Vol.48 - Número 2, Abril/Junio 2019 Imprimir sólo la columna central

Mortality associated with hyperglycemia in non-diabetic, post- revascularization patients with acute myocardial infarction, during the years 2007-2015 in the private clinic Ricardo Palma. Lima, Peru

HEYLA X MINAYA VITOR, FERNANDO M. RUNZER COLMENARES
Clínica Ricardo Palma. San Isidro (15036) Lima, Perú
E-mail
Recibido 11-DIC-2018 – ACEPTADO después de revisión el 04-FEBRERO-2019
There are no conflicts of interest to disclose.

 

ABSTRACT

Acute coronary artery disease is a pathology that demands a high clinic suspicion and early treatment. Likewise, hyperglycemia is well known as a direct mediator of adverse outcomes and a severe marker of coronary artery disease. The Aim of the study is to measure the association between mortality and hyperglycemia at admission in non-diabetic infarcted patients after revascularization.
Methods: The design was of the observational analytic type, with secondary data revision, and had a total population of 738 and the sample was 218. Data analysis was made with Stata v.14.0, and was made one variable data tables. Furthermore, we used descriptive analysis using measures of central tendency, dispersion, and percentage distribution in absolute and relative frequencies, bivariate descriptive statistics and inferential analysis using Chi-squared test for categorical variables and student’s t test for numerical variables. Moreover, we used the Poisson regression technique.
Results: The outcomes whose p value was significant showed that age, macrovascular damage, and high serum glucose values during the year of follow-up were related to higher mortality.
Conclusions: Hyperglycemia during the year following the cardiovascular event is an important predictor of mortality and could help us to reduce specific mortality rates if we perform a proper follow-up of glucose level within the national health plan of each country of the region.
Key words: Hyperglycemia, Myocardial infarction. Mortality.

 

INTRODUCTION
Acute coronary syndrome (ACS) is a pathology that demands a high clinical suspicion and early treatment. It is vital to remember that half of these patients die during the first hour, with no time to reach a nearby health care center [1]. We should also highlight that the percentage of recurrence of a coronary event ranges from 10 to 20% within the 10 years after the first ischemic event [2].

According to the last report of the World Health Organization (WHO), ACS ranks in the first place as overall mortality cause, both at world and continental level [3]. In Peru, the last national report on overall mortality remarkably showed that ischemic heart diseases rank sixth [4]. Likewise, it is important to identify cardiovascular risk factors, among which the following stand out: history of previous ischemic heart disease, family history of early heart disease, age, gender, diabetes mellitus (DM), altered metabolic state, hypertension, dyslipidemia, smoking, arteriosclerotic disease, obesity, sedentarism and stress; cardiovascular risk factors present again in the last update of the Argentine consensus on hypertension [5]; for this reason they were selected as variables in this study.

Different studies point out hyperglycemia as a metabolic state frequently present in patients with ACS. Recent reports warn that from 25 to 50% of ACS patients present hyperglycemic values in admission, and it has also been reported that patients with diagnosis of acute myocardial infarction (AMI) with hyperglycemia in admission presented a higher relative risk when compared to patients with normal blood glucose levels [6].

Hyperglycemia is already acknowledged as a direct mediator of adverse results and a marker of greater severity of the disease as it is linked to less collateral coronary circulation, increase in the size of infarction, apoptosis, ischemic pre-conditioning decrease, and a glucotoxic state existing by increase in oxidative stress [7].

There is not enough information about the increase in mortality rate and associated factors, which entails hyperglycemia in admission, in nondiabetic patients, and even less a long-term assessment as generally, this metabolic state is associated to a decompensation proper of diabetes.

From the observations of the HORIZONS AMI study or the DECODE group investigators, a reason appeared to perform a study from the collection of data from clinical records, both of outpatients and hospitalized patients, of nondiabetic, infarcted patients, who underwent cardiac catheterization in the Ricardo Palma private clinic in Lima, Peru, with the aim of estimating the mortality rate associated to hyperglycemia.

 

MATERIAL AND METHODS
The design was retrospective, analytic, and a secondary review of a database. The clinical records of all nondiabetic patients (n=738) with diagnosis of post-revascularization acute myocardial infarction, with hyperglycemia, upon admission into the Ricardo Palma private clinic, from year 2007 to 2015, were analyzed.

