https://orcid.org/0009-0007-8609-1618
Abstract
Lactate and glucose are widely used biochemical parameters in current predictive risk scores for cardiogenic shock. Data regarding the relationship between lactate and glucose levels in cardiogenic shock are limited. Thus, we aimed to analyze glucose and lactate as early markers for in-hospital mortality in cardiogenic shock. In this retrospective cohort study, 312 patients presenting with cardiogenic shock to a tertiary-care hospital between 2016 and 2018 were included. Apparent cardiogenic shock was defined as hypoperfusion with hemodynamic compromise and biochemical marker increase due to diminished tissue perfusion, corresponding to SCAI shock stages. In-hospital mortality was assessed as the primary endpoint. The median age of the study population was 71 (60–79) years and the etiology of cardiogenic shock was acute myocardial infarction in 45.8%. Overall in-hospital mortality was 67.6%. In the receiver operating curve analysis, the area under the receiver-operating curve (AUC) for prediction of in-hospital mortality was higher for lactate (AUC: 0.757) than for glucose (AUC: 0.652). Both values were significantly associated with outcome (groups created with best cutoff values obtained from the Youden index). Correlation analysis showed a significant non-linear association of both values. In a multivariable stepwise Cox regression analysis, lactate remained an independent predictor for in-hospital mortality, whilst glucose, despite being implicated in energy metabolism, was not independently predictive for mortality. Together, these data suggest that lactate at admission is superior for mortality prediction in patients with apparent cardiogenic shock. Glucose was not independently predictive for mortality.
Figures
Citation: Billig H, Al Zaidi M, Quacken F, Görtzen-Patin J, Goody PR, Gräff I, et al. (2024) Blood glucose and lactate levels as early predictive markers in patients presenting with cardiogenic shock: A retrospective cohort study. PLoS ONE 19(7): e0306107. https://doi.org/10.1371/journal.pone.0306107
Editor: Gaetano Santulli, Albert Einstein College of Medicine, UNITED STATES
Received: January 24, 2024; Accepted: June 11, 2024; Published: July 25, 2024
Copyright: © 2024 Billig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Due to the sensitive nature of the medical data involved in this study and the privacy agreements under which the data was collected, the full dataset for this retrospective analysis is not publicly available. The data contains confidential patient information, and its public disclosure is restricted by ethical and legal regulations to protect patient privacy. However, the dataset can be made available upon reasonable request. Requests for access to the data will be reviewed on a case-by-case basis to ensure compliance with applicable privacy laws and institutional policies. Potential recipients will be required to sign a data access agreement that mandates the confidential handling of the data. Data requests may be sent to the Research Data Service Center of the University Bonn (researchdata@uni-bonn.de). The institutional repository guarantees data preservation for at least 10 years post-publication, ensuring long-term accessibility.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cardiogenic Shock (CS) is a critical syndrome of systemic hypoperfusion and tissue hypoxia associated with high mortality [1]. In-hospital mortality rates of 40–62% have been reported [2], and therefore, early identification of the underlying cause of CS is crucial. In acute myocardial infarction (AMI) complicated by CS, early revascularization of the culprit lesion can improve survival [3, 4]. As for pharmacological support, vasopressors are frequently needed to maintain tissue perfusion in hemodynamically unstable patients [5]. Despite a lack in high-quality evidence, mechanical circulatory support has been increasingly implemented to improve cardiac output and support end-organ perfusion. High complication rates demand careful patient selection and multidisciplinary expertise [6].
Early assessment of outcome is needed for therapeutic decision making. Several scores for prediction of early mortality in CS patients have been introduced [7–10]. Within these scores, glucose and lactate have been increasingly recognized as valuable predictive markers in CS. Elevated lactate levels are a hallmark of poor tissue perfusion and metabolic distress, and have been shown to be associated with increased morbidity and mortality in patients with cardiac disease.
In a physiological state, glucose is the primary source of energy for most cells and is generally metabolized through aerobic glycolysis and the citric acid cycle [11, 12].
Contrary, in hypoxic phases (e.g. tissue hypoperfusion during CS), glucose can also be metabolized in the absence of oxygen. This anaerobic glycolysis is less efficient, however, it allows cells to generate energy quickly in the absence of oxygen [11]. In CS, hyperlactatemia is most likely caused by an increase in lactate production, instead of an impaired lactate clearance [13, 14].
