harjit thesis final 240516 - research-repository.uwa.edu.au€¦ · statement of presentation this...

1

Title

Decline in death rates for coronary heart disease and severity of acute myocardial

infarction in patients hospitalised with acute myocardial infarction: a Western

Australian linked data study.

Candidate

Name: Dr Harjit Kaur

Student Number: 10327322

Qualifications: MBBS

Statement of Presentation

This thesis is presented for the degree of Master of Public Health by Research of The

University of Western Australia.

School

School of Population Health

Year of Submission

2015

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Summary

Background: Coronary heart disease (CHD) is a major public health concern due to its impact on

the health care system and the community. Since the late 1960s there has been a decline in age

standardised rates of CHD mortality. It is postulated that an improvement in trends of the

markers of severity of acute myocardial infarction (AMI) may have contributed to this decline.

However, there is little research on the changes in trends in AMI severity, especially in Australia,

to establish or negate a causal link.

Methods: The primary aim was to examine changes in indicators of AMI severity and its

contribution to the decline in coronary heart disease mortality for patients admitted to hospital

for incident AMI from 1984 to 2003, aged 35‐64 years. Data were obtained from historical case

report forms from previous Western Australian research including the state‐wide Hospital

Morbidity Data Collection, a core dataset of the Western Australian Data Linkage System.

Results: There was an increase in the Charlson comorbidity severity, renal failure, mean PREDICT

final score, odds of having a positive CK‐MB and CK results and ST elevation (women only). There

was a decrease in ECG severity and in the odds of having an abnormal SBP. The components of

shock and clinical history had opposing changes in severity for men only, with a decrease in

shock and an increase in clinical history.

Conclusion: The decline in CHD mortality was not shown to be due to a decreasing trend in AMI

severity. Hence the need to continue Public Health measures to control AMI severity.

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Acknowledgements

I wish to thank my family and friends for providing me with a supportive environment and

helping me complete this thesis. I would also like to extend my appreciation and thanks to my

research supervisors (Associate Professors Tom Briffa and Alexandra Bremner, and Dr Frank

Sanfilippo) for the time and effort that they have provided me in completing this challenging

task. Their direction, encouragement and motivation throughout the years have added a new

dimension to my skills in research which has enhanced my own human capital. Furthermore, I

would also like to thank the Western Australian Data Linkage Branch for making the data

available and the original MONICA researchers for access to their data.

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Declaration by Student

In submitting this thesis for the degree of Master of Public Health by Research of The

University of Western Australia the author provides an acknowledgement of assistance from the

individuals listed previously and from the authors of sources acknowledged in the text, however

states that this thesis is entirely her own work.

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Table of Contents

Summary ....................................................................................................................................... 2

Acknowledgements ...................................................................................................................... 3

Declaration by Student ................................................................................................................ 4

Table of Contents ................................................................................................................. 5

List of Tables ................................................................................................................................. 7

List of Figures ................................................................................................................................ 9

List of Abbreviations .................................................................................................................. 10

Chapter One: Introduction ......................................................................................................... 12

1.0 Background ........................................................................................................................ 12

1.1 Thesis Aims ........................................................................................................................ 16

1.2 Significance of thesis ......................................................................................................... 16

1.3 Overview of thesis ............................................................................................................. 17

Chapter Two: Review of Research Issues .................................................................................. 18

2.1 Diagnosis of an AMI .......................................................................................................... 18

2.2 Definitions for AMI Cases .................................................................................................. 21

2.3 The Need to Measure the Severity of AMI versus Absolute Cases of AMI ..................... 21

2.4 Indicators of AMI Severity ................................................................................................ 22

2.5 Prior Research and Limitations ......................................................................................... 22

2.6 PREDICT Tool ..................................................................................................................... 25

2.7 Implications for Practice ................................................................................................... 25

2.7.1 Importance to Public Health ...................................................................................... 25

2.7.2 Potential for Public Health Strategy ........................................................................... 27

Chapter Three: Methodology..................................................................................................... 28

3.1 Study Design ...................................................................................................................... 28

3.2 Study Population ............................................................................................................... 28

3.3 Observation Period ........................................................................................................... 28

3.4 Data Collection .................................................................................................................. 29

3.5 Data Analysis ..................................................................................................................... 33

3.6 Ethics .................................................................................................................................. 34

Chapter Four: Results ................................................................................................................. 35

4.1 Trends in AMI Severity from 1984 to 2003 for Patients Aged 35‐64 years

Hospitalised in Perth with Incident Definite AMI .................................................................. 35

4.1.1 Subject Characteristics ............................................................................................... 35

