
==== Front
Cardiol J
Cardiol J
Cardiology Journal
1897-5593
1898-018X
Via Medica

37345365
10.5603/CJ.a2023.0041
cardj-31-4-573
Clinical Cardiology: Original Article
The role of stress hyperglycemia and hyperlactatemia in non-diabetic patients with myocardial infarction treated with percutaneous coronary intervention
Terlecki Michał https://orcid.org/0000-0002-1762-8043

Kocowska-Trytko Maryla https://orcid.org/0000-0002-5633-5825

Pavlinec Christopher
Ostrowska Aleksandra
Lis Paweł
Bednarski Adam
Wojciechowska Wiktoria
Stolarz-Skrzypek Katarzyna
Rajzer Marek https://orcid.org/0000-0001-7074-7263

First Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, Institute of Cardiology, Krakow, Poland
Address for correspondence: Marek Rajzer, MD, PhD, First Department of Cardiology, Interventional Electrocardiology and Arterial Hypertension, Jagiellonian University Medical College, ul. Jakubowskiego 2, 30–688 Kraków, Poland, tel: +48 12 400 21 50, e-mail: marek.rajzer@uj.edu.pl
2024
29 8 2024
31 4 573582
26 1 2023
12 5 2023
Copyright © 2024 Via Medica
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially.
Background

Stress hyperglycemia and lactates have been used separately as markers of a severe clinical condition and poor outcomes in patients with myocardial infarction (MI). However, the interplay between glucose and lactate metabolism in patients with MI have not been sufficiently studied. The aim in the present study was to examine the relationship of glycemia on admission (AG) and lactate levels and their impact on the outcome in non-diabetic MI patients treated with percutaneous coronary intervention (PCI).

Methods

A total of 405 consecutive, non-diabetic, MI patients were enrolled in this retrospective, observational, single-center study. Clinical characteristic including glucose and lactate levels on admission and at 30-day mortality were assessed.

Results

Patients with stress hyperglycemia (AG ≥ 7.8 mmol/L, n = 103) had higher GRACE score (median [interquartile range]: 143.4 [115.4–178.9] vs. 129.4 [105.7–154.5], p = 0.002) than normoglycemic patients (AG level < 7.8 mmol/L, n = 302). A positive correlation of AG with lactate level (R = 0.520, p < 0.001) was observed. The coexistence of both hyperglycemia and hyperlactatemia (lactate level ≥ 2.0 mmol/L) was associated with lower survival rate in the Kaplan-Meier estimates (p < 0.001). In multivariable analysis both hyperglycemia and hyperlactatemia were related to a higher risk of death at 30-day follow-up (hazard ratio [HR] 3.21, 95%, confidence interval [CI] 1.04–9.93; p = 0.043 and HR 7.08; 95% CI 1.44–34.93; p = 0.016, respectively).

Conclusions

There is a relationship between hyperglycemia and hyperlactatemia in non-diabetic MI patients treated with PCI and both markers are independent predictors of 30-day mortality. Moreover the coexistence of hyperglycemia and hyperlactatemia decreases survival much more than each factor separately then should be evaluated simultaneously.

hyperglycemia
lactates
myocardial infarction
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pmcIntroduction

Stress hyperglycemia, defined as a transient elevation of blood-glucose concentrations in the acute phase of the disease, e.g., in sepsis, brain trauma or stroke, is a well-known marker of a poor outcome among critically-ill patients [1–7]. The occurrence and level of stress hyperglycemia reflects the range of a body-stress reaction during the acute phase of a severe disease [8]. The above-mentioned observations were an inspiration to conduct interventional studies on the influence of strict glycemic control and intensive hypoglycemic therapy on the outcome of critically-ill patients. Disappointingly, in many studies including NICE-SUGAR [9], it has not been proven that interventions aimed at lowering glucose levels led to a reduction in the mortality of intensive-care-unit patients. These findings have some clinical implications — stress hyperglycemia cannot be considered as a common parameter which improves a patient’s state when it is well-controlled. It should be considered as a marker of the disease severity and there is likely an additional pathomechanism related to stress hyperglycemia.