In recent years, several studies have demonstrated the utility of glucose and lactate as predictive markers in CS, and especially lactate is widely used to guide the management and estimate prognosis of these critically ill patients. Lactate as well as its clearance in a time span of 6–8 hours have been shown to be valuable prognostic markers to predict early prognosis in CS [9, 15–18]. The role of lactate has been extensively studied across diverse patient populations with varying etiologies, including those on mechanical support [17, 19, 20] and those who have experienced cardiac arrest [21–23].
Notably, the majority of research on this topic has been confined to patients experiencing AMI complicated by CS or have excluded patients after cardiac arrest [24–29]. However, a limited number of studies analyzed glucose in a real-world-setting of CS, encompassing various etiologies and severity levels, from beginning CS characterized by hypotension and signs of organ hypoperfusion to severe cases requiring resuscitation [30, 31].
Data comparing usefulness and relationship of both lactate and glucose are limited. The study of Kataja et al. examines the role of admission blood glucose levels as a predictive marker for in-hospital mortality in patients with CS due to AMI or other etiologies and with or without cardiac arrest. The study does mention correlations between glucose levels and other clinical markers, including lactate. Severe hypo- and hyperglycemia is associated with higher lactate levels. However, it does not perform a direct comparative analysis of their usefulness or a detailed exploration of their interrelationship beyond correlational observations [30].
We hypothesized that glucose could serve as an earlier or a complementary marker to lactate for in-hospital mortality in CS.
Methods
Study design and population
We conducted a retrospective, single-center analysis at our tertiary academic center. Patients with cardiogenic shock treated from 2016 to 2018 were included. For this purpose, all patients with ICD-10 code R57.0 were screened for the presence of classic cardiogenic shock.
Inclusion criteria were hemodynamic relevance (defined as hypotension < 90 mmHg or support by catecholamines) and biochemical detectability (defined as lactatemia > 2 mmol/l) of shock. By that, we identified patients exhibiting clinical signs of cardiogenic shock, which correspond to stages B through E of the SCAI shock classification.
Exclusion criteria were age younger than 18 years or treatment in a different department because of the variety of documentation systems at our site.
Because glucose and lactate were aimed to be studied as predictors of short-term outcome, in-hospital mortality was chosen as the primary end point. Patients without baseline glucose and lactate measurements were excluded.
This retrospective study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Human Investigation Committee (IRB) of University of Bonn confirmed that consultation of the committee is not required for our retrospective evaluation of data obtained in the course of routine diagnostics / care (Institutional Review Board approval number: 243/22, approval date: June 14, 2022). As data were analyzed retrospectively and data were analyzed anonymously, no consent has been obtained from the patients.
Data collection
Demographic and clinical data, past medical history, and laboratory values were obtained from the electronic medical record system from July-November 2022. F.Q., H.B. and A.A. had access to individual participant information during data collection. After data collection, information was anonymized.
Statistical analysis
SPSS software version 29 and R 4.4.0. were used for the statistical analyzes.
Because most metric variables were not normally distributed, statistics for all metric variables are shown as median with interquartile range (IQR). Baseline characteristics were tested using the Mann-Whitney U test for metric variables and the chi-square test for categorical variables.
Receiver-operating curves (ROCs) with the corresponding area under the curve (AUC) were calculated for the first glucose and lactate value after hospital admission. The best cut-off values for predicting in-hospital mortality were determined using the Youden index. An association of the groups with the primary end point was examined by Kaplan-Meier-curves and the log-rank test.
Relationship of lactate and glucose levels were tested with the Spearman’s rank correlation coefficient. Strength, direction and form of the correlation was visualized using scatter plots.
Cox regression analysis was used to identify predictors of the primary end point. For this, baseline variables showing an association with in-hospital mortality (age, prior myocardial infarction, arterial hypertension, haemoglobin, platelets, pH, glucose, and lactate) were included using a stepwise-forward entry approach with an entry level of <0.05.
