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IMPACT OF ON ADMISSION OSMOLALITY ON MAJOR ADVERSE
CARDIOVASCULAR EVENTS IN ACUTE MYOCARDIAL INFARCTION
PATIENT
COVER PAGEA graduating paper
Submitted to the board of examiners as
partial fulfillment of the requirement of
Bachelor Degree in Faculty of Medicine
Universitas Gadjah Mada
By:
ACINTYA SEKAR MAHARDHIKA
12/338670/KU/15329
FACULTY OF MEDICINE
UNIVERSITAS GADJAH MADA
YOGYAKARTA
2015
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APPROVAL PAGE
IMPACT OF ON ADMISSION OSMOLALITY ON MAJOR ADVERSE
CARDIOVASCULAR EVENTS IN ACUTE MYOCARDIAL INFARCTION
PATIENT
by
ACINTYA SEKAR MAHARDHIKA
12/338670/KU/15329
Tested and approved on November 10th 2015
Team of Graduating Paper Examiner
Material advisor
dr.Anggoro Budi Hartopo,
M.Sc.,Sp.PD.,Ph.D.
NIP: 19780718 201012 1 004
Methodology advisor
dr. Vita Yanti Anggraini,
M.Sc.,Sp.PD.,Ph.D.
NIP: 19630913 199003 2 001
Examiner
dr.Vina Yanti Susanti, M.Sc., SP.PD., Ph.D.
NIP: 1120110065
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AUTHENTICITY STATEMENT
IMPACT OF ON ADMISSION OSMOLALITY ON MAJOR ADVERSE
CARDIOVASCULAR EVENTS IN ACUTE MYOCARDIAL INFARCTION
PATIENT
By Acintya Sekar Mahardhika
12/338670/KU/15329
The graduating paper is a scientific paper produced by
my individual work and based on my knowledge. It does
not contain material written by other person as the
requirement for graduation in Universitas Gadjah Mada
or the other tertiary institution except certain parts
that there writer quoted as reference by following the
appropriate way and writing ethics of the common
scientific paper.
If, in the future, it is proven that this statement is
untrue, it is fully the writer’s responsibility.
Yogyakarta, November 10th 2015
Writer
Acintya Sekar Mahardhika
12/338670/KU/15329
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PREFACE
I would first like to thank Allah SWT without whom
nothing is possible. I would like to express my deep
gratitude to my supervisors dr. Anggoro and dr. Vita
for spending much time in helping me writing this
graduating paper, as well as dr. Vina as my expert
examiner. They are all hard-working doctors and I
believe their academic achievements will continue to
increase.
Big appreciation to my graduating paper partners,
Brilliant Winona Jhundy and Andanu Progoto Ersa for all
the helps, suggestions and entertainments in doing this
paper.
My deepest gratitude for my best friends who
became my family Dira Mediani, Valerie Hirsy Putri,
Anindya Larasati, Saraswati Anindhita, Muhammad Ivan
Aulia Sani, R. Sureswara Agrawijaya, Aghnia Amalia.
Immeasurable appreciation for my partner in crime,
Adrian Raditya, without you, I don’t know if medical
dream would ever become real.
Last but not least, I dedicated all my works for
my beloved family, thank you for being the angels of my
sweet little heaven, thank you for all the supports no
matter what the condition.
Yogyakarta, November 10th 2015
Acintya Sekar Mahardhika
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TABLE OF CONTENTS
COVER PAGE...................................................................................................................... i
APPROVAL PAGE ............................................................................................................. ii
AUTHENTICITY STATEMENT ....................................................................................... iii
INDEX OF ABBREVIATIONS ........................................................................................ x
ABSTRACT ......................................................................................................................... xi
CHAPTER I ....................................................................................................................... 1
INTRODUCTION ................................................................................................................ 1
A. Background ..................................................................................................... 1
B. Problem Formulation ............................................................................... 3
C. Objectives ..................................................................................................... 3
D. Research Authenticity .......................................................................... 3
E. Study Benefit ............................................................................................. 3
CHAPTER II..................................................................................................................... 5
LITERATURE REVIEW .................................................................................................... 5
A. Literature Review .................................................................................... 5
1. Acute Myocardial Infarction ........................................................ 5
2. Osmolality and Myocardial Infarction .................................. 7
3. Risk Factors Related Cardiovascular Events .................. 12
4. Major Adverse Cardiac Event ...................................................... 15
B. Basic Theory .............................................................................................. 17
C. Hypothesis ................................................................................................... 18
D. Theoretical Framework ................................................................... 19
E. Conceptual Framework ........................................................................... 19
CHAPTER III ................................................................................................................ 20
METHODOLOGY ................................................................................................................ 20
A. Type and Study Design ........................................................................ 20
B. Time and Study Setting ...................................................................... 20
C. Study Subjects ......................................................................................... 20
D. Sample Size ................................................................................................ 21
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E. Study Instrument .................................................................................... 23
F. Measurement and Collection Method ............................................ 23
G. Research Variable .................................................................................. 24
H. Operational Definition ...................................................................... 24
I. Statistical Analysis ........................................................................... 29
J. Ethical Consideration ........................................................................ 30
CHAPTER IV................................................................................................................... 31
A. RESULT AND DISCUSSION ............................................................................ 31
CHAPTER V ..................................................................................................................... 43
CONCLUSION AND SUGGESTION ............................................................................... 43
A. Conclusion ................................................................................................... 43
B. Suggestion ................................................................................................... 43
REFERENCES................................................................................................................... 44
APPENDIX ....................................................................................................................... 53
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INDEX OF TABLE
Table 1 Binary logistic analysis of Osmolality based on MACE .. 32
Table 2 . Characteristics of patients after grouping based on the
osmolality level ............................................... 34
Table 3 . Comparison of characteristics between patients with MACE
and patients without MACE ...................................... 37
Table 4. Binary logistic analysis of Age based on MACE ......... 38
Table 5. Binary logistic analysis of Onset based on MACE ....... 38
Table 6. Binary logistic analysis of Hemoglobin based on MACE .. 38
Table 7. Binary logistic of BUN based on mACE .................. 38