4.1.2 PREDICT Components ................................................................................................. 36

4.1.3 PREDICT Final Score ................................................................................................... 40

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4.1.4 Other Markers of AMI Severity .................................................................................. 42

4.2 Trends in AMI Severity from 1984 to 2003 for Patients Aged 35‐64 Years

Hospitalised in Perth with Incident or Recurrent Definite AMI ............................................ 44




4.2.5 PREDICT components and other markers of AMI severity ......................................... 53

4.3 Trends in AMI Severity between the two years, 1998 and 2003, for Patients

Aged 35‐79 years Hospitalised in Perth with Incident or Recurrent Definite AMI ............... 54





4.3.5 PREDICT components and other markers of AMI severity ......................................... 62

Chapter Five: Discussion ............................................................................................................ 64

5.1 Introduction ....................................................................................................................... 64

5.2 Research Findings and Significance of Thesis ................................................................... 64

5.2.1 PREDICT Components and Final Score ....................................................................... 64


5.2.3 Relevance of research findings to published literature .............................................. 65

5.3 Strengths and limitations ................................................................................................... 67

5.4 Implications for practice .................................................................................................... 68

5.5 Suggestions for future research ......................................................................................... 69

Chapter Six: Conclusion .............................................................................................................. 70

References .................................................................................................................................. 71

Appendix 1 .................................................................................................................................. 78

Appendix 2 .................................................................................................................................. 79

Appendix 3 .................................................................................................................................. 80

Appendix 4 .................................................................................................................................. 82

Appendix 5 .................................................................................................................................. 83

Appendix 6 .................................................................................................................................. 84

Appendix 7 .................................................................................................................................. 85

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List of Tables

Table 4.1 Patient characteristics of history of diabetes, heart failure, stroke, hypertension,

CABG and use of digoxin in the four years prior to their date of diagnosis with incident

definite AMI ................................................................................................................................ 36

Table 4.2 PREDICT component points, binary coding and frequency distribution from

1984 to 2003 ............................................................................................................................... 37

Table 4.3 Results from logistic regression analysis of PREDICT components for cohort 1 as

binary variables from 1984 to 2003 ............................................................................................ 38

Table 4.4 Results from ordinal regression analysis for PREDICT components with more

than 2 categories from 1984 to 2003 ......................................................................................... 39

Table 4.5 Frequency distribution of PREDICT Final Scores aggregated over the study period

from 1984 to 2003 in patients aged 35‐64 years with incident definite AMI ............................. 40

Table 4.6 Coefficients (β) and 95% CIs from linear regression analysis of the PREDICT

final score 1984‐2003 ................................................................................................................. 42

Table 4.7 Categories of other markers of AMI severity and frequency distribution

1984‐2003 in patients aged 35‐64 years with incident definite AMI ......................................... 43

Table 4.8 Results from logistic regression analysis: Changes in ORs of other markers of AMI

severity associated with a 1 year increase in time ..................................................................... 44

Table 4.9 Patient characteristics for history of diabetes, heart failure, stroke, hypertension,

coronary artery bypass graft surgery and use of digoxin in the four years prior to their date

of diagnosis with incident or recurrent definite AMI ................................................................. 45

Table 4.10 PREDICT component points, binary coding and frequency distribution from

1984 to 2003 for incident or recurrent definite AMI .................................................................. 46

Table 4.11 Crude and age‐adjusted odds ratios from logistic regression analysis separately in

men and women for the PREDICT components of Charlson score, shock, clinical history, age,

ECG severity, renal and heart failure .......................................................................................... 47

Table 4.12 Crude and age‐adjusted odds ratios from ordinal regression for variables with

more than two PREDICT categories. ........................................................................................... 48

Table 4.13 Frequency distribution of PREDICT Final Scores aggregated over the study

period from 1984 to 2003 for patients with incident or recurrent definite AMI ....................... 49

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Table 4.14 Coefficients (β) and 95% CIs from linear regression analyses of the PREDICT final

score in patients with incident or recurrent definite AMI from 1984 to 2003 ........................... 51

Table 4.15 Categories of other markers of AMI severity and frequency distribution from

1984 to 2003 in patients aged 35‐64 with incident or recurrent definite AMI .......................... 52

Table 4.16 Results from logistic regression analysis of other indicators of AMI severity in

patients with incident or recurrent definite AMI, 1984‐2003 .................................................... 53

Table 4.17 P‐values for analysis of trends of markers of AMI severity with respect

to incident and recurrent case types .......................................................................................... 55