Power analysis
A post-hoc power analysis was conducted to determine the study’s ability to detect differences in lactate and glucose levels between survivors and non-survivors of cardiogenic shock. The analysis utilized a sample size of 312 patients, with effect sizes of Cohen’s d = 1.015 for lactate and d = 0.532 for glucose. The significance level was set at 0.05 with a two-sided hypothesis test. The results indicated that the study was sufficiently powered, achieving near-complete power (100% for lactate and 99.99% for glucose) to reliably detect significant differences, ensuring the robustness of the findings.
Results
Demographic and clinical characteristics of study participants (overall, and stratified by survival) are summarized in Table 1. The analysis of baseline characteristics was exploratory, intended to identify potential patterns or differences that may warrant further investigation. Overall, the median age of patients was 71 (60–79) years and 76.6% were male. Within the cohort, there was a high prevalence of cardiovascular risk factors (33.3% with diabetes, 63.2% with arterial hypertension, 34.6% were active or former smokers) and ischemic heart disease (27.6%).
The primary endpoint (in-hospital mortality) occurred in 211 (67.6%) patients. In 143 (45.8%) patients, the etiology of cardiogenic shock was acute myocardial infarction (AMI). For patients with AMI, survival rates were significantly higher (57.4%) than in non-AMI-etiologies (40.3%). 207 (66.6%) patients experienced initial cardiac arrest (during prehospital care or in the emergency department). Mechanical circulatory support (VA-ECMO, Impella or intra-aortic balloon pump) was applied in 67 (21.5%) patients.
Mean left ventricular ejection fraction (LVEF) was 40% (25–40%) and did not differ significantly between survivors and non-survivors.
Initial mean arterial pressure was higher in survivors than in non-survivors (82 [71–90] vs. 73 [62–90] mmHg, p 0.026) whilst heart rate was lower (80 [68–94] vs. 88 [75–111] BPM, p 0.014; Table 1).
Baseline values for haemoglobin (Hb) and pH were significantly lower for non-survivors than for survivors (Hb: 11.6 [IQR 9.8–13.2] vs. 12.9 [IQR 11.5–14.8] g/dl, p < 0.001; pH: 7.134 [IQR 6.875–7.343] vs. 7.343 [7.212–7.402], p < 0.001; Table 1). Glucose and lactate differed significantly between non-survivors and survivors (glucose: 262 [IQR 135–367] vs. 191 [IQR 135–268] mg/dl, p < 0.001, lactate: 9.25 [IQR 4.51–12.89] vs. 3.41 [IQR 2.01–6.9] mmol/l, p < 0.001; Table 1, Fig 1A and 1B).
A) Boxplot for glucose at admission, stratified for in hospital mortality. B) Boxplot for lactate at admission, stratified for in hospital mortality. C) scatter plot with LOESS smoothing line for glucose and lactate values at admission.
To test the relationship between glucose and lactate values in survivors and non-survivors, we performed correlation analyses. The results indicated a significant non-linear relationship between lactate and glucose values, as visualized by a scatter plot with a LOESS smoothing line (Fig 1C). This non-linear relationship was confirmed by a Generalized Additive Model, which demonstrated that the smooth term for glucose was highly significant (p < 0.001), indicating a complex, non-linear interaction between these biomarkers.
Univariate analysis of in-hospital mortality
To compare mortality prediction by baseline glucose and lactate values, receiver operating curves (ROC) were generated (Fig 2D). The area under the ROC for glucose was 0.652 (95% CI 0.588–0.715), while it was 0.757 for lactate (95% CI 0.702–0.812). The DeLong test revealed a significant difference between these AUCs (p < 0.001, 95% CI for the difference in AUC: -0.161 to -0.049).
Kaplan-Meier curves for time from admission to death stratified for calculated best cut-off values. A) for glucose at admission. B) for lactate at admission. C) Kaplan-Meier curves for time to death stratified for a score of consisting of glucose and lactate best cutoff values. D) ROC Curves in Prediction of In-Hospital-Mortality for baseline lactate and baseline glucose.
The Youden index was used to obtain best cutoff values for predicting in-hospital mortality. For glucose, the optimal cutoff value was 234 mg/dl (sensitivity 0.573, specificity 0.693) while it was 7.99 mmol/l for lactate (sensitivity 0.607; specificity 0.842).
