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Abstract

Background:

No data are so far available on the association between glycaemic variability and outcomes in patients with cardiogenic shock (CS) following ST elevation myocardial infarction (STEMI).

Methods:

We assessed the relationship between glycaemic variability and mortality, both short term and long term, in 67 consecutive patients with cardiogenic shock following STEMI admitted to our Intensive Cardiac Care Unit. Glycaemic variability was measured in the first 48 h by means of standard deviation (SD) of glucose values and the mean absolute glucose change per hour (MAGC) defined as the sum of all absolute glucose change divided by the time in hours.

Results:

Lower glycaemic variability was observed in survivors when compared with nonsurvivors, as indicated by lower values of SD and MAGC, respectively. In Cox regression analysis, MAGC and SD were independent predictors of death (MAGC: adjusted hazard ratio [HR]: 8.60, 95% confidence interval [CI]: 2.21–33.41, p = 0.002; SD: adjusted HR: 6.64, 95% CI: 1.92–22.99, p = 0.003), as well as peak glycaemia (adjusted HR: 1.95, 95% CI: 1.20–3.15, p = 0.007).

Conclusions:

According to our results, in patients with CS following acute myocardial infarction, early glycaemic variability is an independent predictor of mortality. Further studies are needed to confirm our results in larger cohorts and eventually to assess the effect of strategies specifically targeting glucose variability reduction on mortality.

Keywords

cardiogenic shock glucose values glycaemic variability mortality ST elevation myocardial infarction

Introduction

Glycaemic variability estimates glucose fluctuations which occur in a specific time frame [Kim et al. 2014], and patients with the same mean glucose levels may have different glycaemic variability patterns.

In critically ill patients, it was reported that the association between increased glycaemic variability and adverse outcomes, mainly increased mortality and morbidity (such as nosocomial infections and increased length of stay) [Krinsley, 2008], but no data are so far available on the association between glycaemic variability and outcomes in patients with cardiogenic shock (CS) following ST elevation myocardial infarction (STEMI).

The present investigation was therefore aimed at assessing the relationship between glycaemic variability and mortality, both short term and long term, in 67 consecutive patients with CS following STEMI admitted to our Intensive Cardiac Care Unit (ICCU).

Methods

The study population comprised 67 consecutive patients with CS following STEMI treated with primary percutaneous coronary intervention (PCI) and admitted to our ICCU from 1 January 2011 to 31 December 2013. All case data were collected prospectively, but retrospectively analysed.

A clinical diagnosis of CS was made in the presence of all the following criteria: (a) systolic blood pressure persistently lower than 90 mmHg or vasopressors required to maintain a systolic blood pressure of more than 90 mmHg; (b) signs of hypoperfusion (e.g. urine output less than 30 ml/h or cold/diaphoretic extremities or altered mental status); (c) clinical evidence of elevated left-ventricular filling pressure (e.g. pulmonary congestion on physical examination or chest X-ray) [Hochman, 2003; Richards and Wilcox, 2014]. Pulmonary artery catheterization was not required when clinical criteria and echocardiographic evidence of the left-ventricular dysfunction without mechanical complications were both present. The treatment of shock was according to international recommendations [Richards and Wilcox, 2014].

The Acute Physiology and Chronic Health Evaluation II (APACHE II) score was calculated on admission.

According to guidelines [Deedwania et al. 2008], intensive insulin therapy was administered in patients with significant hyperglycaemia (i.e. plasma glucose greater than 180 g/L).

Glucose values were measured every 12 h during the first 48 h after ICCU admission. Glycaemic variability was measured in the first 48 h by means of standard deviation (SD) of glucose values [Krinsley, 2008; Hermanides et al. 2010], and the mean absolute glucose change per hour (MAGC) defined as the sum of all absolute glucose change divided by the time in hours [Hermainides et al. 2010].

Primary outcome was all-cause death, during ICCU and at follow up.

Follow up was available in all patients discharged alive (8.8 months [0.1–17.8]). Ten patients died (10/38, 26%) during follow up.

The study protocol was in accordance with the Declaration of Helsinki and approved by the local ethics committee. Informed consent was obtained from all patients before enrolment. In patients mechanically ventilated, informed consent was obtained from their relatives.

Statistical analysis

Categorical variables are reported as frequencies and percentages; continuous variables are reported as mean ± SD or median (interquartile range). Between-groups (survivors and nonsurvivors) comparisons were assessed by means of Fisher’s exact test and Student’s t-tests (or Mann–Whitney U test when needed), respectively. After assessment of hazard proportionality, some models of multivariable Cox regression analyses were created in order to assess the predictive value of the glucose trend for long-term mortality. The outcome was death, both in ICCU and at follow up; candidate predictors were chosen as those known to be associated with mortality: age (1-year step), ejection fraction at admission (1% step), APACHE II score (1-unit step) and estimated glomerular filtration rate (5 ml/min/1.73 m² step) at discharge, to whom the parameters of glycaemic variability were in turn added for each model (IBM-SPSS 20 statistical package, SPSS Inc, Chicago, IL, USA).