Table 4.18 Subject characteristics of history of diabetes, heart failure, stroke and hypertension

in the four years prior to their date of diagnosis with incident or recurrent definite AMI ........ 55

Table 4.19 PREDICT component points for 1998 and 2003, including their binary coding and

frequency distribution .. ............................................................................................................. 55


men and women for the PREDICT components in 1998 and 2003 for patients with incident or

recurrent definite AMI ................................................................................................................ 55

Table 4.21 Frequency distribution of PREDICT Final Scores aggregated for years

1998 and 2003 ............................................................................................................................ 59

Table 4.22 Coefficients (β) and 95% CIs from linear regression analysis of the PREDICT final

score in 1998 and 2003 for patients with incident or recurrent definite AMI … ........................ 60

Table 4.23 Categories of other markers of AMI severity and frequency distribution for the years 1998 and 2003 ………………………………………………………………………………………………………...... 61


men and women for other indicators of AMI severity in 1998 and 2003 for patients with

incident or recurrent definite AMI .............................................................................................. 62

Table 4.25 P‐values for analysis of trends of markers of AMI severity with respect to

younger (age 35 to 64 years) and older (age 65‐79 years) age groups ...................................... 63

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List of Figures

Figure 1: Graph of the mean PREDICT Final Score for males and females with incident

definite AMI over the period from 1984 to 2003 ....................................................................... 41

Figure 2: Graph of the mean PREDICT Final Score for males and females with incident

and recurrent definite AMI over the period from 1984 to 2003 ................................................ 50

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List of Abbreviations

ACS Acute Coronary Syndrome

AHA American Heart Association

AMI Acute Myocardial Infarction

BBB Bundle Branch Block

CABG Coronary Artery Bypass Graft

CCI Charlson Comorbidity Index

CHD Coronary Heart Disease

CI Confidence Interval

CK Creatinine Kinase

CK‐MB Creatinine Kinase Muscle Brain

CK‐MBm Creatinine Kinase Muscle Brain mass

cTn Cardiac Troponin

CVD Cardiovascular Disease

dL Decilitre

ECG Electrocardiogram

HMDC Hospital Morbidity Data Collection

IBM International Business Machines

ICD International Classification of Diseases

ICD‐10‐AM International Classification of Diseases, Tenth Revision, Australian Modification

IHD Ischaemic Heart Disease

mg Milligrams

MOCHA Monitoring CHD in the modern era

MONICA MONItoring of trends and determinants in Cardiovascular Disease

NHMRC National Health and Medical Research Council

OR Odds Ratio

PCI Percutaneous Coronary Intervention

SBP Systolic Blood Pressure

SD Standard Deviation

SPH School of Population Health

SPIM Seattle Post Myocardial Infarction Model

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UWA University of Western Australia

WADLS WA Data Linkage System

WHO World Health Organisation

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Chapter One: Introduction

1.0 Background

Cardiovascular disease (CVD) comprises all diseases and conditions of the heart and blood

vessels. [1] Coronary heart disease (CHD) is the most common form of CVD. There are two major

clinical forms of CHD: heart attack and angina. The chief cause of most CVD is atherosclerosis,

which is a build‐up of fat and other substances that forms plaque inside the artery wall.

Atherosclerosis is a problem when it leads to reduced or obstructed blood supply to the brain,

which can manifest as stroke, or to the heart, which can manifest as angina or an acute

myocardial infarction (AMI), i.e. a heart attack. [2]

CHD is a broad term that describes inadequate perfusion of the heart as a result of narrowing of

the coronary arteries. Atherosclerosis results in the gradual accumulation of lipids, calcium and

macrophages in the inner wall of the blood vessels (coronary arteries) that supply the heart

muscle with blood. These fatty deposits, or plaques, gradually reduce the lumen of arteries and

reduce the flow of blood to the heart. Inflammation is one of the main aspects of

atherosclerosis[3], resulting in fatty streak formation to plaque rupture, subsequent thrombosis,

and progressive mechanical and dynamic obstruction. Rupture of the arterial plaque's fibrous

cap exposes tissue factors present in the necrotic core, and triggers inflammatory signalling, cell

adhesion, and the coagulation cascade that eventually leads to a thrombus.[4] Cytokines and

adhesion molecules are the main components of these events that contribute to the

development of an atherosclerotic plaque.