When comparing in-hospital mortality according to the obtained cutoff values, both glucose and lactate were found to be significant predictors for in-hospital mortality (Kaplan-Meier curves, Fig 2A and 2B).
To test if a combination of glucose and lactate values can further improve mortality prediction, we created a score out of both parameters: glucose > 234 mg/dl (high) and lactate > 7.99 mmol/l (high) = 2 points; glucose or lactate high = 1 point; both glucose and lactate < 234 mg/dl or 7.99 mmol/l respectively = 0 points. The score was highly significant in predicting in-hospital mortality for the 3 groups, but it did not outperform outcome prediction obtained by baseline lactate alone (Fig 2C).
A 3-dimensional (3D) plot illustrates the relationship between glucose and lactate levels at admission, and the predicted probability of mortality for patients (Fig 3). The 3D plot suggests that while high glucose levels alone (without elevated lactate) do not significantly increase mortality risk, high lactate levels alone (even without high glucose) do increase mortality risk. This indicates the dominant role of lactate as a prognostic marker in predicting mortality among the patient our study population.
Prediction plot as a function of lactate (x-axis) and glucose (y-axis), with the mortality probability on the z-axis.
We also performed decision curve analysis to evaluate the clinical usefulness of lactate and glucose cut-offs in predicting survival outcomes. The results indicated that the lactate cut-off provided a greater net benefit across a range of threshold probabilities compared to glucose. These findings are detailed in the supplementary material (S1 Fig).
Cox regression analysis
In a multivariable, stepwise Cox regression analysis, age (HR 1.039; 95% CI 1.018–1.06) and lactate (HR 1.829; 95% CI 1.005–3.33) were independently associated with the primary endpoint (Table 2). In the Cox regression analysis, glucose was not significantly predictive for in-hospital mortality (HR 1.19; 95% CI 0.679–2.083; p-value 0.544). Cardiac arrest at presentation, CS due to myocardial infarction, diabetes, baseline hemoglobin and pH were not significantly predictive in the Cox regression analysis.
To further examine a possible non-linear relationship between glucose and lactate with survival outcomes, we performed a Restricted Cubic Splines (RCS) model. The RCS model confirmed the significant impact of lactate on survival outcomes, indicating a primarily linear relationship (non-linear p = 0.579). Conversely, glucose levels did not exhibit significant non-linear effects and maintained a largely linear or non-significant relationship with survival outcomes (p-value = 0.5469; non-linear p = 0.443). Detailed results of the RCS analysis are provided in the supplementary data (S3 Table).
Discussion
The major findings of this retrospective study are: first, there is a significant non-linear relationship between glucose and lactate at baseline in patients with cardiogenic shock (CS). Second, despite this pronounced association, only lactate was an independent predictor for in-hospital mortality.
Glucose and lactate have increasingly been recognized as predictive markers for mortality in cardiogenic shock as well as other cardiac and non-cardiac diseases [7, 9, 27, 30, 32, 33]. Two studies investigated the origin of hyperlactatemia in shock: Levraut et al. suggested that in patients with septic shock that have already been weaned from catecholamines, hyperlactatemia resulted from a reduction of lactate clearance [34]. Revelly et al. investigated hyperlactatemia in patients with septic or cardiogenic shock and compared the results to a cohort of healthy subjects. In contrast to Levraut et al., they found that hyperlactatemia was mainly caused by an increase of lactate production, while lactate clearance was not substantially different in shock patients [7]. Beside possible methodological differences between the two studies, clinical discrepancies between both cohorts may explain the differing results. First, lactate metabolism may be essentially different in shock situations from different causes. Second, the application of catecholamines, such as epinephrine, may contribute substantially to lactate production through β2-receptor stimulation and consecutive cAMP-production [14].
In acute illness, hyperglycemia is caused by extensive gluconeogenesis due to hypothalamic-pituitary-adrenal and sympatho-adrenal response [35]. Although the elevation of glucose levels is a part of the body’s physiologic response to stress, extensive hyperglycemia at admission is implicated with a worse prognosis and it has been associated with worse outcomes across various clinical scenarios, including heightened inflammation and subsequent complications, impaired left-ventricular function and greater myocardial damage in patients AMI [36, 37].