There are several studies confirming that stress hyperglycemia is observed in patients with myocardial infarction (MI), and it is proven to be a marker of a worse prognosis in this group of patients [10–14]. Some researchers underline that among patients with MI, hyperglycemia at admission is a marker of a worse outcome irrespective of diabetic status and it may even be more pronounced in patients without diabetes [3, 10, 13, 15].

Hyperlactatemia is another prognostic marker among critically-ill patients [16, 17], with multi-organ trauma [18], sepsis [17, 19] or in patients with acute cardiac conditions including MI [20]. Previous studies have shown that in MI patients, the higher the blood lactate level, the higher the risk of in-hospital death at 30-day follow up [21–23].

There are some pathophysiological premises indicating that abnormal glucose accumulation could be at least in part due to the elevated lactates levels because excess lactates observed in critically-ill patients may be eliminated by oxidation to pyruvate or may be transformed into glucose by gluconeogenesis or into glucagon via the Cori cycle [24]. It is possible that stress hyperglycemia and hyperlactatemia may be dependent on each other as indicators of the same phenomenon. However, there are very few studies evaluating the association of stress hyperglycemia with hyperlactatemia and how early lactate and glucose levels correlate with the outcome in critically-ill patients [25–29]. According to available research, this relationship has not yet been explored in patients with MI. Thus, it was decided to conduct the current study to examine the interplay between lactatemia and stress hyperglycemia in non-diabetic MI patients treated with percutaneous coronary intervention (PCI). It was hypothesized that in critically-ill patients with MI, both parameters coexist and are closely correlated. Moreover, it may be possible that hyperlactatemia may also impact the relationship between stress hyperglycemia and a worse prognosis among patients with MI.

Methods

Medical records of consecutive non-diabetic patients who were admitted to a PCI-capable Cardiology Department at University Hospital, Krakow, Poland were retrospectively analyzed between January 1, 2016, and December 31, 2019, with MI treated with PCI and standard medical therapy according to the European Society of Cardiology (ESC) guidelines [30, 31]. To avoid the influence of diabetes and hypoglycemic treatment on the results obtained, patients with diabetes (with a prior history or de novo) were excluded from this study (Fig. 1). Blood samples were obtained directly on admission in each patient and basic laboratory results including serum lactate level and glucose level were measured.

Myocardial infarction is a heterogeneous disease with various clinical presentations (including both relatively stable but also unstable, life-threatening conditions such as pulmonary edema or cardiogenic shock) and different electrocardiographic manifestations (ST segment elevation MI [STEMI] or non-ST segment elevation MI [NSTEMI]), thus, it was decided to include both STEMI and NSTEMI patients. The diagnosis of MI was based on the criteria set forth by the most current ESC guidelines [30, 31]. Lactate levels and glucose levels were measured using an ABL90 FLEX analyzer (Radiometer, Copenhagen, Denmark) from blood samples obtained directly after admission to hospital (within a few minutes). Stress hyperglycemia was defined as serum glucose levels on admission ≥ 7.8 mmol/L and hyperlactatemia as serum lactate level on admission ≥ 2.0 mmol/L. The same blood sample was used to obtain other biochemical parameters: i.e.: high sensitive troponin I (hsTnI) and creatinine. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) formula [32]. Heart rate, arterial blood pressure, Killip class and Global Registry of Acute Coronary Events (GRACE) risk scores were assessed in all patients on admission [33]. Thrombolysis In Myocardial Infarction (TIMI) coronary flow grade score was evaluated before and after PCI [34]. After the analysis of serial creatinine measurements during their hospital stay, the occurrence of renal function worsening was assessed and defined as lowering of glomerular filtration rate by at least 30% compared to admission values. Based on data obtained from the Universal Electronic System for Registration of the Population in Poland, the occurrence of death from all causes at 30 days from the admission to the hospital was evaluated for all study participants.