Results

Our series included 67 CS patients (mean age 72.9 ± 12.4 years), mainly males (M:F, 40/27 (59.7/40.3). A history of diabetes was present in 19 patients (28.4%), while 37 patients were hypertensive (55.2%). A total of 29 patients died (43.3%) during ICCU stay,. In our series, length of stay was 96 (36–173) h (survivors: 120 h [60–206], nonsurvivors: 48 h [20–144]). When compared with survivors, nonsurvivors were older (dead: 76.8 ± 10.9 years versus survivors: 70.0 ± 12.8 years, p = 0.024), and showed a higher APACHE II score (dead: 26 [18–28] versus survivors: 17 [12–22], p < 0.001). No difference was observed in admission ejection fraction values between the two subgroups (dead: 28.3 ± 11.6 versus survivors: 32.5 ± 8.0, p = 0.085). In our series, no difference was detectable in the severity of coronary artery disease between survivors and dead patients. Intra-aortic balloon pumps were implanted in 67.2% (45/67) and revascularization was more frequently completed in survivors (p = 0.030).

Table 1 shows glucose values (mean ± SD) measured every 12 h for the first 48 h in all patients as well as in survivors and dead patients. Mean absolute glucose change per hour during the first 48 h is also shown in all patients as well as in survivors and dead patients. As depicted in Table 1, a significant and progressive decrease in glucose values was observed in all patients during the first 48 h from admission during which nonsurvivors showed significantly higher glucose levels than survivors. Lower glycaemic variability was observed in survivors when compared with nonsurvivors, as indicated by lower values of SD and MAGC, respectively. No difference was observed in nadir glycaemia between survivors and nonsurvivors (survivors: 0.91 [0.80–0.95], versus nonsurvivors: 0.97 [0.65–1.20] g/L, p = 0.375), while nonsurvivors showed significantly higher values of peak glycaemia (nonsurvivors: 2.24 [2.03–3.00] versus survivors: 1.89 [1.60–2.32] g/L, p = 0.008). Values of glycaemia < 0.70 g/L were observed in 10 patients: 3 patients among survivors (3/ 27, 11.1%) and 7 patients among nonsurvivors (7/39, 17.9%); (p = 0.446).

Table 1.

Glucose values and glucose variability during the first 48 h.

Glucose values	During the first 48 h
Mean ± standard deviation	Admission glycaemia	12 h	24 h	36 h	48 h	Within groups p
All	1.98 ± 1.11	1.40 ± 0.46	1.29 ± 0.40	1.34 ± 0.34	1.20 ± 0.28	< 0.001
Survivors	1.65 ± 0.72	1.41 ± 0.48	1.31 ± 0.39	1.31 ± 0.35	1.16 ± 0.25	< 0.001
Dead	2.40 ± 1.38	1.39 ± 0.45	1.26 ± 0.43	1.41 ± 0.33	1.32 ± 0.34
Between groups p	0.019
Glucose variability during the first 48 h			All patients (n = 67)	Survivors (n = 38, 56.7%)	Dead (n = 29, 43.3%)	p
Mean absolute glucose change per hour			0.42 (0.20–0.68)	0.37 (0.18–0.55)	0.63 (0.30–0.97)	0.011
Standard deviation			0.36 (0.23–0.58)	0.31 (0.18–0.43)	0.56 (0.29–0.91)	0.008

All values are median (interquartile range) and are reported as g/L.

At linear regression analysis, MAGC and SD were significantly related to mean glucose values, respectively (MAGC: coefficient 0.54, Pearson’s R² 0.34, p < 0.001; SD: coefficient 0.64, Pearson’s R² 0.38, p < 0.001)

Cox regression analysis is shown in Table 2. Different models are depicted to assess the predictive value of different glucose measurements for long-term mortality. In Cox regression analysis, MAGC and SD were independent predictors of death (MAGC: adjusted hazard ratio [HR]: 8.60, 95% confidence interval [CI]: 2.21–33.41, p = 0.002; SD: adjusted HR: 6.64, 95% CI: 1.92–22.99, p = 0.003), as well as peak glycaemia (adjusted HR: 1.95, 95% CI: 1.20–3.15, p = 0.007) .

Table 2.

Cox regression analysis.