Most of the population were patients born and living in Lima, Peru. The level of complexity in the hospital center is level III-1, located in the district of San Isidro, Lima, Peru.

Inclusion criteria

  • Clinical history of patients with diagnosis of AMI.
  • Clinical history of patients who underwent coronary revascularization.
  • Clinical history of patients who were attended in the cardiology service of the Ricardo Palma Clinic.

Exclusion criteria

  • Clinical history of patients with no diagnosis of AMI.
  • Clinical history of patients with other cardiovascular pathologies, such as valvular heart diseases and cardiomyopathies.
  • Clinical history of patients with diagnosis of diabetes mellitus.
  • Clinical history of patients who did not undergo coronary revascularization.
  • Incomplete clinical records.

Sampling
In this study, data from all clinical records meeting the inclusion and exclusion criteria were collected, to finally obtain a sample of 218 patients.

Study procedures
The development of this investigation started with data collection from the clinical records at the Ricardo Palma clinic, so it was necessary to request the corresponding authorization from the Director of the Clinic through a letter addressed to the Independent Ethical Committee.

This study required the validation of the data collection instrument by experts’ judgement, which was requested to 3 specialists: two cardiologists and one endocrinologist. A file, consistency matrix and instrument were distributed to each specialist, to perform a binomial test that helped to measure the degree of concordance between experts, with a percentage above 70% (0.7), thus validating the instrument and enabling the use of this matrix with the purpose of this investigation, with which one-variable data tables and bar diagrams were created.

Data collection
Clinical records, both of outpatients and hospitalized patients, were reviewed over a 6-month term.

Activity supervision and monitoring
At the end of each month, the data collected by the clinical record provider were supervised. In September 2017, a detailed supervision was conducted on data collection at 50%, which was approved.

Variables of the study:
Age: Discrete quantitative variable, quantified in years, arranged in the following intervals: 30-40, 41-50, 51-60, 61-70 and >70.
Gender: Nominal qualitative variable, whether female or male.
Job: Nominal qualitative variable, arranged as employee, retired, or housewife.
Vital functions: The following were taken as sub-variables: systolic and diastolic blood pressure, heart rate upon admission.
Co-morbidities: The following were included: cardiovascular event, allergy, sedentarism, alcoholism, smoking, hypertension, dyslipidemia, chronic kidney disease. They were arranged into sub-categories, as they presented 0, 1 and ≥2 co-morbidities.
Admission electrocardiogram (ECG): All ECGs were recorded when patients arrived to the hospital. They were grouped into 3 categories: normal ECG, ECG with signs of ischemia by ST segment depression or elevation, and a final group categorized as “Others”, comprising the remaining “non-significant” electrocardiographic alterations, such as branch blocks or hypertrophies, etc.
Troponin T: Necrosis marker determined upon admission, with values expressed in µg/L.
CPK-MB: Necrosis marker upon admission, expressed in ng/dl.
Clinical symptoms upon admission: Symptoms and signs present upon admission which we grouped as asymptomatic, typical symptoms of angina, and atypical symptoms, and generally associated to epigastric pain, vomits and/or diarrhea.
Balloon time: It is the time since the onset of the index event (AMI) and the time to reach a center of high complexity, where percutaneous revascularization is performed. We arranged this variable in <6 hours, 6-12 hours and >12 hours.
TIMI RISK score: Thrombolysis in Myocardial Infarction (TIMI) [8,9], a score that predicts the risk of death at 30 days, in which a score of 0-1: risk of death of 4.7%; a score of 2: risk of 8.3%; a score of 3: a risk of 13.2%; a score of 4: a risk of 19.9%; a score of 5: a risk of 26%; and finally, a score of 6-7: a risk of death of 40.9%.
KILLIP score [10]: A score that predicts mortality according to the presence or not of heart failure and complications. Categorized as: KILLIP 1 with 5% of mortality; KILLIP 2 with 10%; KILLIP 3 with 40%, and KILLIP 4 with up to 90% of mortality.
Procedure: Nominal qualitative variable grouped into catheterization, stent implant, and others such as open surgeries of the bypass type, etc.
Follow-up: During the first year, different tests were performed such as electrocardiograms that are qualified according to whether they had ischemia or not; positive or negative stress test, cardiovascular event or no cardiovascular event, as chest angina; and finally macrovascular damage or not; or at least acute myocardial infarction, peripheral artery disease and/or stroke.
Drugs: Agents that are frequently indicated in the studied population, such as: acetylsalicylic acid, ACEIs, ARBs, calcium antagonists, beta blockers, clopidogrel, statins and/or diuretics.
Glucose: Continuous quantitative variable, determined in serum upon admission, the mean and standard deviation of which were grouped into maximum and average values.
Glycosylated hemoglobin (HbA1c): Continuous quantitative variable upon admission, and standard deviation of average value.
Hemoglobin: Continuous quantitative variable, with minimum value of the study population.
Creatinine: Continuous quantitative variable, with minimum value of the study population.
Mortality: Applied in a general manner, to thus reach some level of association with the independent variables.