Table 8. Binary logistic analysis of variables with p-value less
than 0.05 ...................................................... 39
Table 9 ........................................................ 53
Table 10 ....................................................... 53
Table 11 ....................................................... 54
Table 12 ....................................................... 54
Table 13 ....................................................... 54
Table 14 ....................................................... 54
Table 15 ....................................................... 55
Table 16 ....................................................... 55
Table 17 ....................................................... 56
Table 18 ....................................................... 56
Table 19 ....................................................... 56
Table 20 ....................................................... 56
Table 21 ....................................................... 57
Table 22 ....................................................... 57
Table 23 ....................................................... 58
Table 24 ....................................................... 58
Table 25 ....................................................... 58
Table 26 ....................................................... 59
Table 27 ....................................................... 59
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Table 28 ....................................................... 59
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INDEX OF FIGURE
Figure 1. Incidence of MACE in the research .................................... 36
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x
INDEX OF ABBREVIATIONS
Abbreviation Full Citation
ACS
AMI
Acute Coronary Syndrome
Acute Myocardial Infarction
ATPase
aVF
Adenosine Triposphatase
Augmented Voltage Foot
aVL Augmented Voltage Left
BUN Blood Urea Nitrogen
CAD Coronary Artery Disease
CK-MB Creatinin Kinase MB
CKD Chronic Kidney Diseasae
df Degree of freedom
dl
ECG
deciliter
Electrocardiogram
ESRD End Stage Renal Disease
Exp(B) Exponentiation of B coefficient
ICCU Intensive CardioCare Unit
kg kilogram
LL
MACE
Log Likelihood
Major Adverse Cardiac Event
mEq/l Milliequivalent per liter
mg milligram
mm millimeter
mmol millimol
mOsmol milliosmol
NSTEMI Non-ST Elevation Myocardial Infarction
NYHA
PCI
New York Heart Association
Percutaneous Coronary Intervention
STEMI ST Elevation Myocardial Infarction
VF Ventricular Fibrilation
VT Ventricular Tachycardia
WHO United Nation World Health Organization
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IMPACT OF ON ADMISSION OSMOLALITY ON MAJOR ADVERSE
CARDIOVASCULAR EVENTS IN ACUTE MYOCARDIAL INFARCTION
PATIENT
AcintyaSekar Mahardhika, Anggoro Budi Hartopo, Vita
Yanti Anggraini
Faculty of Medicine, Universitas Gadjah Mada
ABSTRACT
Background : The main cause of death around the world is
cardiovascular disease. According to WHO, in 2008 there
are 17.3 million deaths caused by cardiovascular
disease, with 7.3 million is the result of myocardial
infarction. Some studies suggested that there is an
association between elevated blood glucose and BUN on
admission and mortality due to acute myocardial
infarction. As plasma glucose, BUN, and sodium are the
main component of osmolality, we should also analyze
the effect of on admission osmolality on clinical
endpoints in AMI patient. Previous studies show thatthe value of osmolality significantly related with
clinical outcome in ACS patient.
Objective: The objective of this research is to
investigate the influence of increasing osmolality on
to the incidence of MACE in AMI patients hospitalized
in ICCU
Method : The objective of this research is to
investigate the influence of increasing osmolality on
to the incidence of MACE in AMI patients hospitalized
in ICCU.
Results:This study is an observational study. The studydesign is a prospective cohort. The study is a part of
the previous study conducted in Department of
Cardiology and Vascular Medicine. Plasma osmolality was
calculated using concentration of sodium, plasma
glucose, and blood urea nitrogen on admission.
Conclusion:There is strong association between
increasing value of on admission osmolality and the
increasing of MACE occurrence.
Key Words: osmolality, AMI, MACE
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CHAPTER I
INTRODUCTION
A. Background
The main cause of death around the world is
cardiovascular disease, and is frequently associated
with acute myocardial infarction. According to WHO, in
2008 there are 17.3 million deaths caused by
cardiovascular disease, with 7.3 million (42% of all
cardiovascular deaths) is the result of myocardial
infarction. Cardiovascular disease give contribution
around 10% of the disability –adjusted life years
(DALYs) lost in low-middle income countries and around
18% of DALYs lost in high-income countries (Anonymous,
2012).
In clinical setting, serum biomarkers are
popularly used for risk estimation, since biomarkers
are sensitive and specific for acute myocardial
infarction (AMI). Some studies suggested that there is
an association between elevated plasma glucose on
admission and mortality due to acute myocardial
infarction. Other than plasma glucose, elevated blood
urea nitrogen (BUN) is highly predictive for mortality,
myocardial infarction, and stroke. As plasma glucose,
BUN, and sodium are the main component of osmolality,
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we should also analyze the effect of on admission
osmolality on clinical endpoints in AMI patient.
Previous studies show that the value of osmolality
significantly related with clinical outcome in ACS
patient. Osmolality is closely related with kidney
function. There is also reported that there is a
relationship between worsening kidney function in
patient of decompensate heart failure and poor clinical
outcome (Klein et al., 2008). Osmolality higher than
296 mosmol/kg at the time of admission shows increase
risk of death 2.4 folds higher (Bhalla et al., 2000).
Osmolality consists of three component, glucose,
BUN, and sodium. Each of the components of osmolality
shows a significant relationship with MACE (Major
Adverse Cardiovascular Event). Hyperosmolality caused
by hyperglycemia show deleterious effect on survival of
patient with ACS (Rohla et al., 2014). The increase of
BUN as the largest contributor of osmolality can become
a large predictor of death, recurrent myocardial
infarction, and congestive heart failure after 30 days
among ACS patient (Kirtane et al., 2005). As plasma
glucose, BUN, and sodium are the main components of
osmolality, we should also analyze the effect of on
admission osmolality on clinical endpoints in AMI
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patient, which probably will give benefit in predict
the outcome of AMI patients since the early.
B. Problem Formulation
Does the increase of osmolality have the
prognostic value for developing Major Adverse
Cardiovascular Events (MACE) in AMI patients?
C. Objectives
The objective of this research is to investigate
the influence of increasing osmolality on the incidence
of MACE in AMI patients hospitalized in ICCU.
D. Research Authenticity
During searching of previous studies and literature
review there is no other finding related to this
research. Therefore, it is considered that this
research is the first research studying about relation
of osmolality and MACE in AMI.
E. Study Benefit
This research is expected to bring additional
source of information in predicting factor of prognosis
in AMI patients. In addition, this research is also
expected to give reference to other researcher which is
interested to investigate further about plasma
osmolality. This research will also answer the
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clinician speculation about the role of measuring
plasma osmolality in AMI, because this factor is still
less considered in management of AMI patient.
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CHAPTER II
LITERATURE REVIEW
A. Literature Review
1. Acute Myocardial Infarction
In cardiovascular disease there is an ischemic
heart disease as the subtype. Ischemic heart disease
itself then divided into two groups of patient, chronic
coronary artery disease (CAD) and acute coronary
syndrome (ACS). Further, ACS is divided into three,
non-ST segment elevation acute myocardial infarction
(NSTEMI), ST segment elevation acute myocardial
infarction (STEMI), and unstable angina pectoris (UAP)
(Theroux, 2011).