	Adjusted HR	95% CI	p value
Model 1
Age (1-year step)	1.03	1.00–1.07	0.086
Admission LVEF (1% step)	0.98	0.93–1.03	0.393
APACHE II (1-unit step)	1.03	1.00–1.05	0.077
Admission eGFR (5 ml/min/1.73 m² step)	0.91	0.85–0.97	0.003
MAGC (1 g/L step)	8.60	2.21–33.41	0.002
Model 2
Age (1-year step)	1.04	1.00–1.08	0.040
Admission LVEF (1% step)	0.98	0.93–1.03	0.380
APACHE II (1-unit step)	1.02	1.00–1.05	0.097
Admission eGFR (5 ml/min/1.73 m² step)	0.92	0.86–0.97	0.006
SD (1 g/L step)	6.64	1.92–22.99	0.003
Model 3
Age (1 year step)	1.05	1.01–1.09	0.023
Admission LVEF (1% step)	0.98	0.94–1.03	0.442
APACHE II (1-unit step)	1.02	1.00–1.05	0.071
Admission eGFR (5 ml/min/1.73 m² step)	0.92	0.87–0.98	0.007
Peak glucose (1 g/L step)	1.95	1.20–3.15	0.007
Model 4
Age (1-year step)	1.04	1.01–1.08	0.027
Admission LVEF (1% step)	0.99	0.94–1.03	0.530
APACHE II (1-unit step)	1.03	1.01–1.05	0.019
Admission eGFR (5 ml/min/1.73 m² step)	0.92	0.86–0.97	0.003
Nadir glucose (1 g/L step)	2.98	0.79–11.30	0.108

APACHE II: Acute Physiology and Chronic Health Evaluation II; CI, confidence interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; LVEF, left-ventricular ejection fraction; MAGC, mean absolute glucose change per hour; SD, standard deviation.

Discussion

The main finding of the present investigation is that early glycaemic variability (as indicated by MAGC and SD) is an independent predictor of mortality in consecutive CS patients following STEMI.

The association between high glycaemic variability and mortality has been described in different populations of critically ill patients [Ali et al. 2008]. Dossett and colleagues investigated the impact of glycaemic variability in a cohort of ventilated surgical intensive care unit (ICU) patients [Dossett et al. 2008]. In this series, survivors and nonsurvivors had similar mean glucose levels during ICU stay, but nonsurvivors showed increased values of several measures of glycaemic variability. In 4084 critically ill patients, glycaemic variability was strongly and independently associated with mortality among critically ill nondiabetic patients, but not among patients with diabetes [Krinsley, 2009]. Egi and colleagues analyzed data on glucose values from 7049 Australian patients admitted to five different ICUs over 4 years and observed that glycaemic variability was a significant and independent predictor of ICU and hospital mortality, and that it was a stronger predictor of ICU mortality than mean glucose concentration [Egi et al. 2006]. More recently, in 297 medical and surgical patients receiving total parental nutrition [Farrokhi et al. 2013], high glycaemic variability was associated with increased mortality, independently of hypoglycaemia or hyperglycaemia. Few studies investigated the role of glycaemic variability in patients with acute myocardial infarction [Su et al. 2013; Zang et al. 2014; Wang et al. 2014]. Su and colleagues observed that elevated admission glycaemic variability was more important than admission glucose and prior long-term abnormal glycometabolic status (glycosylated haemoglobin) in predicting 1-year major cardiac adverse events (MACE) in 222 patients with acute myocardial infarction [Su et al. 2013]. In STEMI patients glycaemic variability remained an independent prognostic factor for composite MACE in STEMI patients undergoing primary PCI [Zang et al. 2014]. Similarly, in a small series of 34 patients glycaemic variability was an independent predictor of MACE in diabetic patients with acute myocardial infarction [Wang et al. 2014].

The present study assessed for the first time the impact of glucose variability in patients with CS following STEMI, and we documented that survivors showed lower glycaemic variability than nonsurvivors and that early glycaemic variability is an independent predictor of early mortality in our series.

There are several possible explanations for this association. First, lower glycaemic variability may indicate more attention in medical and nursing care. Second, higher glycaemic variability may be associated with more severe illness. Third glycaemic variability may have a true deleterious effect since there is growing evidence from experimental models [Piconi et al. 2004], and patient studies in diabetes mellitus that fluctuations in blood glucose may increase the risk of hyperglycaemia-induced oxidative stress [Monnier et al. 2006].

In the available literature [Yang et al. 2013; Vis et al. 2007], only the prognostic impact of admission glycaemia has been investigated in patients with CS following acute myocardial infarction, and it was reported that admission glucose levels were independently associated with mortality in these patients. In the present investigation, we further extended these findings observing that significantly higher glucose values are detectable in nonsurvivors during the first 48 h and that peak glycaemia is an independent predictor of mortality.