A heart attack occurs when a coronary plaque breaks through the vessel wall. This causes a blood

clot to form that partially or completely blocks blood flow to the part of the heart muscle

downstream from the occlusion. This is a life‐threatening emergency that can cause severe chest

pain, radiating to the neck, jaw and left arm, and often collapse and sudden death. Clots that

cannot be treated or unblocked quickly result in damage and death of part of the heart muscle,

and this is known as an AMI. AMI is considered the most severe manifestation of CHD, as it can

result in a fatal outcome.[5, 6] AMI is a major cause of death, disability and hospital admissions

worldwide and affected around 1.4 million Australians in 2011.[7, 8] CHD risk factors such as

hypertension, cigarette smoking, diabetes mellitus or increased glucose level, increased

cholesterol levels, physical inactivity and obesity are all major contributors of cardiovascular

death globally.[9] In 2011, CHD was the cause of death in over 21,500 Australians (14% of all

deaths) and is still a leading cause of mortality in Australia.[10‐13]

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One way to increase our understanding, and therefore control, of CHD is to monitor CHD

prevalence. However, it is difficult to monitor the true population prevalence of CHD, so death

and hospitalisation for CHD are accepted surrogate measures to monitor population trends in

CHD.[14] There has been a decline in CHD mortality in Australia since the 1960s.[15, 16] The

greatest contribution to this decline in Perth has been to the fall in out‐of‐hospital deaths, but

in‐hospital death rates and non‐fatal MI age‐standardised admission rates have also decreased.

[17]

From 1996 to 2006, national age‐standardised CHD death rates fell by 45% in males and 44% in

females; i.e. to 133 and 77 per 100,000 respectively.[18] In Western Australia (WA), crude

mortality rates from CHD has fallen in the Perth Statistical Division since 1968.[18,19] In Europe,

CHD mortality in men declined from 139 per 100,000 in 1985‐1989 to 93 per 100,000 in 2000‐

2004, a fall of 33% in age‐standardised rates. [20] In women, the fall was 27% from 61 per

100,000 to 44 per 100,000. CHD mortality continues to decline in most developed countries.[20]

Hospital admission rates for AMI in the Perth Statistical Division have tracked the fall in CHD

mortality locally.[21]

Using linked data for medical research

Data linkage is the process of linking records for the same person but from different datasets

using probabilistic algorithms. Studies involving linked data have greatly enhanced the ability to

investigate factors influencing health, health service utilisation and quality of surgical/medical

interventions. In WA, linked data are available through the WA Data Linkage System (WADLS),

which makes it possible to follow entire populations efficiently and cost‐effectively. In addition,

it allows retrospective studies to be conducted years after exposure to a treatment, diminishes

loss to follow‐up and abolishes reliance on self‐reported data. Further, the privacy of the medical

records is wholly protected, because the matching techniques and process used abide by best‐

practice guidelines for data linkage.[22, 23] However, data collected is restricted to information

collected for research purposes and access for researchers is dependent on approval from the

WADLS administrators.

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WA Data Linkage System

Established in 1995, the WADLS is distinctive in Australia and constitutes a powerful source for

whole‐population health services research and descriptive epidemiology within an Australian

setting. Using the WADLS overcomes limitations due to sample size, loss to follow‐up, and

ascertainment of precise exposure and outcome measures. The WADLS pools eight core

datasets including inpatient hospital admissions (Hospital Morbidity Data Collection, HMDC),

deaths and electoral roll registrations, which date back variously to 1966.[22] The System is

constantly updated and the quality of linkage has been assessed by comparison of routine

linkage to clerical investigation, with average proportions of invalid links (false‐positives) and

missed links (false‐negatives) both projected at 0.11%.[22]

It is worth considering the reliability of hospital discharge register data in the assessment of AMI

trends. A study in Finland [24] found that the trends obtained from their hospital discharge

register were very similar to the trends obtained from the FINMONICA AMI register, a study‐

specific population‐based registry. However, the attack rates of AMI differed significantly, and

the change in the International Classification of Diseases (ICD) from version 8 to version 9

resulted in a change in the attack rates obtained from the hospital discharge register.[24]

Thus, although hospital discharge register data can be used to assess AMI trends in the

community, modifications of the ICD codes (new versions of the classification) and changes in

the clinical use of the codes for CHD can have an impact on the AMI rates obtained from the

hospital discharge register, and the reliability of the hospital discharge register data should be

regularly assessed. The hospital discharge register data can also be supplemented with clinical

and biomedical datasets.