Acute coronary syndrome (ACS) occurs due to the
imbalance between oxygen supply and demand of the
cardiac muscle. The factors that underlying this
condition, such as vigorous exercise, stress, and
decrease perfusion pressure due to hypotension, and
severely decreased in blood oxygen content due to
anemia (Lilly, 2011). The understanding of
pathophysiology of ACS has undergone a remarkable
change. Previously it consider as cholesterol storage
disease that currently known as atherosclerosis (Libby
& Theroux, 2005). The all region of infarction in ACS
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is caused by the development of culprit coronary
atherosclerotic plaque. Thrombosis is one of the major
causes of AMI. Thrombosis occurs because of two
different processes. The first process is the extension
of endothelial denudation so that large areas of
subendothelial connective tissue surface are exposed.
In that area thrombus is formed and adhere forming
plaque surface. The second process is plaque
disruption. Here the plaque cap disrupt and exposed
the lipid core into blood in the lumen of the artery.
The lipid core is highly thrombogenic; consist of
tissue fragment, fragments of collagen, and crystalline
surface that help accelerate coagulation (Davies,
2000). Furthermore the disrupted plaque then travelled
along the blood stream until finally stop in the
smaller vessel such as coronary artery and forming
vessel obstruction that leading to ischemia.
Patient is diagnosed as AMI based on the clinical
presentation, electrocardiographic findings, and serum
biomarker of myocardial damage. ECG in patients with
STEMI shows ≥ 1 mm elevation in more than 2 lead
II,III,aVF and I – aVL, ≥ 2 mm in V1-V6 and/or new
left bundle branch block (LBBB). Cardiac marker
troponin I, myoglobin, and CK-MB also increases in
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patients with STEMI aND will be no ST-segment elevation
in NSTEMI(Leonard, 2011).
2. Osmolality and Myocardial Infarction
Total body water is different between men and
women, and it is decreasing due to aging. Around 50-60%
of total body weight is water; two-third of it is
intracellular fluid, while one-third is extracellular
fluid. One-fourth of extracellular fluid is located in
intravascular space. The changes in body fluid may be
due to the loss of water, either from intracellular or
extracellular space. The changes of body fluid are best
evaluated from the changes of body weight (Guyton &
Hall, 2011).
Solute concentration of body fluid is measured by
osmolality. Osmoles per kilogram of water is
osmolality; osmoles per liter of solution is
osmolality. A physiological concentration of solute
normally is 285-295 mOsmol/kg. The two measurements of
osmolality and osmolality are clinically
interchangeable. Tonicity refers to osmolytes that is
impermeable toward cell membrane. The difference of
osmolyte concentration across cell membrane leads to an
osmosis and the shift of fluid, stimulation of thirst,
and secretion of antidiuretic hormone (ADH). Other
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substances that easily permeate cell membranes such as
urea and ethanol are ineffective osmoles, because they
do not cause fluid shift crossing the fluid
compartments (McPhee et al., 2015).
The estimation of the value of osmolality is
obtained from a formula,i.e. osmolality = 2Na +
(glucose/18) + (BUN/2.8), (Na is sodium, BUN is blood
urea nitrogen) (Brubacher et al., 2001). Based on the
formula, the important determinants of osmolality are
sodium, glucose and urea nitrogen level. Therefore,
their value should be gained from the laboratory result
and incorporated into the formula.
There are some factors that can cause the changes
of those three components, whether increasing or
decreasing. First component is sodium. Sodium is the
primary extracellular fluid cation. Sodium can be
pumped out to and entered from extracellular space
through Na+/K+-ATPase channel in the cell membrane.
When the sodium pumped into the intracellular, there
will be amount of potassium that is pumped out toward
the extracellular space. Acute change in serum sodium
will produce free shifting of water into and out of
extracellular space until osmolality equilibrates in
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this compartment (Richard, 2011). The rise of sodium
level is defined as hypernatremia.
Hypernatremia is defined as plasma sodium
concentration > 145mEq/l. Hypernatremia indicates a
decrease in total body water relative to sodium and is
invariably associated with plasma hyperosmolality
though total body sodium content may be normal,
decreased, or increased (Arora, 2013). It may be caused
by a primary gain in sodium or an excess of water.
Another source of sodium gaining is hyperaldosteronism,
Cushing’s syndrome, or excessive hypertonic saline or
sodium bicarbonate administration. Conversely, the
decrease of sodium is defined as hyponatremia.
Hyponatremia occur when the ratio of solute body water
content is altered by parallel changes in serum sodium
and osmolality. Generally hyponatremia defined as a
serum sodium concentartion < 135 to 136 mmol/L and
devided into 2 types, dilutional or depletional (Oren,
2005).
Mostly hyponatremia cases are caused by reduced
renal excretion of water with continued water intake or
by sodium loss from urine. Hyponatremia is usually
asymptomatic and most of the cases are associated with
low serum osmolality (Guyton & Hall, 2011).
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Hyponatremia is a common hospital-acquired electrolyte
disorder and is often related with high mortality and
morbidity due to progression of previous underlying
disease. In some research mention that hyponatremia
shown become a predictor of cardiovascular mortality
among patient with heart failure (Goldberg et al.,
2004). Hyponatremia also induced by diuretic medication
in congestife heart failure patien.
The second component is blood glucose. Blood
glucose levels fluctuated depend on food intake varies
over a 24-hour period. Normal range of fasting blood
glucose is between 70-100 mg/dl. The result outside
this range could be indicated as blood glucose
regulation dysfunction which usually occurs in diabetes
mellitus patient (James & McFadden, 2004). From the
previous study reported that glycaemia correlate with
short-term and long-term prognosis. Hyperglycemia
associated with endothelial dysfunction, platelets
hyperactivity, microvascular dysfunction, increase of
cytokine activity, increase level of free fatty acid,
and increase oxidative stress level, where all these
factors adversely affect outcomes in AMI. There is also
an addition, an increase of on admission plasma glucose
is a major independent predictor of in-hospital and
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long-term outcome of the patient with AMI (Nesto &
Lago, 2015).
The use of insulin in treating hyperglycemic
patient shows significant change in osmolality
component. In the research conducted by DeFronzo et al
shows there was no significant alteration of blood
glucose concentration with the remaining 96-99% blood
glucose alert. Plasma sodium concentration shows no
significant change, while the mean sodium clearance and
sodium excretion were both significantly change during
administration of insulin. There was also an
association between time of insulin administration and
insulin effect to the electrolytes in body fluid.