 

STATISTICAL ANALYSIS
The database was constructed with the Stata v.14.0 software, with which one-variable data tables were made.

  • Descriptive analysis of data:

This modality of analysis was made with the processed information. Thus, for quantitative variables, central tendency measures were estimated, and dispersion measures such as standard deviation. For the qualitative variables, the percentage distribution of data was estimated, both in absolute and relative frequency.

  • Inferential analysis

Bivariate analysis was made with chi square techniques for categorical variables, and student’s t test for numerical variables. For bivariate analysis, the population was divided according to the dependent variable (mortality), which measured the cases of deceased patients during the year of follow-up after the cardiovascular event.

  • Regression analysis

Finally, a Poisson regression analysis was constructed for the estimation of prevalence ratios and their respective confidence intervals at 95%, taking into account the statistically significant variables in the bivariate analysis. First, the unadjusted model was constructed, and later a model adjusted with all the variables included in the unadjusted model.

  • Statistical potential

The statistical power of this study was estimated taking as reference point the paper “Association between blood glucose and long-term mortality in patients with acute coronary syndromes in the OPUS-TIMI 16 trial” [11] that sought to estimate the degree of association between mortality and hyperglycemia; where as glucose quartiles increase, the mortality estimated at 10 months increases too. In turn, in the nondiabetic population, this mortality and hyperglycemia relationship was more significant (p=0.0001).

Taking into account what was mentioned before, a mortality of 2.7% was estimated in this study in individuals with normal glucose levels, and of 6.1%i n patients with altered glucose levels, obtaining a strength for the study of 99.51%.

 

RESULTS
Three tables were constructed, grouping the variables proposed for the study.

In Table 1, the list of variables to study is presented: N value, percentage and/or mean +/- standard deviation were included.

Table 1. Descriptive analysis of the study sample (n=218).
VARIABLES n %

Mean ± SD¹

Age in years
30-40
41-50
51-60
61-70
>70

6
24
57
65
65

2.76
11.06
26.95
29.95
29.95
 
Gender
Male
Female

160
58

73.39
26.61
 
Job
Employee
Retired
Housewife

135
74
9

61.93
33.94
4.13
 
Vital functions
Systolic blood pressure
Diastolic blood pressure
Heart rate
   
126 ± 21.97
75 ± 12.12
74 ± 12.33
Co-morbidities 2
0
1
≥2

13
30
175

5.96
13.76
80.28

ECG upon admission
Normal
Ischemia 3
Others

18
160
40

8.26
73.39
18.35
 
Troponin T µg/L
    0.99 ± 1.62
22.48 ±
CPK MB ng/dl     60.90

Clinical symptoms upon admission
Asymptomatic
Typical
Atypical


16
190
10

7.41
87.96
4.63
 
Balloon time
> 6 hours
6 - 12 hours
> 12 hours

51
30
137

23.39
13.76
62.85
 
TIMI RISK SCORE
4.70%
8.30%
13.20%
19.90%
26.20%
40.90%

188
8
5
7
8
2

86.24
3.67
2.29
3.21
3.67
0.92
 
KILLIP SCORE
I
II
III
IV

190
24
3
1

87.16
11.01
1.38
0.46
 
Procedure
Catheterization
Stent
Others

132
80
2

61.68
37.38
0.93
 
ECG 1st year
Normal
Ischemia 3

125
84

59.81
40.19
 
Stress test 1st year
Negative
Positive

191
26

88.02
11.98
 
Cardiovascular event 1st year
None
Angina

108
110

49.54
50.46
 
Macrovascular damage 1st year
None
At least one 4

205
13

94.04
5.96
 
Drugs 5
Minimum hemoglobin
Maximum creatinine
    2.1 ± 1.43
10.9 ± 4.37
0.93 ± 0.78
Glucose
Maximum
Average
   