Trends in CHD risk factors, morbidity and mortality

Using linked administrative data, rates of hospital admissions for AMI in Perth, WA, fell 3.0% per

year in men and 3.3% per year in women until 2004.[14] These findings suggest that the

improvement in CHD mortality in WA may in part be due to a decrease in the disease severity,

in conjunction with better treatment of AMI and CHD risk factor management in both primary

and secondary prevention. [25‐28]

However, the recent results show that the rate at which CHD death has been falling in recent

years has slowed in younger (35‐54 years) age groups.[29] From 1998‐99 to 2009‐10, the general

rate of hospitalizations for CVD fell by 13%, with decreases detected for most major CVDs. This

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highlights that, in Australia, CVD is still a major healthcare burden even though there has been

a decrease in death and hospitalisation rates.

The major risk factors for CHD [30] have been monitored in diverse populations through the

World Health Organisation (WHO) multinational MONItoring of trends and determinants in

CArdiovascular disease (MONICA) project.[31] The aim of the MONICA project was to determine

how trends in event rates for CHD and stroke were linked to trends in the main coronary risk

factors. Repeated surveys of risk factors for AMI were completed across representative samples

from 38 populations in 21 countries in four continents from 1979 to 1996. Perth was one of the

participating centres in Australia for the MONICA study. The findings showed that the risk factor

trends for CHD were generally downward in most populations.[30,32‐33] However, there was a

greater increase in BMI for men, and an increase in smoking and diabetes in women.[32, 33] In

the Perth MONICA study, there was a significant reduction in the mortality rate from CHD from

1985 to 1993 for men and women.[32, 33]

Monitoring CHD in the modern era (MOCHA) was a cohort study in Perth, WA, which included

validation of the coding of AMI in the HMDC against the American Heart Association (AHA)

criteria for classifying AMI in epidemiological studies.[34] The MOCHA cohort contains a

representative sample of AMI cases in patients aged 35‐79 years during 1998 and 2003. The

data collection in MOCHA included information on risk factors, comorbidities, cardiac biomarker

testing and ECG evidence of myocardial injury.

Trends in CHD risk factors need to be viewed in the larger context of other possible key impacts

on AMI severity. Despite medical advancements, the burden of CHD is likely to remain over the

coming decades as there will be a growing population of elderly Australians among whom it is

highly prevalent. The majority of hospital admissions for heart attack and cardiac procedures

occurs among people aged 60 years and over—70% of AMI hospital admissions, 73% of coronary

artery bypass graft (CABG) procedures and 61% of percutaneous coronary intervention (PCI)

procedures.[35, 36] It is thus prudent to consider the role of AMI treatment. Improvements in

the treatment of AMI have had an important impact on the continuing decline in mortality from

CHD in Australia. A study by Briffa et al. found a lower 12‐year mortality in 28 day survivors of

incident AMI in patients aged 35‐64 years hospitalised in Perth from 1984 to 1993.[36] This

study analysed data from the WA MONICA cohort study. The authors concluded that improved

survival was consistent with better treatment of AMI and not necessarily explained by a change

in disease severity, although the latter was not examined in detail. Therefore it is necessary to

conduct a thorough exploration of changes in disease severity with the use of reliable and widely

accepted methods.

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In summary, we need to study trends in severity of AMI using updated, standardised guidelines

to be able to determine whether disease severity has made a significant contribution to the

decline in CHD mortality. My study will allow us to monitor trends in the severity of AMI. It will

thus provide a better understanding of the factors associated with the historical decline in CHD

mortality in Perth, WA. This will contribute to the evaluation of current health services, and

hence planning of future health services for the prevention and management of CHD in

Australia. It would also be pertinent to consider the diagnostic elements of AMI, in addition to

the factors which contribute to the severity of AMI. This allows for a better understanding of

the trends in AMI severity.

1.1 Thesis Aims

The aim of the study was to examine trends in AMI severity from 1984 to 2003 in patients who

were admitted to hospital in Perth, WA. This was achieved through the use of three cohorts: (i)

admissions for incident definite AMI in patients aged 35‐64 years (MONICA cohort‐ 1984 to

1996); (ii) admissions for incident or recurrent definite AMI in patients aged 35‐64 years

(MONICA cohort); and (iii) admissions for either incident or recurrent definite AMI in patients

aged 35‐79 years (MOCHA cohort – 1998 and 2003).

1.2 Significance of thesis

CHD is a major public health concern due to its impact on the health care system and the

community at large. It is postulated that the decline in age‐adjusted mortality due to CHD since

the late 1960s is due to a decrease in coronary risk factor levels in the community and an

improvement in the treatment of CHD, as well as a decrease in the severity of presentations of

AMI. This study documents and provides important information on changes in indicators of AMI

severity over a period of 20 years from 1984 to 2003 in WA. This allows us to better understand

if changes in trends in AMI severity may have possibly contributed to the decline in CHD locally.