Insulin effect was significant in the first 30-60
minutes after administration and increasing during 60-
90 and 90-120 minutes collection period (DeFronzo et
al., 1974)
The third component is blood urea nitrogen. Blood
urea nitrogen normal range I s lie between 5 to 20
mg/dl, or 1.8 to 7.1 mmol/L, but normal range may vary
depending on the reference range used by the lab and
age (Hall et al., 1990). Generally, a high blood urea
nitrogen level related to kidney dysfunction. Another
factors causing elevation of blood urea nitrogen level
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are urinary tract obstruction, congestive heart failure
or recent heart attack, gastrointestinal bleeding,
dehydration, severe burns, and high protein diet
(Guyton & Hall, 2011). Higher concentration of BUN was
associated with the increase in mortality at 30 days
and throughout the follow up period. Patient with
higher BUN were have increase chance to have recurrent
myocardial infarction and congestive heart failure by
30 days, were likely to undergo revascularization, were
more likely to had stroke, and also had more frequent
adjudicated bleeding event (Kirtane et al., 2005). A
study about correlation between on admission osmolality
with all-cause of death in ACS patient has already been
done with the result of strong association between
admission osmolality and all-cause death in ACS patient
undergoing PCI (Rohla et al., 2014)
3. Risk Factors Related Cardiovascular Events
Chronic kidney disease (CKD) is a progressive loss
of renal function for months to years periods that
shows symptoms of uremia when GFR is reduce about 10-
15% from normal (Kasper et al., 2005). Hypertension is
the most common symptom that occurs first in CKD. From
many previous studies already showed that there was
strong correlation between renal failure and
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cardiovascular events. Around 50% of patients with end-
stage renal disease (ESRD) die from cardiovascular
cause (Schiffrin et al., 2007). The relationship
between renal disease and cardiovascular mortality has
also been shown mostly increasing in patients with
stage 3 to 4 CKD (GFR < 60 mL/min per 1.73 m2) rather
than progress to ESRD (Levin et al., 1996).
The most principal pathophysiological mechanisms
involved in the association of CKD and cardiovascular
event has been appointed to be endothelial dysfunction.
Many of traditional and non-traditional cardiovascular
risk factors that affect endothelial function can be
found in correlation with CKD. Another related
condition such as obesity, diabetes, and hypertension
are presence in renal dysfunction (Amann et al., 2006).
Endothelial dysfunction is recognized as the initial
mechanism of atherosclerosis formation. Endothelial
dysfunction that occur both in small and large arteries
is present in renal disease (Endemann & Schiffrin,
2004). Another experimental study showed that
endothelial dysfunction give contribution to the
mechanism lead to progression of renal disease
(Fujihara et al., 1995).
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Beside endothelial dysfunction, hypertension
represents a strong risk factor for CVD in CKD. Sodium
retention due to CKD activated renin-angiotensin system
has been recognized as the most important mechanism in
the elevation of blood pressure in patient with kidney
disease (Guyton et al., 1999). Renin-Angiotensin system
occurs in many types of renal disease. Angiotensin II
leads to generation of superoxide anion and play a role
to endothelial dysfunction and vascular remodelling and
growth by stimulates NADPH oxidase (Touyz & Schiffrin,
2004).
Elevation of inflammatory markers also been
recognized as one of the risk factors casing
cardiovascular event. The increasing level of
circulating inflammatory markers such as C-reactive
protein (CRP), serum amyloid A, interleukin-6, and
interleukin-1 receptor antagonist are commonly seen in
ACS. Those elevations correlate with in-hospital and
short-term adverse prognosis (Libby et al., 2002).
There are some triggers for inflammations in
atherogenesis process such as oxidize lipoproteins,
dyslipidaemia, hypertension, obesity, and infection.
Prevalence of endothelial dysfunction, low-grade
inflammation, and dyslipidaemia associated with
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incipient and progressive renal disease explain fast
progression of atherosclerosis and together with
hypertension explain the high prevalence of coronary
ischemia and cardiovascular events in CKD.
4. Major Adverse Cardiac Event
In Indonesia, the management of AMI patients
almost meet the protocol and proven that the prognosis
become well after undergoing the treatment. But still
there are some cases that usually is called as major
adverse cardiovascular events (MACE) emerging even
after a careful treatment. The exact definition for
this term often varies depending on the specific study.
The definition of MACE that use today by clinician at
the broadest level includes end points that reflect
safety and effectiveness with various treatment
approaches (Hollabaugh et al., 2008). A book written
by Kern et al defined MACE following percutaneous
coronary intervention (PCI) include death, peri-
procedural myocardial infarction, emergent coronary
artery bypass graft surgery (CABG), significant vessel
dissection or perforation, cerebrovascular accident, or
vasvular complication. At first the use of the term
MACE is refer to evaluate ‘net effect’, in which the
net effect refer to potential utility (effectiveness)
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2%, urgent or emergent CABG generally <2%, and Q-wave
myocardial infarction are 1-2% (Kern et al., 2006).
The risk factors in having in hospital MACE are
divided as clinical and procedural factors. Risk
factors included in clinical factors are multi-vessels
disease, decrease left ventricle (shock), comorbidity
(renal insufficiency, peripheral vascular disease),
age, gender, and MI within 24 hours. Procedural factors
such as lesion characteristics (length, associated
thrombus, bifurcation), procedural circumstance
(urgent/emergent), and intra-procedural complication
(abrupt vessel closure, significant dissection) (Dorros
et al., 1983).
B. Basic Theory
Based on the literature review, it may be concluded
that:
1. Acute coronary syndrome (ACS) occurs due to the
imbalance between oxygen supply and demand of the
cardiac muscle.
2. Solute concentration of body fluid is measured by
osmolality. A physiological concentration of
solute normally is 285-295 mOsmol/kg.
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3. Based on the formula, the important determinants
of osmolality are sodium, glucose and urea
nitrogen level. formula,i.e. osmolality = 2Na +
(glucose/18) + (BUN/2.8).
4. Major adverse cardiac events (MACEs) are a
clinical end points that occurring either in
hospital or within 7 days of the PCI. MACEs
including death, MI, or urgent target vessel
revascularization.
C. Hypothesis
1. Null hypothesis: An elevation of on admission
osmolality associate with major adverse cardiac
events in Acute Myocardial Infarct patient.
2. Alternative hypothesis: An elevation of on
admission osmolality does not associate with major
adverse cardiac events in Acute Myocardial Infarct
patient.
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D. Theoretical Framework
E. Conceptual Framework
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CHAPTER III
METHODOLOGY
A. Type and Study Design
This study is an observational study. The study
design is a prospective cohort. The study is a part of
the previous study conducted in Department of
Cardiology and Vascular Medicine Faculty of Medicine
Universitas Gadjah Mada (Hartopo et al., 2014).
B. Time and Study Setting
This research was conducted during 2013-2015. The
research was done in Emergency Unit and Intensive
Coronary Care Unit of Dr. Sarjito Hospital, Yogyakarta.