Statistical analysis

Categorical variables were presented as numbers and percentages. Continuous variables were expressed as means and standard deviation (SD) or medians and interquartile range (IQR). Normality was assessed by the Shapiro–Wilk test. Equality of variances was assessed using Levene’s test. The study’s population was divided into two groups according to their glucose levels on admission (normoglycemia group: < 7.8 mmol/L, hyperglycemia group: ≥ 7.8 mmol/L). Differences between groups were compared using the Student or Welch t-test depending on the equality of variances for normally distributed variables. The Mann-Whitney U-test was used for non-normally distributed continuous variables. Ordinal variables were compared using the Cochran–Armitage test for trend. Categorical variables were compared by the Pearson χ2 test or by the Monte Carlo simulation for the Fisher test if 20% of cells had an expected count of less than 5. The Spearman Rank correlation coefficient was calculated to measure the monotonic trend between two variables. Multivariable logistic regression was used for searching possible covariates of the likelihood of hyperlactatemia (lactates level on admission ≥ 2.0 mmol/L). Then odds ratios (OR) and corresponding 95% confidence intervals (95% CI) were calculated for possible covariates influencing the occurrence of hyperlactatemia. To analyze event-free survival in the groups according to their glucose and lactate levels, Kaplan-Meier curves were generated. The log-rank statistic was used to test for the differences in outcomes between the groups. Additionally, univariable and multivariable Cox proportional hazard analyses were performed to identify independent predictors of mortality. The variables used in the univariable analyses were selected as potential risk factors based on their clinical relevance for death at a 30-day follow-up period. The variables selected for the final multivariable model had to be clinically significant and associated with an increased risk of 30-day mortality during the univariable analyses (p-value < 0.05). The variables entered into the Cox regression model were demographic and clinical variables including sex, body mass index, STEMI, MI in their history, arterial hypertension, smoking, left ventricular ejection fraction (LVEF), hyperglycemia (glucose on admission ≥ 7.8 mmol/L), hyperlactatemia (lactates on admission ≥ 2.0 mmol/L), post-procedural TIMI flow grade 0–2, time from pain to hospital admission and GRACE score [35]. Due to the fact that some variables (age, cardiac arrest before admission, eGFR on admission, Killip class, heart rate on admission, systolic blood pressure on admission, the presence of ST segment deviation on electrocardiogram on admission and abnormal results of cardiac hsTnI on admission) were used to calculate the GRACE scores, only GRACE score was included in the final multivariable analysis (instead of the abovementioned variables). The results are presented as hazard ratios (HR) with 95% CI. The proportional hazards model assumptions were checked using Schoenfeld test and graphical diagnostics. Statistical analyses were performed with JMP®, Version 14.2.0 (SAS Institute INC., Cary, NC, USA) and using R, Version 3.4.1 (R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2017, https://www.r-project.org/).

Results

The present study group consisted of 405 patients (72.8% males), with MI. The mean (SD) age was 65.70 (12.21) years. There were 183 (45.2%) STEMI and 222 (54.8%) NSTEMI patients. Arterial hypertension (70.4%) was the predominant coexisting disease. There were 44 (10.9%) patients with Killip class 3 or 4. Cardiac arrest before admission occurred in 20 (4.9%) subjects. Median (IQR) time from pain to hospital admission was 663,0 (182.0–792.0) min. Median (IQR) GRACE score was 131.8 (107.8–160.9), median (IQR) LVEF 48.0% (38.0–55.0). Left main coronary artery (LMCA) was an infarct related artery in 13 (3.2%) patients and 177 (43.7%) subjects had multi-vessel disease. In 19 (4.7%) patients TIMI 0 persisted after PCI and the 30-day mortality rate was 5.4% (n = 22).