Data for this study were obtained from clinical records within datasets from previous WA studies

(MONICA and MOCHA) as well as the HMDC using the research capacity of the WADLS. These

datasets are stored within the School of Population Health, University of Western Australia. The

MONICA register includes data on patients hospitalised in Perth for AMIs from 1984‐1993 in

patients aged 35‐64 years. Perth, in addition to Newcastle and Auckland, was one of the Pacific

collaborating centres in this project. For the purposes of our study, access to data from

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jurisdictions other than Perth was not possible. Updated standardised international definitions

are used in the study to analyse the rich historical clinical data to allow some generalisation of

the studies’ findings beyond Perth, WA, to countries with similar socio‐demographics.

1.3 Overview of thesis

The specific research aim of this thesis is outlined in Section 1.1. The primary aim of this study

is to document changes in indicators of AMI severity for patients admitted to hospital for

incident AMI over a period of 20 years from 1984 to 2003. One of the objectives of the study is

to determine whether trends in AMI severity are influenced by incident as opposed to all

(incident or recurrent) cases of AMI. Trends in indicators of AMI severity for a younger compared

to an older age group during the years 1998 and 2003 were also examined.

Chapter two contains the review of literature and explores the key components of the thesis in

more detail. Chapter three explains the methods used to analyse the data that were obtained

from existing datasets, to meet the aim of the thesis. Chapter four presents the research

findings. Lastly, Chapter five discusses the significance of the research findings and their wider

implications to public health. Chapter six concludes the thesis by highlighting and collating the

salient issues presented in the thesis.

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Chapter Two: Review of Research Issues

This chapter includes a description of the diagnosis of an AMI and the definitions used for AMI

cases. The need to measure trends in the severity of AMI versus the absolute cases of AMI is

discussed. The indicators of AMI severity, prior research and limitations are also outlined. The

chapter concludes with a discussion of the implications for practice: the importance to public

health and the potential for public health strategies.

2.1 Diagnosis of an AMI

Based on a widely used but variably interpreted international system, the diagnosis of an AMI

comprises clinical presentation (symptoms), electrocardiogram (ECG) findings and cardiac

biomarker levels.[37] Moreover, advancing diagnostic technology, therapeutic interventions,

and changing disease presentation in recent years necessitates a re‐evaluation of case

definitions for acute AMI.[38]

Cardiac Biomarkers

Cardiac biomarkers used to assist with the diagnosis of an AMI for which we have data include

creatinine kinase (CK), CK‐muscle brain (CK‐MB) isoenzyme, CK isoenzyme MB mass (CK‐MBm),

and latterly cardiac troponin (cTn) for which there are two types (I and T). The order of

biomarkers for their diagnostic value in AMI is cTn, CK‐MBm, CK‐MB, and CK in a decreasing

order of accuracy. [37‐40] A positive biomarker is an elevation of the cardiac enzyme above the

normal range where the value exceeds the 99th percentile of the distribution in healthy

populations or the lowest level at which a 10% coefficient of variation can be demonstrated for

that laboratory.[39, 40] CTn provides diagnostic information of an AMI that is more sensitive

and specific than CK in detecting even minor myocardial cell damage.[37] (see Appendix 1)

Studies have also demonstrated the need for more sensitive troponin measurement methods

because patients with even small increases of cTn seem to be at increased risk of cardiac

events.[41] In the years that cTn testing has been available in clinical laboratories, the biomarker

has changed testing of patients with acute coronary syndrome (ACS). ACS refers to the group of

conditions, AMI and unstable angina, which result from blockage of bloodflow in coronary

arteries. [42] Consequently there have been several generations of troponin assays, all toward

tests that consistently detect lower concentrations of this critical analyte.[43] Guidance for

understanding cTn has been in the form of MI redefinition and evidence‐based clinical and

19

analytical guidelines. While terminology has varied in the naming of generations for cTn assays,

state‐of‐the‐art assays in the period of interest are generally referred to as 'sensitive' assays.

Studies demonstrate that use of a sensitive troponin assay can result in diagnosis of early MI.