The data analysis was conducted in Yogyakarta, 2015.
C. Study Subjects
The target population of this research is patients
with acute myocardial infarction whom are hospitalized
and treated in the Intensive Coronary Care Unit (ICCU).
The source population for the study is patients with
AMI whom are hospitalized and treated in the ICCU RSUP
Dr. Sarjito, Yogyakarta, Indonesia. The subjects for
the study are the source population whom satisfy the
research criteria.
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D. Sample Size
The subjects are sampled in sequence (consecutive
sampling) since this sampling method is easier to
employ during the research and it is less opportunity
for intentional or unintentional manipulation by the
data analysis process or errors due to confusion.
Inclusion criteria for the subjects are: (1)
patient with AMI, both STEMI and NSTEMI, whom are
diagnosed according to the PERKI criteria and ACC/AHA
guideline, (2) the onset of chest pain is not more than
24 hours before admission, (3) patients by the age of
18-75 years, and (4) patients willing to participate in
the study by confirming an informed consent. Exclusion
criteria are: (1)patients with a history of: chronic
kidney disease stage IV-V, chronic heart failure NYHA
class > II, known valvular heart disease and cirrhosis
hepatic, (2) patients with comorbid factors: sepsis,
diabetic patients treated with insulin, venous
thromboembolism, and decompensated diabetics
(ketoacidosis diabetic or hyperglycemic hyperosmolar
state), (3) patients with malignancy and (4) patients
use loop diuretics in daily basis.
Based on literature review there is no study
before about the relationship between the increasing of
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osmolality with incidence of MACE. This research will
perform sample size calculation using 100 patients to
get the minimum representative sample size.
Sample size needed in this research is calculated
based on the following formula:
Optimum allocation when n is constant for random
sampling stratified by proportion
∑
n1= sample for group 1
N1= total sample from group 1
P1= proportion of n1 from total sample based on osmolality
Q1 = 1-P1
n = total sample for calculating minimal sample size
√
√ √
√
√ √
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From the calculation minimal sample size used for
each group is 69 for group 1 (subject with
normoosmolality and having MACE) and 31 for group 2
(subject with hyperosmolality and having MACE)
E. Study Instrument
Instruments that are used in the research
including (1) case report form to record demographic
data, clinical , laboratory and outcome data, (2)
software for statistical analysis.
F. Measurement and Collection Method
The subjects are sampled consecutively. Subjects
whom meet the criteria are recorded in case report
form. Data that is recorded are demographic data
including age, gender, smoking history, hypertension,
diabetes mellitus, and ischemic heart disease. On
admission clinical data including systolic and
diastolic blood pressure and heart rate. On admission
laboratory data including (1) routine blood test:
hemoglobin, leukocyte, platelet, and erythrocyte, (2)
blood chemical examination: random glucose, urea
nitrogen (BUN) and creatinine, (3) electrolyte
examination: sodium, potassium, and chloride and (4)
cardiac enzyme examination: CK-MB and troponin I
Plasma osmolality value is determined by formula:
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Osmolality= 2Na + BUN/2.8 + GDS/18
Major adverse cardiovascular events are determined
during hospitalization in ICCU. Major adverse
cardiovascular events namely mortality, acute heart
failure, cardiogenic shock, resuscitated lethal
arrhythmia (VT/VF) and reinfarction.
G. Research Variable
Independent variable is plasma osmolality on
admission. Dependent variable is major adverse cardiac
event. Confounding variables (controllable) are
variables in exclusion criteria, these criterias
controlled by excluded from the research. Confounding
variables (uncontrollable) are age, gender, diabetes,
dyslipidemia, and onset of chest pain.
H. Operational Definition
1. Plasma osmolality
Plasma osmolality is concentration measure of all
chemical particles found in the fluid part of blood
(Keane & O’Toole, 2013). Plasma osmolality is measured
by formula Osmolality= 2Na + BUN/2.8 + GDS/18
2. Major Adverse Cardiac Event (MACE)
MACE is a composite of mortality, acute heart failure,
cardiogenic shock, resuscitated lethal arrhythmia
(VT/VF) and reinfarction (Hartopo, 2013).
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3. Acute myocardial infarction
There is evidence of myocardial necrosis with clinical
presentation, electrocardiographic findings, and serum
biomarker of myocardial damage. ECG in patients with
STEMI shows ≥ 1 mm elevation in more than 2 lead
II,III,aVF and I – aVL, ≥ 2 mm in V1-V6 and/or new
left bundle branch block (LBBB). Cardiac marker
troponin I, myoglobin, and CK-MB also increases in
patients with STEMI aND will be no ST-segment elevation
in NSTEMI (Lilly, 2011)
4. Onset of angina
Time from the patient begin to feel the symptom of
angina until patient presentation in emergency room.
5. Blood pressure
Blood pressure is the pressure of the blood within the
arteries. It is produced primarily by the contraction
of the heart muscle. Its measurement is recorded by
two numbers, systolic and diastolic. Blood pressure was
recorded twice during early presentation, by adult-size
cuff sphygmomanometer during supine position.
6. Chronic kidney disease
Chronic kidney disease is a progressive loss of renal
function for more than 3 months that shows symptoms of
uremia when GFR is reduce about 10-15% from normal.
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Chronic kidney disease is determined as elevated
creatinine and BUN levels more than 3 months (Schiffrin
et al., 2007).
7. Congestive heart failure
Heart failure is inability or failure of the heart to
adequately meet the needs of organ and tissue for
oxygen on nutrients. The congestive sign and symptom at
daily activity was recorded. Sign of congestion is
divided based on which side of the heart involved. Sign
of left-sided congestion are diaphoresis, tachycardia,
tachypnea, pulmonary rales, loud P2, S3 gallop, and S4
gallop. Symptoms of left-sided congestion are dyspnea,
orthopnea, paroxysmal nocturnal dyspnea, and fatigue.
Sign of right-sided congestion are jugular venous
distention, hepatomegaly, and peripheral edema.
Symptoms of right-sided congestion are peripheral edema
and right upper quadrant discomfort due to hepatic
enlargement (Lilly, 2011).
8. Diabetes mellitus (DM)
Diabetes mellitus compromises a group of metabolic
disorders that share the common phenotype of
hyperglycemia. DM is classified into DM type 1 and type
2. Diagnostic criteria of DM include of fasting plasma
glucose ≥ 7.0 mmol/L (≥126 mg/dL), symptom of diabetes
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Malignancy refers to cancerous cell that have the
ability to spread to other sites in the body
(metastasize) or to invade nearby (locally) and destroy
tissues. Malignant cells tend to have fast,
uncontrolled growth and do not die normally due to
changes in their genetic makeup (Kumar et al., 2015).