152.6 ± 62.74
124.2 ± 33.08
Hb1Ac average     5.6 ± 1.01
Mortality
No
Yes

235
24

90. 73
9. 27
 
1 Standard deviation
2 Cardiovascular event, allergy, sedentarism, alcoholism, smoking,
hypertension, dyslipidemia and chronic kidney disease
3 STE and NSTE
4 Acute myocardial infarction, peripheral artery disease and/or stroke
5 Acetylsalicylic acid, ACEI, ARB, calcium antagonists,
beta blockers, clopidogrel, statins and/or diuretics

 

In Table 2, the same variables were included, but performing a bivariate analysis with chi squared techniques for qualitative variables and student’s t test for numerical variables. From this analysis, variables with a significant p value were obtained, such as age, KILLIP score, macrovascular damage during the first year of follow-up, hemoglobin (mean +/- standard deviation) and creatinine (mean +/- standard deviation).

Table 2. Bivariate analysis of the sample according to mortality during one-year follow-up (n=218).
VARIABLES Living at the end of follow-up n= 194 (88.99%) Living at the end of follow-up n= 194 (88.99%)

P value

Age
30-40
41-50
51-60
61-70
>70

6 (3.11)
24 (12.44)
57 (29.53)
65 (33.68)
41 (21.24)

0 (0)
0 (0)
0 (0)
0 (0)
24 (100)
<0.0001
Vital functions (mean ± SD1)
Systolic blood pressure
Diastolic blood pressure
Heart rate

126.27 ± 21.16
75.43± 11.31
74.67± 12

129.46 ± 28.05
76.08± 17.67
76.87± 14.87

0.5
0.8
0.4
Co-morbidities2
None
1
>1

12 (6.19)
24 (12.37)
158 (81.44)

1 (4.17)
6 (25)
17 (70.83)


0.2
Serum Troponin upon admission (mean ± SD1) 1.02± 1.65 0.76± 1.45 0.4
Serum CPK MB upon admission
(mean ± SD1)

Balloon time
> 6 hours
6 - 12 hours
> 12 hours



22.11± 62.31
46 (23.71)
27 (13.92)
120 (61.86)



24.82± 52.42
5 (20.83)
3 (12.5)
16 (66.67)



0.8
0.8
0.8
0.6

TIMI RISK SCORE
4.70%
8.30%
13.20%
19.90%
26.20%
40.90%

168 (86.6)
6 (3.09)
5 (2.58)
7 (3.61)
7 (3.61)
1 (0.52)

20 (83.33)
2 (8.33)
0 (0)
0 (0)
1 (4.17)
1 (4.17)
0.3
KILLIP SCORE
I
II
III
IV

168 (86.6)
23 (11.86)
3 (1.55)
0 (0)

22 (91.67)
1 (4.17)
0 (0)
1 (4.17)
0.02
Clinical symptoms upon admission
Asymptomatic
Typical
Atypical

16 (8.29)
170 (88.08)
7 (3.63)

0 (0)
20 (86.96)
3 (13.04)
0.054
Procedure
Catheterization
Stent
Others

118 (62.11)
71 (37.37)
1 (0.53)

14 (58.33)
9 (37.5)
1 (4.17)
0.2
Macrovascular damage 1st year
None
Present 3


185 (95.36)
9 (4.64)


20 (83.33)
4 (16.67)
0.02
ECG 1st year
Normal
Ischemia 4

114 (60.96)
73 (39.04)

11 (50)
11 (50)
0.3
Cardiovascular event 1st year
None
Angina

94 (48.45)
100 (51.55)

14 (58.33)
10 (41.67)
0.4
Stress test 1st year
Negative
Positive

169 (87.56)
24 (12.44)