There were 302 patients with a glucose level < 7.8 mmol/L (normoglycemia group), and 103 patients with ≥ 7.8 mmol/L (hyperglycemia group). Patients with higher glucose levels were older, were more frequently non-smokers and in more severe general clinical conditions (lower blood pressure, higher Killip class, higher GRACE score and more frequently had out-of-hospital cardiac arrest before admission). Higher lactate levels (reference range < 2.0 mmol/L) were noticed in patients with higher glucose levels on admission (Table 1).

The location of the culprit-related artery in angiography was similar in both groups with the only exception: LMCA was the more frequent culprit-related artery in patients with hyperglycemia when compared to the normoglycemia group. Occurrence of TIMI 0–2 after PCI was similar in both groups while a worsening of kidney function and the need for mechanical ventilation occurred more often in the hyperglycemia group than in the normoglycemia group. Lower frequency rate of beta-blocker and higher catecholamines use was observed in hyperglycemic group in comparison to normoglycemic group. Patients with higher glucose levels (hyperglycemia group) had a significantly higher 30-day mortality rate (Table 2).

Significant correlations between GRACE scores, glucose levels and lactate levels at admission were observed and there was a significant positive correlation with glucose and lactate levels as seen in Figure 2.

In a multivariable logistic regression model (including Killip class, STEMI, GRACE score, age, LVEF, out of hospital cardiac arrest and hyperglycemia), it was determined that the severity of the clinical state on admission (assessed by Killip class) and hyperglycemia observed on admission were significantly associated, after adjustment, with hyperlactatemia (lactates ≥ 2 mmol/L) (Fig. 3). In comparison to the survivors, the patients who died prior to the 30-day follow-up period had significantly higher admission glucose levels (median [IQR], 9.7 [7.5–13.0] vs. 6.6 [5.8–7.6] mmol/L, p < 0.001) and admission lactates (median [IQR], 4.4 [2.1–8.6] vs. 1.5 [1.1–2.0], p < 0.001) (Fig. 4).

The Kaplan-Meier estimate showed that patients with coexisting stress hyperglycemia and hyperlactatemia had the lowest survival rate in comparison to patients with either normal lactate levels (both normoglycemic and hyperglycemic groups) or hyperlactatemia with normoglycemia (Fig. 5).

The Cox proportional hazards model was constructed to evaluate the contribution of the studied parameters — glucose and lactate levels — on the prognosis measured at 30-day mortality. In multivariable analysis both hyperglycemia and hyperlactatemia next to GRACE score were independently associated with an increased risk of death in 30-day follow up (Table 3).

Discussion

In the current study, it was confirmed that in non-diabetic MI patients treated with PCI, stress hyperglycemia at admission is a marker of worse clinical outcome. Moreover, it was found that increased glucose levels were positively correlated to lactate levels. It was also confirmed that the severity of the patient’s state at admission and higher glucose levels are associated with a higher probability of the occurrence of hyperlactatemia. The coexistence of both hyperglycemia and hyperlactatemia is associated with lower survival rate at 30-day follow-up and in multivariable analysis, both hyperglycemia and hyperlactatemia remained independent predictors of higher mortality.