A possible limitation of our study is represented by the small number of enrolled patients. However, our series comprises consecutive CS patients, all managed by the same medical team according to implemented protocols for glucose management [Richards and Wilcox, 2014; Lazzeri et al. 2012]. Further studies are needed to confirm our results in larger cohorts and eventually to assess the effect of strategies specifically targeting glucose variability reduction on mortality. Another potential limitation may be the fact that in our study we measured glucose values every 12 h during the first 48 h after ICCU admission while recent papers [Su et al. 2013; Zang et al. 2014; Wang et al. 2014], assessing the role of glycaemic variability in patients with acute myocardial infarction, used a continuous glucose monitoring system, which is supposed to provide a more accurate measurement of glycaemic changes (since it provides a higher number of detection data). However, so far, there is no acknowledged gold standard for numerous indicators reflecting glycaemic variability, especially in critically ill patients [Meynaar et al. 2012].

Footnotes

Conflict of interest statement

The authors declare no conflicts of interest in preparing this article.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

Ali

O’Brien

Jr Dungan

Phillips

Marsh

Lemeshow

. (2008) Glucose variability and mortality in patients with sepsis. Crit Care Med 36: 2316–2321.

Deedwania

Kosiborod

Barrett

Ceriello

Isley

Mazzone

. (2008) American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Hyperglycemia and acute coronary syndrome: a scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 117: 1610–1619.

Dossett

Cao

Mowery

Dortch

Morris

Jr May

(2008) Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg 74: 679–685.

Egi

Bellomo

Stachowski

French

Hart

(2006) Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 105: 244–252.

Farrokhi

Chandra

Smiley

Pasquel

Peng

Newton

. (2013) Glucose variability is an independent predictor of mortality in hospitalized patients treated with total parenteral nutrition. Endocr Pract 20: 41–45.

Hermanides

Vriesendorp

Bosman

Zanstra

Hoekstra

DeVries

(2010) Glucose variability is associated with intensive care unit mortality Crit Care Med 38: 838–342.

Hochman

(2003) Cardiogenic shock complicating acute myocardial infarction: expanding the paradigm. Circulation 107: 2998–3002.

Kim

Rajan

Sims

Wroblewski

Reutrakul

(2014) Impact of glycemic variability and hypoglycemia on adverse hospital outcomes in non-critically ill patients. Diabetes Res Clin Pract 103: 437–443.

Krinsley

(2009) Glycemic variability and mortality in critically ill patients: the impact of diabetes. J Diabetes Sci Technol 3: 1292–1301.

10.

Krinsley

(2008) Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit Care Med 36: 3008–3013.

11.

Lazzeri

Valente

Chiostri

Attanà

Picariello

Gensini

(2012) The prognostic role of in-hospital peak glycemia in STEMI patients with and without diabetes. Acta Diabetol 49: 379–386.

12.

Meynaar

Eslami

Abu-Hanna

van der Voort

de Lange

de Keizer

(2012) Blood glucose amplitude variability as predictor for mortality in surgical and medical intensive care unit patients: a multicenter cohort study. J Crit Care 27: 119–124.

13.

Monnier

Mas

Ginet

Michel

Villon

Cristol

. (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295: 1681–1687.

14.

Piconi

Quagliaro

Da Ros

Assaloni

Giugliano

Esposito

. (2004) Intermittent high glucose enhances ICAM-1, VCAM-1, E-selecting and interleukin-6 expression in human umbilical endothelial cells in culture: the role of poly (ADP-ribose) polymerase. J Thromb Haemost 2: 1453–1459.

15.

Richards

Wilcox

(2014) Diagnosis and management of shock in the emergency department. Emerg Med Pract 16: 1–22.

16.

Tao

Yang

Zheng

. (2013) Impact of admission glycemic variability, glucose and glycosylated hemoglobin on major adverse cardiac events after acute myocardial infarction. Diabetes Care 36: 1026–1032.

17.

Vis

Sjauw

van der Schaaf

Baan

Jr Koch

DeVries

. (2007) In patients with ST-segment elevation myocardial infarction with cardiogenic shock treated with percutaneous coronary intervention, admission glucose level is a strong independent predictor for 1-year mortality in patients without a prior diagnosis of diabetes. Am Heart J 154: 1184–1190.

18.

Wang

Zhao

Dorje

Yan

Qian

(2014) Glycemic variability predicts cardiovascular complications in acute myocardial infarction patients with type 2 diabetes mellitus. Int J Cardiol 172: 498–500.

19.

Yang

Song

Hahn

Choi

. (2013) 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. Crit Care 17: R218.

20.

Zang

Cao

Yang

Zhou

(2014) Effect of glycemic variability on short term prognosis in acute myocardial infarction subjects undergoing primary percutaneous coronary interventions. Diabetol Metab Syndr 6: 76.

Early glucose variability in cardiogenic shock following acute myocardial infarction: a pilot study

Abstract

Background:

Methods:

Results:

Conclusions:

Keywords

Introduction

Methods

Statistical analysis

Results

Discussion

Footnotes

Conflict of interest statement

Funding

References