22 (91.67)
2 (8.33)
0.6
       
Drugs 5 (mean ± SD1)
Hemoglobin (mean ± SD1)
Creatinine (mean ± SD1)
Maximum glucose (mean ± SD1)
Average glucose (mean ± SD1)
Average Hb1Ac (mean ± SD4)
2.12 ± 1.41
12.31 ± 3.7
0.9 ± 0.34
152.92 ± 64.64
124.01 ± 34.02
5.64 ± 1.02
2.26± 1.62
11.1 ± 2.85
1.08 ± 0.3
150.12 ± 45.42
125.79 ± 24.65
5.73 ± 0.91
0.7
0.004
0.04
0.8
0.8
0.7
1 Standard deviation
2 Cardiovascular event, allergy, sedentarism, alcoholism, smoking, hypertension,
dyslipidemia and chronic heart disease
3 Acute myocardial infarction, peripheral artery disease and/or stroke
4 STE and NSTE
5 Acetylsalicylic acid, ACEI, ARB, calcium antagonists, beta blockers, clopidogrel, statins and/or diuretics

 

In Table 3, Poisson regression analysis was made to determine the association between mortality, glycemia and associated factors, building unadjusted and adjusted models.

Table 3. Poisson regression analysis to determine association between mortality, glycemia and associated factors (n=218)
  VARIABLES Unadjusted model Adjusted model
    Prevalence ratio CI95%¹ Prevalence
ratio
CI95%¹
  Age
Clinical symptoms upon admission

Typical
Atypical
1.65

Reference
2.85

1.34-2.04


0.84-9.59

1.23

Reference
1.51

1.18- 1.32

0.75- 1.13

  Macrovascular damage 1st year
No
Yes


Reference
4.11



1.16-14.56


Reference
3.19



1.56-6.51

  Lower value of serum Hemoglobin during follow-up
0.93 0.87-0.99 0.96
0.91-1.02
  KILLIP Score
1.1 0.43-2.81 1.34
0.79-2.29
  Greater value of serum glucose during follow-up 1.15 0.81-1.61 1.01 1.01-1.02
1Confidence interval at 95%

 

A significant degree of association was established between the variables of age with a prevalence ratio (PR) of 1.65 (CI95% 1.34-2.04) in the unadjusted model and 1.23 (CI95% 1.18-1.32) in the adjusted model.

The variable macrovascular damage during the first year of follow-up presented in the unadjusted model a PR of 4.11 (CI95% 1.16-14.56) and 3.19 (CI95% 1.56-6.51) in the adjusted model.

The variable lower value of serum hemoglobin during the first year of follow-up, obtained in the unadjusted model, a PT of 0.93 (CI95% 0.87-0.99). In turn, the greatest variable of serum glucose during the first year of follow-up presented in the adjusted model a PR of 1.01 (CI 95% 1.01-1.02).

Finally, it was observed also, that the greater the age, the greater the increase in the prevalence of mortality: 1.65 times (CI95% 1.34-2.04) in the unadjusted model and 1.23 times (CI95% 1.18-1.32) in the adjusted model.

 

DISCUSSION
This investigation had as its aim to verify the association between high values of glycemia upon admission, in patients infarcted that received interventionist management, and increase in mortality rate, both in the short and the long term, besides focusing on the identification of prevalent associated risk factors in this group of patients, and that in some cases could go unnoticed due to a vertical and automatized approached when evaluating the infarcted patient.

In this study, it was verified that hyperglycemia was a factor associated to mortality independently from all the variables presented in the adjusted model. The variable greater value of serum glucose during the first year of follow-up presented in the adjusted model, a PR of 1.01 (CI95% 1.01-1.02), defining that its presence promotes a tragic outcome in patients with characteristics similar to the studied population.

Hyperglycemia as direct mediator of adverse outcome and a marker of greater severity of cardiovascular disease has already been reported in literature, by different study groups but not enough to include it as part of the national regional health care plan, and to perform strict controls subsequent to a cardiovascular event.

The HORIZONS-AMI study in patients with ST-segment elevation myocardial infarction, with indication of percutaneous coronary intervention, concluded that hyperglycemia upon admission was an independent predictor of early and late death in patients both with known and unknown diabetes [12].

A review article published in 2015 in the Revista de la Federación Argentina de Cardiología, highlights the significant impact of diabetes on the overall morbidity load indicating that hyperglycemia in a fasting state, presents HR of 1.2 associated to cardiovascular disease [13].

