heat stress on hematological parameters and oxidative stress among bakery workers. Toxicol Ind Health. 2020;36(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  35. Kario K, McEwen B, Pickering T. Disasters and the heart: a review of the effects of earthquake-induced stress on cardiovascular disease. Hypertension research : official journal of the Japanese Society of Hypertension. 2003;26(5):355–67.

    Article  PubMed  Google Scholar 

  36. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet (London, England). 1974;2(7872):81–4.

    Article  CAS  PubMed  Google Scholar 

  37. Teasdale G, Jennett B. Assessment and prognosis of coma after head injury. Acta Neurochir. 1976;34:45–55.

    Article  CAS  PubMed  Google Scholar 

  38. Kim D, Na S, Cho Y, Chung C, Jeon K, Suh G, et al. Predictors of Survival to Discharge After Successful Weaning From Venoarterial Extracorporeal Membrane Oxygenation in Patients With Cardiogenic Shock. Circ J. 2020;84(12):2205–11.

    Article  CAS  PubMed  Google Scholar 

  39. Kompanje E, Jansen T, van der Hoven B, Bakker J. The first demonstration of lactic acid in human blood in shock by Johann Joseph Scherer (1814–1869) in January 1843. Intensive Care Med. 2007;33(11):1967–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tayek J, Katz J. Glucose production, recycling, Cori cycle, and gluconeogenesis in humans: relationship to serum cortisol. Am J Physiol. 1997;272:E476–84.

    CAS  PubMed  Google Scholar 

  41. Revelly J, Tappy L, Martinez A, Bollmann M, Cayeux M, Berger M, et al. Lactate and glucose metabolism in severe sepsis and cardiogenic shock. Crit Care Med. 2005;33(10):2235–40.

    Article  CAS  PubMed  Google Scholar 

  42. Weekers F, Giulietti A, Michalaki M, Coopmans W, Van Herck E, Mathieu C, et al. Metabolic, endocrine, and immune effects of stress hyperglycemia in a rabbit model of prolonged critical illness. Endocrinology. 2003;144(12):5329–38.

    Article  CAS  PubMed  Google Scholar 

  43. Sodi-Pallares D, Ponce de León J, Bisteni A, Medrano G. Potassium, glucose, and insulin in myocardial infarction. Lancet (London, England). 1969;1(7609):1315–6.

    Article  CAS  PubMed  Google Scholar 

  44. Nishikawa T, Edelstein D, Du X, Yamagishi S, Matsumura T, Kaneda Y, et al. Normalizing mitochondrial superoxide production blocks three pathways of hyperglycaemic damage. Nature. 2000;404(6779):787–90.

    Article  CAS  PubMed  Google Scholar 

  45. Guerci B, Böhme P, Kearney-Schwartz A, Zannad F, Drouin P. Endothelial dysfunction and type 2 diabetes. Part 2: altered endothelial function and the effects of treatments in type 2 diabetes mellitus. Diabet metab. 2001;27:436–47.

    CAS  Google Scholar 

  46. Richards J, Scalea T, Mazzeffi M, Rock P, Galvagno S. Does Lactate Affect the Association of Early Hyperglycemia and Multiple Organ Failure in Severely Injured Blunt Trauma Patients? Anesth Analg. 2018;126(3):904–10.

    Article  CAS  PubMed  Google Scholar 

  47. Kaukonen K, Bailey M, Egi M, Orford N, Glassford N, Marik P, et al. Stress hyperlactatemia modifies the relationship between stress hyperglycemia and outcome: a retrospective observational study. Crit Care Med. 2014;42(6):1379–85.

    Article  CAS  PubMed  Google Scholar 

  48. Green J, Berger T, Garg N, Horeczko T, Suarez A, Radeos M, et al. Hyperlactatemia affects the association of hyperglycemia with mortality in nondiabetic adults with sepsis. Acad Emerg Med Off J Soc Acad Emerg Med. 2012;19(11):1268–75.

    Article  Google Scholar 

  49. Valente S, Lazzeri C, Vecchio S, Giglioli C, Margheri M, Bernardo P, et al. Predictors of in-hospital mortality after percutaneous coronary intervention for cardiogenic shock. Int J Cardiol. 2007;114(2):176–82.

    Article  PubMed  Google Scholar 

  50. Torbey M, Pauls Q, Gentile N, Falciglia M, Meurer W, Pettigrew C, et al. Intensive Versus Standard Treatment of Hyperglycemia in Acute Ischemic Stroke Patient: A Randomized Clinical Trial Subgroups Analysis. Stroke. 2022;53(5):1510–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. William HH, Yong M, Gabriel U, Steven H, Steven EK, Edward SH, et al. Differences in A1C by race and ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program. Diabetes Care. 2007;30(10):2453–7.

    Article  Google Scholar 

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Acknowledgements

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Funding

This work was supported by the Scientific Research Foundation of the Science and Technology Department of Wenzhou City under grant (No.2022Y0566) and the Clinical Research Foundation of the 2nd Affiliated Hospital of Wenzhou Medical University under grant (SAHoWMU-CR2019-01–117).

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  1. Qianqian Xu and Jinsheng Wang contributed equally to this work.

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  1. Department of Cardiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Xueyuanxi Road, No 109, Wenzhou , Zhejiang, 325027, China

    Qianqian Xu, Jinsheng Wang, Zhihui Lin, Dongyan Song, Kangting Ji & Huaqiang Xiang

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Contributions

Conceptualization, QX, JW, KJ and HX; Data curation, ZL and DS; Formal analysis, QX and JW; Funding acquisition, KJ and HX; Investigation, QX and JW; Methodology, QX and JW; Project administration, KJ and HX; Resources, ZL and DS; Software, ZL and DS; Supervision, KJ and HX; Validation, QX and JW; Visualization, ZL and DS; Writing–original draft, QX and JW; Writing–review & editing, QX and HX. All authors read and approved the final manuscript.

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Correspondence to Kangting Ji or Huaqiang Xiang.

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The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Research and Ethics Institute of the Second Affiliated Hospital of Wenzhou Medical University (protocol code 2023-K-136–01). Our research used data obtained in previous clinical trials. And we didn’t use data that the patient explicitly refused to utilize. Besides, the data was anonymous. Consequently, we applied for exemption of informed consent to the Institutional Research and Ethics Institute of the Second Affiliated Hospital of Wenzhou Medical University. And the organization approved our application. The ethics committee approval letter of this study was attached below. And you can see the items reviewed included a informed consent waiver application form.

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Xu, Q., Wang, J., Lin, Z. et al. The glycemic gap as a prognostic indicator in cardiogenic shock: a retrospective cohort study. BMC Cardiovasc Disord 24, 468 (2024). https://doi.org/10.1186/s12872-024-04138-w

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BMC Cardiovascular Disorders

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