Despite the pathophysiological rationale indicating that hyperglycemia may be directly linked to lactate metabolism, only a few papers evaluated the simultaneous relationship of hyperglycemia and hyperlactatemia in critically-ill patients [25–29] and according to available research, there are no studies assessing this phenomenon in patients with MI treated with PCI. Stress hyperglycemia is associated with hyperlactatemia in critically-ill patients including patients with MI via several mechanisms. It appears that in patients with MI with varying degrees of circulatory compromise, an increased blood lactate concentration is related to its increased production [18]. Classically, increased lactate production is explained by dysoxia at the tissue level, which leads to anaerobic glycolysis and consequent lactate production [18]. On the other hand, there are indications that lactate formation can also manifest even in the absence of dysoxia, such as after catecholamine release in response to a stressful stimulus (in this case — MI) or other factors (ex.: triggered in a similar mechanism by an inflammatory response cascade) stimulating Na+/K+ adenosine triphosphatase activity, which in turn, augments glycogenolysis [18, 36]. The increased hepatic glycogenolysis and gluconeogenesis observed during MI (which represents a degree of hypermetabolic stress for the body), impaired insulin secretion by pancreatic cells, increased insulin resistance and reduced glucose consumption in the periphery are mechanisms that attenuate glucose-lactate cycling and lead to an increased lactate production resulting in hyperlactatemia. Subsequently, accumulated lactates can be cleared by oxidation to pyruvate or transformed into glucose by gluconeogenesis or into glycogen via the Cori cycle [24]. Hyperlactatemia appears to inhibit glucose uptake by muscle cells and decrease activity of the GLUT-4 transporters, resulting in hyperglycemia [37]. Hyperlactatemia has also been shown to increase insulin resistance directly [38]. Revelly et al. [18] conducted a study in which they evaluated the relationship of lactate and glucose levels in cardiogenic shock by infusing radiolabeled lactate and glucose into critically-ill adults and healthy volunteers. They showed that increased production of lactate was concomitant to markedly increased glucose turnover, which they considered the main cause that contributed to the hyperlactatemia in this group of patients [18]. This investigation was experimental in which 7 patients in cardiogenic shock were included. In the present observational study in which 26 (6.4%) patients had Killip 4, it was confirmed that a severe clinical condition and increasing glycemic levels are factors that increase the likelihood of hyperlactatemia.

Based on the present results, it was confirmed that the coexistence of both hyperglycemia and hyperlactatemia is associated with lower survival rate at a 30-day follow-up and in multivariable analysis, both hyperglycemia and hyperlactatemia remained independent predictors of higher mortality. Observations herein, are consistent with prior literature [28, 39]. It is believed, based on the results, that such a correlation is further evidence that glycemia and lactate share a common metabolic pathway as a function of carbohydrate metabolism and can be explained by the fact that during an increased stress response, such as MI, hyperglycemia reflects not only an increased adrenergic response via altered gluconeogenesis but also by increased lactate production. It should be noted once again that most previous investigations had only analyzed hyperglycemia and hyperlactatemia separately, without considering both parameters simultaneously.

The conclusions of the current study further explains the reasons for the failure of the NICE sugar trial [9]. Stress hyperglycemia cannot be considered as a simple parameter which can reduce mortality risk when it is being tightly controlled in a critically-ill patient. It is postulated herein, that interventions directed to the prevention of an excessive increase in lactate levels are of crucial importance in this population. In the case of MI patients, this includes effective revascularization without unnecessary delay, a more prompt introduction of medications to optimize cardiac output or improvement of oxygen delivery to tissues (catecholamines, diuretics, or fluid supplementation, oxygen therapy, correction of hemoglobin level, etc.).

The results of the present study have some clinical implications. Among patients with MI, there is a whole range of clinical situations when risk stratification is not straightforward, e.g.: especially in patients with NSTEMI, electrocardiography has insufficient sensitivity and specificity [40, 41] and risk scales e.g.: GRACE are time-consuming. In these situations, laboratory markers can be utilized to allow for a more prompt and streamlined approach to triage patients with suspected MI and to screen those with a higher risk of mortality. Due to the increased availability of point of care analyzers which have been tested and confirmed to be reliable in multiple previous investigations [42–44], the screening for glucose and lactate levels at admission should be encouraged and is an “easy-to-obtain” method to aid in risk stratification. As demonstrated in this investigation, even seemingly stable patients at admission may quickly deteriorate if hyperglycemia and hyperlactatemia are found, meaning these blood tests could serve as markers of organ hypoprivation and indicate a need for more decisive intervention.

Limitations of the study

There are several limitations in this study. Firstly, this study was a single-center observational, retrospective study. This data collection method is prone to misclassification and selection bias. Additionally, it cannot be excluded that, at least theoretically, other confounding factors could exist, which would modify the association between glucose and lactates and would impact on their relationship with increased mortality risk. However, the correlations cited and the common metabolic pathways shared by glucose and lactate metabolism are so significant that the possible influence of other factors on the results obtained seem to be insignificant. Additionally, the present analysis was conducted before the severe acute respiratory syndrome coronavirus 2 infection era, thus, the potential relationship between stress hyperglycemia, lactates and the outcome among patients with MI affected by COVID-19 was not assessed.