Succeeding generations of cTn I and T assays are termed 'high sensitivity'; these assays have the

ability to measure cTn with a coefficient of variation of less than 10% at concentrations

significantly lower than the 99th percentile of the normal reference population with elevated

cTn. [44]

The new generation cTn assays have adequate accuracy to detect and quantify plasma cTn

concentrations below the lower threshold of detection of previously used cTn tests.[45] Risk

stratification methods for patients with possible ACS recommend the use of sequential sensitive

cTn testing over a period of at least six hours. Cullen et al. in 2014 found that enhanced risk

stratification of patients with ACS symptoms may occur at two hours post‐presentation using

cTn results measured by a sensitive assay.[46]

Kost et al. in 1998 designed a strategy for cardiac injury marker testing in the diagnosis of

AMI.[47] Twenty‐seven cases of AMI were documented. It was concluded that CK‐MB mass,

myoglobin, and troponin I were selected as the cardiac injury markers of choice at their

institution. The strategy called for serial testing of myoglobin and CK‐MB mass initially, and

serially if warranted by heightened clinical suspicion, with troponin I added if indicated for (1)

specific confirmation, (2) late presentation, or (3) risk stratification. Kitamura et al. [48] found

that the high‐sensitivity troponin T displayed 100% sensitivity and negative predictive value for

the patients admitted more than 120 minutes from the onset, but the specificity was limited.

For the purposes of the study, in light of the availability of data and quality of the analysis, the

cardiac biomarkers used were CK, CK‐MB and early cTn assays (I or T).

Cardiac Symptoms and Signs

Cardiac symptoms and signs are findings from the history and clinical examination of the patient

presenting with likely AMI. They are elicited when assessing the patient on initial presentation

to the hospital in the Emergency Department. Cardiac symptoms include the presence of acute

chest, epigastric, neck, jaw, or arm pain, discomfort or pressure without apparent non‐cardiac

source. More general atypical symptoms, such as fatigue, nausea, vomiting, diaphoresis,

faintness, and back pain, should not be used as diagnostic criteria, even though they are

clinically useful in arriving at the correct diagnosis. The cardiac signs of key importance are acute

congestive heart failure or cardiogenic shock in the absence of non‐CHD causes.[38]

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ECG Findings

An ECG is a diagnostic tool that detects cardiac abnormalities by measuring the electrical activity

generated by the heart as it contracts. A typical ECG tracing is a repetitive cycle of three

electrical entities: a P wave (atrial depolarization), a QRS complex (ventricular depolarization)

and a T wave (ventricular repolarization).

Clinical studies have established a definite role for the use of the initial ECG as the most rapid

and readily available tool in the emergency department for the evaluation of patients presenting

with suspected MI. [49] One or more ECGs may be collected in a possible cardiac event. For

clinical use, these are viewed by doctors to identify any abnormalities usually seen as an

evolution of changes between ECGs. The evolution of ECG findings may be demonstrated

between the ECGs associated with the event or between a previously recorded ECG, where one

is available, and the event ECGs. In cases when only a single ECG is available for the qualifying

event, an evolving diagnostic ECG pattern can be recorded only if a previous study ECG is

available.

However, for epidemiological use, a more consistent and non‐changing classification method

needs to be adopted to code the ECG findings, especially for trend analysis. The most extensively

used classification system for ECG findings is the Minnesota Code.[50,51]

Taneja et al. [52] published a study in 2010 in the background of the controversy that exists

regarding the significance of pathological 'Q' waves on ECG following established AMI in

predicting non‐viable myocardium. The conclusion was that the presence of extensive 'Q' waves

does not predict non‐viable myocardium. Interestingly, the absence of 'Q' waves predicts the

presence of viable myocardium for inferior territory, but this is relatively less reliable for anterior

territory. Myocardial viability should be sought in patients with extensive 'Q' waves and in

unclear anterior territories.

Patients with ST elevation or new left bundle branch block are generally referred for timely

reperfusion therapy, while those without ST elevation are typically treated conservatively. [53‐

55] Patients are diagnosed as having anterior, inferior‐posterior, or lateral MI based on the

patterns of ST deviation, and assessment of risk is frequently made by basic measurements of

the magnitude of ST segment deviation or the width of the QRS complexes.[56] ST segment

elevation has been considered to be a sign of earlier repolarization of myocardial cells in the

damaged area and a current of injury which deviates the QT‐segment. [57]

21

2.2 Definitions for AMI Cases

Definitions of cases of acute CHD for epidemiology studies and clinical trials were previously

based on WHO (1959) and AHA (1964) reports, followed by the WHO European AMI Registry

criteria.[58,59] Further specifications and working definitions of CHD came from the

Framingham Study.[60] Differing case definitions have made it difficult to draw comparisons

among and within studies. Thus for the purposes of the research, a standardised international

protocol for the definition of AMI was used incorporating the epidemiological criteria that was

current at the time of hospitalisations as described in the AHA Scientific Statement by Luepker

et al.[38] This international, standardised epidemiological definition of AMI allows for the

monitoring of rates and comparisons of the disease within and between populations, and

incorporates troponin tests in the diagnostic algorithm for AMI.