I. Statistical Analysis
Descriptive statistics are performed in baseline
variables and stratified by cut off of osmolality.
Discrete characteristics are expressed as frequency
counts as percentages, differences between groups were
determined with the chi-squared test. Continuous
characteristics are expressed as mean and standard
deviation or median and quartiles, the differences in
those variables were examined with the Kruskal-Wallis.
Parametric test used in this research is logistic
regression due to both of the variables are nominal
data. Univariate analysis between osmolality and MACE
and multivariate analysis of variables with p-value
under 0.05 were done using binomial regression.
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J. Ethical Consideration
Ethical clearance for the research has been obtained
from Medical and Health Research Committee Faculty of
Medicine Universitas Gadjah Mada for the current
research.
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CHAPTER IV
A. RESULT AND DISCUSSION
In this research total subjects used are 167 males
and 37 females. The main data needed from the raw data
is osmolality data (blood sugar, sodium, and BUN) and
MACE. The MACEs are observed over a period of time
since the first time patient admitted in ICCU until
patient discharged. This research only has one
population without control population, because the
researcher only aims to observing whether there is an
association between the increase values of on admission
osmolality with the increase of MACE occurrence.
Data were collected for approximately 2 years from
a span of years 2013 to 2015. With total 201 subjects,
there are 2 groups of subjects normoosmolal indicating
the value of osmolality under 295.0 mOsmol with total
137 subjects and hyperosmolal indicating the values of
osmolality over 295.0 mOsmol with total 64 subjects.
We used logistic regression binary logistic type
to analyze the association between osmolality and MACE.
The logistic regression was used because both
independent variable (MACE) represent as binominal
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data. The result of the analysis is the coefficient of
constant contribution give very high significance in
predicting MACE occurrence (Wald 78.64, p < 0.001).
After analyzing variables that include in the equation
the next step is analyzing variable that not included
in the equation. The analysis showed the score is 6.174
with significance 0.013. The result shown in
significance row is 0.015 which less than 0.05, this
result consider that the predictor (osmolality) is very
significant in predicting MACE occurrence. After
consider the predictor give a significant effect in
predicting MACE occurrence, there is one last test to
determine whether there is a significant difference if
the predictor is removed from the model. The test is
carried out using Model if Term Removed table and shows
the result 0.016 which is significant.
Table 1. Binary logistic analysis of Osmolality based
on MACE
Variable Odd
Ratio
95% CI p-value
Lower Upper
Osmolality 2.69 1.21 5.99 0.015
From the logistic regression analysis it can be
summarize that the fitness between data and the model
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is good. The final result concluded that the H0
accepted, which means that with the increase of
osmolality value give impact to the increasing of MACE
occurrence.
The baseline characteristics of the group with
normoosmolal and hyperosmolal are depicted in Table 1.
This table showing how significant the influence of
each variable to osmolality in predicting MACE, to
discover whether there is any other variable that may
confound the result of the correlation between
osmolality and MACE.
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Table 2 . Characteristics of patients after grouping
based on the osmolality level
Characteristics Normoosmolal
N=137
Hyperosmolal
N=64
P-
Value
DemographicAge, year (mean±SD) 56.2±8.9 57.9±8.7 0.99
Onset, hour (mean±SD) 9.1±6.8 8.6±6.5 0.99
Sex (n (%)) Male 117(85.4) 47(73.4) 0.04
Diabetes (n (%)) 12.4 50.0 <0.001
Hypertension (n (%)) 61.3 50.0 0.13
IHD /stable angina (n
(%)) 10.2 14.1 0.43
Systolic Pressure
(mean±SD)
130.6±23.5 129.1±23.6 0.99
Diastolic Pressure
(mean±SD)
80.8±14.6 78.5±13.4 0.99
Current smoking (n (%)) 54.7 40.6 0.06
Dyslipidemia (n (%)) 7.3 17.2 0.03
Family History (n (%)) 2.2 0.0 0.23
Diagnosis (n (%)) STEMI NSTEACS
77.4
22.6
82.8
17.2
0.06
STEMI (n (%)) 37.1 13.2 0.06
Primary PCI (n (%)) 25.5 29.7 0.54
Intervention and Medication
Thrombolysis (n (%)) 34.3 34.4 0.99Heparin (n (%))
LMWH UFH Fondaparinux No Heparin
7.3
78.1
10.9
3.6
7.8
84.8
4.7
3.1
0.56
Nitrate (n (%)) 99.3 100.0 0.49
Aspilet (n (%)) 100.0 100.0 N.A.
Clopidogrel (n (%)) 100.0 100.0 N.A.
Statin (n (%)) 100.0 100.0 N.A.
ACE Inhibitor (n (%)) 83.9 76.6 0.21
Beta Blocker (n (%)) 53.3 50.0 0.66
Furosemide IV (n (%)) 0.0 1.6 0.14
Vasopressor (n (%)) 0.0 1.6 0.14Inotropic (n (%)) 0.0 1.6 0.27
Heart Rate (mean±SD) 75.5±14.6 77.3±19.1 1.00
Routine Blood Test
Leucocyte (mean±SD) 12.9±3.7 12.1±3.5 0.99
Thrombocyte (mean±SD) 257.0±63.2 241.9±46.9 0.99
Creatinine (mean±SD) 1.2±0.4 1.4±0.7 1.00
Haemoglobin (mean±SD) 14.2±1.7 13.7±1.9 0.99
BUN ((mean±SD) 13.4±5.5 18.5±8.6 0.99
Electrolyte
Sodium (mean±SD) 137.3±2.9 139.9±3.5 0.99
Pottasium (mean±SD) 3.9±0.5 4.0±0.6 0.99
Chloride (mean±SD) 109.9±85.6 104.5±3.8 0.99
Glucose (mean±SD) 151.5±58.9 250.6±132.1 0.99
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Comparison through all groups was performed with the Chi-
squared test for the discrete data or the Kruskal-Wallis test for
the continuous characteristics.
ACE, angiotensin-converting enzyme;BUN;CABG, coronary artery
bypass grafting, ischemic heart disease;LMWH, low molecular weight
heparin; NSTEACS, non-segment T elevation acute coronary syndrome;PCI, percutaneous coronary intervention; STEMI, segment T
elevation myocardial infarction;UFH, unfractionated heparin.
After got the result from logistic regression test with
significant result, the researcher continue analysis
using multinominal regression because from the data
comparison in table 1, several variables show
significant difference between patients with MACE and
without MACE.
The incidence of MACE that happen in hospital is
declining over the past decade. This decrement can be
associate with many factors such as the improvement of
technology, pharmacology, and operator experience (Kern
et al., 2006). Table 2 shows the comparison of
characteristics between patients with MACE and patients
without MACE. Patients with age 60 years old (SD ±
8.2), onset of attack 8.5 hours (SD ± 5.9), haemoglobin
14.1 mg/dl (SD ± 1.8), and BUN 20.6 mmol/L (SD ± 10.4)
prone to have in hospital MACE.