Conclusions

There is a relationship between hyperglycemia and hyperlactatemia in non-diabetic MI patients treated with PCI and both markers are independent predictors of 30-day mortality. Moreover the coexistence of hyperglycemia and hyperlactatemia decreases survival much more than each factor separately then should be evaluated simultaneously.

Figure 1 Flow chart of patient inclusion; DM — diabetes mellitus; MI — myocardial infarction, OGTT — oral glucose tolerance test; PCI — percutaneous coronary intervention

Figure 2 A–C. The correlation between glucose, lactate and Global Registry of Acute Coronary Events (GRACE) score

Figure 3 Multivariable logistic regressions demonstrating the adjusted odds ratio for diagnosis of hyperlactatemia (lactates levels ≥ 2.0 mmol/L); GRACE — Global Registry of Acute Coronary Events; LVEF — left ventricular ejection fraction; OR — odds ratio; STEMI — ST segment-elevation myocardial infarction; N — no; Y — yes

Figure 4 Glucose (A), lactate (B) levels in the comparison between survivors and non-survivors at a 30-day follow-up. Boxes represent the median and the 25–75th percentile

Figure 5 The Kaplan-Meier curve displaying proportional survival rate in 30 day follow-up stratified by glucose and lactate levels on admission

Table 1 Baseline characteristics according to glycemic status on admission

	Normoglycemia (glucose < 7.8 mmol/L), n = 302 (74.6%)	Hyperglycemia (glucose ≥ 7.8 mmol/L), n = 103 (25.4%)	P	
Age [years]	64.8 (11.9)	68.5 (12.6)	0.007	
Male	232 (76.8%)	63 (61.2%)	0.002	
BMI [kg/m2]*	27.5 (4.3)	27.7 (4.9)	0.68	
STEMI	134 (44.4%)	49 (47.6%)	0.33	
Cardiac arrest before admission	5 (1.7%)	15 (14.6%)	< 0.001	
MI in the history	65 (21.5%)	22 (21.4%)	0.55	
Arterial hypertension	206 (68.2%)	79 (76.7%)	0.07	
Smoking	119 (39.4%)	26 (25.2%)	0.006	
Atrial fibrillation	29 (9.6%)	19 (18.4%)	0.049	
LVEF [%]**	45.9 (12.2)	43.0 (13.3)	0.06	
Heart rate [min−1]	80.0 (16.8%)	79.7 (18.8%)	0.86	
Systolic BP [mmHg]	143.0 (127.0–160.0)	139 (119.0–152.0)	0.003	
Diastolic BP [mmHg]	80.0 (71.3–90.0)	78.0 (64.5–90.0)	0.001	
Glucose [mmol/L]	6.2 (5.6–6.8)	9.3 (8.4–11.6)	< 0.001	
Troponin I hs [umol/L]	9296.14 (9247.56)	8393.59 (9271.13)	0.63	
eGFR [mL/min/1.73 m2]	94.6 (33.8)	86.5 (34.1)	0.04	
Lactates [mmol/L]	1.4 (1.0–1.8)	2.3 (1.5–3.7)	< 0.001	
Killip 3 or 4	19 (6.3%)	25 (24.3%)	< 0.001	
GRACE score [points]	129.4 (105.7–154.5)	143.4 (115.4–178.9)	0.002	
Time from pain to hospital admission [min]	360 (145–713)	255 (131–410)	0.477	
Continous data are presented as mean (standard deviation) or median (interquartile range) unless indicated otherwise;

* Data available for 247 patients with normoglycemia and 76 with hyperglycemia;

** Data available for 298 patients with normoglycemia and 103 with hyperglycemia;

BMI — body mass index; BP — blood pressure; eGFR — estimated glomerular filtration rate; GRACE — Global Registry of Acute Coronary Events; LVEF — left ventricular ejection fraction; MI — myocardial infarction; STEMI — ST-segment elevation myocardial infarction