The DECODE study group did not prove an independent association between hyperglycemia and cardiovascular disease; however, they did identify the existing association between elevated values of glucose and mortality in women [14].

DESCARTES [15] is the first recording representative of non-ST-segment elevation acute coronary syndromes management in Spain, which shows that in spite of its high risk profile, these patients received suboptimal medical care according to the current clinical guidelines.

In Latin America, there is limited information about the impact of glucose metabolism alterations as a prognostic risk factor in acute myocardial infarction.

A cohort, observational, prospective, multicenter study with the participation of 8 centers in Colombia and Ecuador, which included 439 patients with confirmed diagnosis of AMI, of whom 305 (69.5%) had type-2 diabetes mellitus or pre-diabetes, concluded that abnormalities in glucose metabolism are significant in the prognosis in the short and long term for survivors of a first infarction [16].

A Cuban study determined that patients with hyperglycemia evolved with a greater incidence of adverse events and less survival; for this reason they suggested the significance of monitoring during the first year of follow-up after the cardiovascular event; however, it could not be considered as an independent predictor of mortality, which could be due to circadian changes in glycemia values and time variability since the last consumption of food, and the time when the patient is admitted with AMI, which may interfere with the glycemia values upon admission. Further, the glycemia value could be influenced by the administration of dextrose solutions indicated in these patients during their transportation to the hospital or upon their arrival to the center, so a small number of patients could present a false hyperglycemia [17].

In the Colombian population, altered glycemia (>100 mg/dl) was reported as a risk factor with greater strength of association to mortality in patients with acute coronary syndrome, regardless of the presence of other classical cardiovascular risk factors [18].

Hyperinsulinemia was also the most significant factor associated to the appearance of new cardiovascular events in Colombian patients with AMI, which emphasizes the fundamental role of insulin resistance in the pathophysiological mechanisms of atherosclerosis; particularly in developing countries [19].

A systematic review of 14 publications, in year 2000, which describes 15 studies, reported that stress hyperglycemia upon admission in individuals with AMI was associated to a significant increase in hospital mortality, congestive heart failure and shock, in diabetic patients and even more in individuals with no diabetes [20].

The association of glucose levels, necessarily not in a fasting state, with cardiovascular disease was determined prospectively in 1382 men and 2094 women from 45 to 84 years, in individuals participating in the Framingham Heart Study [21]. Multivariate analysis confirmed the independent association of glucose levels with cardiovascular disease in nondiabetic women, an independent risk factor not present in men.

 

LIMITATIONS
As this was a private clinic of high complexity, it is likely that data collection comes from clinical records of patients referred due to a greater complexity and severity of ongoing CAD, and in turn with more co-morbidities. Also, as this is a secondary review on data, there were missing data from the evaluated clinical records, which adds to what was mentioned above and imposes a bias to take into account in regard to the analysis conducted.

 

CONCLUSIONS
Although the determination of serum glucose levels upon admission in individuals with infarction did not display a statistical significance to associate it with mortality, hyperglycemia during the year following the cardiovascular event could be considered a significant mortality predictor, with a remarkable potential impact on public health by being able to reduce mortality rate if a proper follow-up is made on this variable in the regional health care plans.

Other significant findings were the strength of the association existing between mortality and older age, presence of macrovascular damage and a lower hemoglobin value in serial controls during the year after myocardial infarction.

 

ACKNOWLEDGEMENTS
To the Ricardo Palma Clinic in the city of Lima, Peru, and Drs. Jorge Casana Bejarano and Jonathan Barraza Durand, specialists in Cardiology and Nephrology of the Ricardo Palma Clinic.