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Figure 1. Incidence of MACE in the research
14
123
15
49
MACE Non-MACE
Normoosmolal Hyperosmolal
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Table 3 . Comparison of characteristics between
patients with MACE and patients without MACE
Characteristics With MACE
N= 29
Without MACE
N= 172
P-
Value
Age, year (mean±SD) 60.8±8.2 56.1±8.8 0.01
Onset, hour (mean±SD) 8.5±5.9 8.9±6.8 0.02
Sex (n (%))
Male 24(11.9) 140(69.7)
0.86
Diabetes (n (%)) 5.0 19.4 0.18
Hypertension (n (%)) 9.0 48.8 0.61
IHD /stable angina (n
(%))
1.0 10.4 0.41
Current smoking (n (%)) 6.5 43.8 0.53
Dyslipidemia (n (%)) 1.5 9.0 0.99
Family History (n (%)) 0.0 1.5 0.48Diagnosis (n (%))
STEMI
NSTEACS
12.9
1.5
66.2
19.4
0.97
Primary PCI (n (%)) 5.5 21.4 0.15
STEMI (n (%)) 8.2 42.1 0.98
Thrombolysis (n (%)) 6.0 28.4 0.39
Heparin (n (%))
LMWH
UFH
Fondaparinux
No Heparin
1.0
10.9
2.0
0.5
6.5
69.2
7.0
3.0
0.81
Nitrate (n (%)) 14.4 85.1 0.86Aspilet (n (%)) 14.4 85.6 <0.001
Clopidogrel (n (%)) 14.4 85.6 <0.001
Statin (n (%)) 14.4 85.6 <0.001
ACE Inhibitor (n (%)) 10.9 70.6 0.39
Beta Blocker (n (%)) 6.5 45.8 0.39
Furosemide IV (n (%)) 0.5 0.0 0.15
Vasopressor (n (%)) 0.5 0.0 0.14
Inotropic (n (%)) 0.0 0.5 0.27
Haemoglobin (mean±SD) 14.1±1.8 14.1±1.8 0.04
Systolic Pressure
(mean±SD)
124.5±24.7 131.0±23.2 0.52
Diastolic Pressure
(mean±SD)
78.2±15.0 80.4±14.2 0.98
Heart Rate (mean±SD) 82.1±21.4 70.1±14.8 0.20
Leucocyte (mean±SD) 13.4±3.9 12.6±3.5 0.09
Thrombocyte (mean±SD) 238.1±52.5 254.6±59.7 0.15
Creatinine (mean±SD) 1.6±1.0 1.2±0.4 0.55
BUN ((mean±SD) 20.6±10.4 14.1±5.8 0.02
Sodium (mean±SD) 137.6±4.0 138.2±3.2 0.34
Pottasium (mean±SD) 4.2±0.8 3.9±0.5 0.70
Chloride (mean±SD) 103.2±4.2 109.1±76.4 0.97
Glucose (mean±SD) 238.5±139.2 173.7±88.9 0.41
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From comparison table above there are some
variables have significance under 0.05 such as age,
onset, haemoglobin, and BUN. The researcher continues
to analyse those variables since they may have
influence in the occurrence of MACE.
Table 4. Binary logistic analysis of Age based on MACE
Variable OddRatio 95% CI p-value Lower Upper
Age 1.06 1.02 1.11 0.009
Table 5. Binary logistic analysis of Onset based on
MACE
Variable Odd
Ratio
95% CI p-value
Lower Upper
Onset 0.99 0.93 1.05 0.732
Table 6. Binary logistic analysis of Hemoglobin based
on MACE
Variable Odd
Ratio
95% CI p-value
Lower Upper
Hemoglobin 1.01 0.81 1.26 0.91
Table 7. Binary logistic of BUN based on mACE
Variable Odd
Ratio
95% CI p-value
Lower Upper
BUN 1.11 1.06 1.17 0.00
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Table 8. Binary logistic analysis of variables with p -
value less than 0.05
Variable Odd
Ratio
95% CI p-value
Lower Upper
BUN 1.13 1.06 1.19 0.00
Age 1.07 1.01 1.13 0.01
Onset 0.95 0.88 1.03 0.19
Osmolality 2.69 1.21 5.99 0.01
Hemoglobin 1.25 0.98 1.61 0.07
The main finding of this logistic regression
analysis is the strong association between increasing
value of on admission osmolality (hyperosmolality) and
the increasing of MACE occurrence. On admission was
independently and highly predictive of MACE occurrence
after adjusting inclusion and exclusion criteria. A
study about correlation between on admission osmolality
with all-cause of death in ACS patient has already been
done with the result of strong association between
admission osmolality and all-cause death in ACS patient
undergoing PCI (Rohla et al., 2014).
But there are also some variables that show
influence to MACE, such as sex and history of
dyslipidemia. There is a research analyzing about
cardiovascular outcomes in patients with type 2
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diabetes mellitus after having first acute myocardial
infarction attack, one of the independent risk factor
is dyslipidemia. Patient with dyslipidemia risk factor
having higher risk suffered at least one myocardial
infarction (Pastewka et al., 2003). Another research
discussing about the relation between metabolic
syndrome; this syndrome consist of diabetes mellitus/
glucose intolerance, arterial hypertension, central
obesity, dyslipidemia, and microalbuminuria; gender,
and the size of infarction stated that the female
patient with metabolic syndrome tend to have larger
size of infarction that can lead to higher risk of
having MACE (Kranjcec & Altabas, 2012). From this
research all at once explaining that dyslipidemia and
sex also have influence to MACE occurrence.
There are also some variable that may influence in
determining MACE result as stated in table 4. Age,
onset, haemoglobin, and BUN have significance under
0.05. After analysed using binomial regression the only
variable having significance under 0.05 is BUN. There
are some studies discussing about MACE related with
variables above.
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Age and onset are already established as clinical
factors that can increase the incidence of MACE. Beyond
being a risk factor for CAD and universal comorbidity
for cardiac related disease, age is an important
predictor of outcome following PCI and CABG. Patient
with age more than 80 years old were 2 folds higher in
having 3-vessels CAD compared with younger patients (<
65 years)(38% versus 20%)(Kern et al., 2006). Since
inclusion criteria for this study is patients with age
range from 18-75 years old, age is not consider as
independent predictor based on analysis result from
Table 4 ( p = 0.13). Onset as another risk factors of in
hospital MACE, the average time from onset until
patients admitted to the emergency unit is 8.5 hours
(SD ± 5.9). This interval is way too long in this
study, the long the patient delayed will increase
patient’s mortality and risk of MACE (Sadrnia et al.,
2013). From this research the onset of MI attack not
related with the occurrence of MACE ( p = 0.18).