Table 2 Angiography results, in-hospital drug therapy and patient outcome according to glycemic status on admission

	Normoglycemia (glucose < 7.8 mmol/L), n = 302 (74.6%)	Hyperglycemia (glucose ≥ 7.8 mmol/L), n = 103 (25.4%)	P	
LMCA as IRA	6 (2.0%)	7 (6.8%)	0.03	
LAD as IRA	109 (36.1%)	43 (41.7%)	0.18	
Cx as IRA	71 (23.5%)	23 (22.3%)	0.46	
RCA as IRA	102 (33.8%)	32 (31.1%)	0.35	
Multivessel disease	129 (42.7%)	48 (46.6%)	0.28	
Post-procedural TIMI flow grade 0–2	24 (7.9%)	17 (16.5%)	0.013	
ACEI/ARB	269 (89.1%)	89 (87.3%)	0.47	
Beta-blockers	267 (88.4%)	78 (76.5%)	0.002	
Statins	284 (94.0%)	91 (89.2%)	0.10	
MRA	57 (18.9%)	31 (30.4%)	0.02	
Calcium blockers	49 (16.2%)	14 (13.7%)	0.52	
Glycoprotein IIb/IIIa inhibitors	36 (11.9%)	15 (14.7%)	0.49	
Catecholamines	22 (7.3%)	35 (34.3%)	< 0.001	
Worsening of kidney function	44 (14.6%)	27 (26.2%)	0.007	
Mechanical ventilation	7 (2.3%)	22 (21.4%)	< 0.001	
Death from any cause within 30 days since admission	7 (2.3%)	15 (14.6%)	< 0.001	
ACEI — angiotensin-converting enzyme inhibitors; ARB — angiotensin receptor blockers; Cx — left circumflex artery; IRA — infarct related artery, LAD — left anterior descending artery; LMCA — left main coronary artery; MRA — mineralocorticoid receptor antagonists; RCA — right coronary artery; TIMI — thrombolysis in myocardial infarction

Table 3 Univariable and multivariable Cox regression model assessing the risk of death at a 30-day follow-up

Variable	Univariable analysis	Multivariable analysis	
		
HR (95% CI)	P	HR (95% CI)	P	
Male sex	0.99 (0.39–2.54)	0.99			
STEMI	5.64 (1.91–16.66)	< 0.001	3.70 (1.00–13.72)	0.05	
MI in the history	0.81 (0.28–2.40)	0.71			
Arterial hypertension	0.89 (0.36–2.19)	0.81			
Smoking	0.39 (0.13–1.16)	0.08			
Time from pain to hospital admission (per 10 min)	0.98 (0.96–1.01)	0.23			
LVEF (per 1%)	2.84 (1.05–7.69)	0.03	0.97 (0.93–1.01)	0.10	
Hyperglycemia (glucose on admission ≥ 7.8 mmol/L)	6.62 (2.70–16.25)	< 0.001	3.21 (1.04–9.93)	0.043	
Troponin I hs (per 50 umol/L)	0.99 (0.99–1.00)	0.37			
Hyperlactatemia (lactates on admission ≥ 2.0 mmol/L)	15.47 (4.58–52.27)	< 0.001	7.08 (1.44–34.93)	0.016	
Post-procedural TIMI flow grade 0–2	10.17 (4.40–23.48)	< 0.001	2.26 (0.70–7.24)	0.17	
GRACE score (per 1 point)	1.03 (1.02–1.03)	< 0.001	1.02 (1.005–1.03)	0.003	
CI — confidence interval; GRACE — Global Registry of Acute Coronary Events; HR — hazard ratio; LVEF — left ventricular ejection fraction; MI — myocardial infarction; TIMI — Thrombolysis in Myocardial Infarction; STEMI — ST-segment elevation myocardial infarction

Conflict of interest: None declared.
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