 

BIBLIOGRAPHY

  1. Solla Ruiz I, Bembibre Vazquez L, Freire Corzo J. Manejo del síndrome coronario agudo en urgencias de atención primaria en España. Cad Aten Primaria  2011; 18: 49-55.
  2. Vallejo E. Enfermedad arterial coronaria o cardiopatía isquémica: Dos entidades distintas con diferentes procedimientos diagnósticos (2nd ed). DF México. 2013; pp.534.
  3. WHO mortality report 2015 (revista electrónica) 2015; 1(1) Disponible en: http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html
  4. MINSA PERÚ. Mortalidad y sus principales causas (revista electrónica) 2018 Disponible en: http://www.minsa.gob.pe/estadisticas/estadisticas/Mortalidad/Macros.asp?00
  5. Consenso Argentino de Hipertensión Arterial. Federación Argentina de Cardiología. Rev Fed Arg Card 2018; Disponible en: http://www.fac.org.ar/2/revista/pdfs/Consenso-HTA%202018.pdf – Versión electrónica ISSN 1666-5694 – www.revistafac.org.ar
  6. Deedwania P, Kosiborod M, Barrett E, et al. Hyperglycemia and acute coronary syndrome. Circulation. 2008; 117 (12): 1610-19.
  7. Giakoumidakis K, Eltheni R, Patelarou E, et al. Effects of intensive glycemic control on outcome of cardiac surgery. Heart Lung 2013; 42 (2): 146-51.
  8. Antman EM, Cohen M, Bernink PJ, et al. The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making. JAMA 2000; 284 (7): 835-42.
  9. Amin ST, Morrow DA, Braunwald E, et al. Dynamic TIMI risk score for STEMI. J Am Heart Assoc 2013; 2: p. e003269
  10. Killip T III, Kimball JT. Treatment of myocardial infarction in a coronary care unit. Am J Cardiol 1967; 20 (2); 457-64.
  11. Bhadriraju S, Ray KK, DeFranco AC, et al. Association between blood glucose and long-term mortality in patients with acute coronary syndromes in the OPUS-TIMI 16 trial. Am J Cardiol 2006; 97 (11): 1573-77.
  12. Planer D, Witzenbichler B, Guagliumi G, et al. Impact of hyperglycemia in patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention: The HORIZONS-AMI trial. Int J Cardiol 2013; 167 (6): 2572-79.
  13. Muntaner J, Roggia R, Badimon J. Diabetes y aterotrombosis. Importante impacto en la carga global de morbilidad. Mecanismos fisiopatológicos involucrados. Rev Fed Arg Cardiol 2015; 44 (3): 133-38. Disponible en: http://www.fac.org.ar/2/revista/15v44n3/revision/revision01/muntaner.php
  14. The DECODE Study Group. Glucose tolerance and cardiovascular mortality. Arch Intern Med. 2001; 161: 397-404.
  15. Bueno H, Bardají A, Fernández-Ortiz A, et al. Investigadores del Estudio DESCARTES.Management of non-ST-segment-elevation acute coronary syndromes in Spain. The DESCARTES (Descripción del Estado de los Síndromes Coronarios Agudos en un Registro Temporal ESpañol) study. Rev Esp Cardiol 2005; 58 (3): 244-52.
  16. Gómez-Arbelaez D, Sánchez-Vallejo G, Pérez M, et al. Hiperglicemia se asocia a un mayor número de desenlaces adversos en individuos latinoamericanos con infarto agudo de miocardio. Clin Investig Arterioscler 2016; 28 (1): 9-18.
  17. García Cairo Y, González Rodríguez C, Jorrin Román F, et al. Hiperglucemia, marcador pronóstico de eventos adversos en el infarto agudo de miocardio. Rev Cubana de Cardiología y Cirugía Cardiovascular. 2013; 19 (2).
  18. Ramírez F, Gracia R, Silva F. Glicemia alterada en ayuno es el factor de riesgo más sensible en la enfermedad ateroesclerótica coronaria en pacientes colombianos con angina pectoris. Acta Médica Colombiana 2004; 29: 4-20. Disponible en: www.actamedicacolombiana.com/anexo/articulos/04.2004-04.pdf
  19. García RG, Rincón MY, Arenas WD, et al. Hyperinsulinemia is a predictor of new cardiovascular events in Colombian patients with a first myocardial infarction. Int J Cardiol 2011; 148 (1): 85-90.
  20. Capes SE, Hunt D, Malmberg K, Gerstein HC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet 2000; 355 (9206): 773-78.
  21. Wilson PW, Cupples LA, Kannel WB. Is hyperglycemia associated with cardiovascular disease? The Framingham Study. Am Heart J. 1991; 121 (2 Pt 1): 586-90.


Publication: June 2019



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