The last variable that may influence MACE
occurrence is blood urea nitrogen or BUN. BUN is one of
the components of osmolality. Osmolality itself already
proven become an independent predictor of MACE. There
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is also a study discussing about BUN independently
predicting survival rate in chronic heart failure
patient. Patients with high serum BUN develop
hypotension that needs intervention (Klein et al.,
2008). Another research in Japan also found that BUN of
>25 mg/dl was associated with long-term mortality
predictor in ACS or stable CAD patients that undergoing
PCI (Kawabe et al., 2014). .
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CHAPTER V
CONCLUSION AND SUGGESTION
A. Conclusion
There is strong and independent association
between increasing value of on admission osmolality and
the increasing of MACE occurrence.
B. Suggestion
1. Further test with adding more subjects is
recommended to increase the accuracy in predicting
the outcome.
2. Analysis in correlation from each aspect of
osmolality to MACE is needed to find if there is
independent predictor among blood glucose, sodium,
and BUN.
3. A multi variation analysis for predictor is
recommended to know if there is any other factor
influencing the occurrence of MACE.
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Amann, K, Wanner, C, Ritz, E 2006, ‘Cross-talk Between
the Kidney and the Cardiovascular System’, American
journal of kidney diseases : the official journal
of the National Kidney Foundation, vol. 17, pp.
2112 – 2119.
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APPENDIX
Table 9
MACE * Osmolable Cross tabulation
Osmolable
TotalHyper Normo
MACE No Count 21 62 83
Expected Count 24,1 58,9 83,0
% within MACE 25,3% 74,7% 100,0%
% within Osmolable 72,4% 87,3% 83,0%
% of Total 21,0% 62,0% 83,0%
Std. Residual -,6 ,4
Yes Count 8 9 17
Expected Count 4,9 12,1 17,0
% within MACE 47,1% 52,9% 100,0%
% within Osmolable 27,6% 12,7% 17,0%
% of Total 8,0% 9,0% 17,0%
Std. Residual 1,4 -,9
Total Count 29 71 100
Expected Count 29,0 71,0 100,0
% within MACE 29,0% 71,0% 100,0%
% within Osmolable 100,0% 100,0% 100,0%
% of Total 29,0% 71,0% 100,0%
Table 10
Tests of Normality
osmol
Kolmogorov-Smirnova Shapiro-Wilk
Statistic Df Sig. Statistic df Sig.
MACE 1 ,529 137 ,000 ,346 137 ,000
2 ,474 64 ,000 ,525 64 ,000
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 201 100,0
Missing Cases 0 ,0
Total 201 100,0
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Classification Tablea,b
Observed
Predicted
MACE
Percentage
Correct
Not
experiencing
MACE
Experiencing
MACE
Step 0 MACE Not experiencing MACE 172 0 100,0
Experiencing MACE 29 0 ,0
Overall Percentage 85,6
a. Constant is included in the model.
b. The cut value is ,500
Table 15
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -1,780 ,201 78,644 1 ,000 ,169
Table 16
Variables not in the Equation
Score df Sig.
Step 0 Variables osmol(1) 6,174 1 ,013
Overall Statistics 6,174 1 ,013
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Table 17
Iteration Historya,b,c,d
Iteration -2LL
Coefficients
Constant osmol(1)
Step 1 1 165,218 -1,063 -,529
2 160,224 -1,180 -,888
3 160,081 -1,184 -,985
4 160,081 -1,184 -,989
5 160,081 -1,184 -,989
a. Method: Forward Stepwise (Likelihood Ratio)b. Constant is included in the model.
c. Initial -2LL: 165,887
d. Estimation terminated at iteration number 5 because
parameter estimates changed by less than ,001.
Table 18
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 5,806 1 ,016
Block 5,806 1 ,016
Model 5,806 1 ,016
Table 19
Model Summary
Step -2 Log likelihood
Cox & Snell R
Square
Nagelkerke R
Square
1 160,081a ,028 ,051
a. Estimation terminated at iteration number 5 because
parameter estimates changed by less than ,001.
Table 20
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 ,000 0 .
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Table 21
Classification Tablea
Observed
Predicted
MACE
Percentage
Correct
Not
experiencing
MACE
Experiencing
MACE
Step 1 MACE Not experiencing
MACE
172 0 100,0
Experiencing MACE 29 0 ,0
Overall Percentage 85,6
a. The cut value is .500
Table 22
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
osmolal ,989 ,408 5,874 1 ,015 2,690 1,208 5,986
Constan
t-2,173 ,282 59,358 1 ,000 ,114
a. Variable(s) entered on step 1: osmolal.
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Table 23
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
Umur ,002 ,020 ,008 1 ,928 1,002 ,963 1,042
Onset -,036 ,027 1,798 1 ,180 ,964 ,915 1,017
HB -,080 ,098 ,666 1 ,415 ,923 ,762 1,119
BUN ,108 ,030 13,272 1 ,000 1,114 1,051 1,181
MACE(1
)-,442 ,466 ,901 1 ,343 ,643 ,258 1,601
Constan
t-,720 2,218 ,105 1 ,745 ,487
a. Variable(s) entered on step 1: Umur, Onset, HB, BUN, MACE.
Table 24
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
BUN ,102 ,026 15,464 1 ,000 1,108 1,053 1,166
Consta
nt-3,478 ,504 47,598 1 ,000 ,031
a. Variable(s) entered on step 1: BUN.
Table 25
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
HB ,013 ,112 ,014 1 ,905 1,013 ,814 1,262
Constan
t-1,968 1,589 1,533 1 ,216 ,140
a. Variable(s) entered on step 1: HB.
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Table 26
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
Onset -,011 ,031 ,117 1 ,732 ,989 ,931 1,052
Constan
t-1,687 ,334 25,431 1 ,000 ,185
a. Variable(s) entered on step 1: Onset.
Table 27
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
Umur ,062 ,024 6,730 1 ,009 1,064 1,015 1,114
Constan
t-5,386 1,442 13,950 1 ,000 ,005
a. Variable(s) entered on step 1: Umur.
Table 28
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
95% C.I.for
EXP(B)
Lower Upper
Step
1a
BUN ,121 ,030 16,127 1 ,000 1,129 1,064 1,198
Umur ,068 ,027 6,159 1 ,013 1,070 1,014 1,128
Onset -,051 ,039 1,724 1 ,189 ,950 ,881 1,025
HB 227 128 3 133 1 077 1 255 976 1 613