Under the assumption that hyperlactatemia is secondary to hyperglycaemia in CS, we hypothesized that glucose might be an earlier or a synergistic marker for outcomes in CS. However, our results showed that only lactate was a significant prognostic parameter in patients with CS, while glucose did not show significant predictive value in the Cox regression model. Correlation analyses revealed a complex non-linear relationship of both parameters.
The predictive value of glucose may differ between patients with and without previously recognized diabetes. Analysis of 141 680 patients with AMI showed that only in patients without diabetes, elevated glucose values were associated with a linear increase of mortality [27]. These findings have been confirmed by Yang et al. who investigated the prognostic value of blood glucose levels in 816 patients with and without diabetes and with CS as a consequence of AMI [26]. Glucose was predictive for 30-day mortality only in patients without known diabetes. The CardShock trial investigated glucose levels in 212 patients presenting with apparent CS of different etiologies from 2010–2012. The cohort experienced cardiac arrest in only 28% and the etiology was acute coronary syndrome in 81%. After separation of the cohort to five distinct groups of hyperglycaemia, glucose showed a negative predictive value for the group with the most severe hyperglycaemia. However, the authors found that diabetes modified the prognostic value of admission blood glucose [30].
For patients with AMI, survival rates in our cohort were significantly higher (57.4%) than in those with non-AMI etiologies (40.3%). The observation that AMI was more prevalent among survivors is intriguing and may be attributed to several key factors. Firstly, patients with AMI often present with more recognizable symptoms, leading to rapid diagnosis and treatment, including timely interventions such as revascularization, which significantly improve survival outcomes. Secondly, the clinical pathways for managing AMI are well-established, offering a clear protocol for immediate treatment. In contrast, other etiologies of cardiogenic shock may lack such well-defined treatment protocols, potentially delaying critical interventions and contributing to lower survival rates.
Patients with impaired glucose metabolism or diabetes are known to have a higher risk for mortality when presenting with AMI alone or AMI complicated by CS [38–40]. As diabetes has a high prevalence in patients with CS, identification of prognostic parameters is crucial for risk evaluation. In contrast to recent studies, we studied a cohort of patients with cardiogenic shock of various etiologies. In line with the results for patients with AMI, we could demonstrate that glucose values are not significantly predictive for short-term-outcomes in our cohort.
Medical history or the origin of CS is infrequently known in patients who are referred to the emergency department. In this acute setting, comprehensively applicable prognostic parameters are of great relevance.
In comparison to most other investigations, our cohort comprised a more severe state of CS with a majority (66.6%) presenting with cardiac arrest and mechanical circulatory support being applied in 21.5%. Only 45.8% experienced CS because of AMI. In this critically-ill cohort, lactate values at admission showed better discriminative characteristics for in-hospital mortality than glucose (AUC, Kaplan-Meier curves). The combination of both, baseline glucose and lactate in a score did not add predictive value in comparison to baseline lactate alone. In addition, after adjusting for diabetes in a multivariate regression analysis, glucose was no independent predictor for in-hospital mortality.
We acknowledge several limitations in our study. Firstly, our reliance on electronic medical records may have resulted in incomplete or inaccurately recorded data, potentially affecting the validity of our findings. Secondly, the observational design limits our ability to establish causal relationships, and unmeasured confounding factors may influence the observed associations. Thirdly, the generalizability of our results is constrained by the single-center setting, which may not reflect broader patient populations or different healthcare environments. Finally, while our patients were classified within SCAI stages B-E, individual SCAI stages were not available. This limitation impacts the granularity of our prognostication.
Conclusion
In this cohort of critically-ill patients with and without known diabetes, lactate at admission was superior for mortality prediction in comparison to glucose. Glucose, despite being implicated in energy metabolism, was not independently predictive for mortality.
Supporting information
S1 Table. Patient characteristics, dissected for AMI and non-AMI.
https://doi.org/10.1371/journal.pone.0306107.s001
(DOCX)
S2 Table. Additional patient characteristics applying to patients who were admitted to intensive care unit (ICU).
https://doi.org/10.1371/journal.pone.0306107.s002
(DOCX)
S1 Fig. Decision curves for lactate and glucose cutoffs.
https://doi.org/10.1371/journal.pone.0306107.s004
(TIF)
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