Skripsi ACINTYA 15329 Final

8/17/2019 Skripsi ACINTYA 15329 Final

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i

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



ii

APPROVAL PAGE



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



iii

AUTHENTICITY STATEMENT



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



iv

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



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



xi



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



1

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



4

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



7

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).



10

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



12

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



15

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)





17

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.



18

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.



19

D. Theoretical Framework

E. Conceptual Framework



20

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.



21

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



22

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

√

√ √

√

√ √



23

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:



24

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).



25

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.



26

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







29

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.



30

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.



31

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



32

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



33

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.



34

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



35

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.



36

Figure 1. Incidence of MACE in the research

14

123

15

49

MACE Non-MACE

Normoosmolal Hyperosmolal



37

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



38

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



39

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



40

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.



41

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



42

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). .



43

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.



44

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53

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


% within MACE 47,1% 52,9% 100,0%


% of Total 8,0% 9,0% 17,0%

Std. Residual 1,4 -,9

Total Count 29 71 100


% within MACE 29,0% 71,0% 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





55

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



56

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 .



57

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



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.



58

Table 23



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



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



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.



Table 26



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



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



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

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