2.3 The Need to Measure the Severity of AMI versus Absolute Cases of AMI

In recent years, there has been an increase in publicity advising individuals with sudden onset

chest pain to seek prompt medical attention which has led to an increase in the number of

individuals with chest pain presenting to emergency departments. It has been postulated that

this publicity may have possibly resulted in an increased number of admissions of individuals

with less severe cases of AMI in many jurisdictions.[58] In addition, there have been advances

in diagnostic technology; for instance, the introduction of troponin tests since the mid‐1990s.

This introduction has led to an increase in the detection of mild cases of AMI that would have

previously been undetected. [61]

The resulting increase in AMI cases being diagnosed has been estimated to range from 23 to

195 percent.[62,63] However, this increase may not necessarily reflect an increase in the degree

of severity of the disease. Hence, it is also important to examine the trends in severity of AMI

rather than just absolute admissions for AMI.

A recent study in Norway looked at the decline in AMI incidence rates during the years 2001‐

2009. The decline was due to reductions in rates of out‐of‐hospital deaths and hospitalisations

in individuals 45 years or older. A worrying increase in hospitalisation rates was observed in

those younger than 45 years for nonfatal AMI. However, it is difficult to make a meaningful

assessment of severity by simply looking at trends in absolute cases.[64]

22

The need to measure the severity of AMI rather than absolute cases can also be highlighted by

the MIYAGI‐AMI Registry Study, which shows a steady trend of increasing incidence, but

decreasing mortality for AMI in Japan over the 30 years prior to 2008 in the Miyagi

prefecture.[65]

2.4 Indicators of AMI Severity

AMI severity can be measured by its clinical indices or indicators. These indicators, as studied

by Jacobs et al.[66] and Goff et al. [67], include cardiac biomarkers, systolic blood pressure (SBP)

and pulse as well as ECG findings, and have been used in the current study. SBP and pulse were

the readings measured on presentation to the hospital. The pulse is classified as abnormal if it

was <60 or >100 beats per minute. The SBP was abnormal if the reading was <100 mm Hg. Using

the Minnesota Code [51, 52], ECGs were classified as: (i) either initial ST‐segment elevation or

subsequent ST‐segment elevation, (ii) initial Q wave, a subsequent Q wave, any major Q wave,

any minor Q wave.

The PREDICT tool [66], which is based on a composite of these and other indicators, is used as

an aggregate measure of AMI severity. PREDICT is a risk score for acute CHD patients that uses

information routinely obtained on the day of hospital admission for AMI. The PREDICT risk score

is a simple and powerful predictor of 6–year mortality after hospitalization for AMI. It is largely

independent of sex and CHD manifestation and reflects the severity of the event. Hence, this

independence of the PREDICT score has the advantage of diminishing confounding bias in

epidemiological studies of trend surveillance, quality assessment of care, the effects of hospital

treatment and long‐term prognosis. [66, 68, 69] Using the PREDICT tool for hospitalisations from

1985 to 1990, Jacobs et al. [66] found that the 6-year risk of death in 6134 patients post AMI

with 0 to 1 score points was 4%, growing to 89% for ≥ 16 points. The 6-year risk dropped to

32% from 1970 to 1990, when adjusted for case severity. The PREDICT tool and its

components will be further discussed in section 2.6.

2.5 Prior Research and Limitations

Previous studies have reported improved survival of AMI patients in recent decades.[15, 19‐21]

It has been suggested that this improvement is largely due to a decline in the severity of AMI as

well as an improvement in the treatment of AMI. The latter has been shown in an aggregated

analysis of the Newcastle and Perth MONICA data, which assessed long‐term survival after

evidence‐based treatment of AMI and revascularization.[36] It was shown that changes in

23

severity of AMI did not make a significant contribution to trends in CHD mortality. However, the

study did not monitor the trends in severity of AMI using updated, standardised international

definitions. The study looked at trends in rates of events, trends in levels of medical treatment

and trends in levels of risk factors. Non‐fatal coronary events were identified from routinely

collected data from discharge diagnoses recorded from the hospital. The definition used for the

identification of these cases was from the Ninth Revision of the ICD (ICD‐9) codes for AMI or

sub‐acute ischaemic heart disease (ICD codes 410 or 411 respectively). Studies by Sanfilippo et

al. [14], Beaglehole et al. [17], McGovern et al. [70], Rosamond et al. [71], Tunstall‐Pedoe et al.

[72] and Goldberg et al. [73] have shown that changes in severity of AMI have had mixed

contributions to the recent decline in CHD mortality. However, one study by Hellermann et al.

[74] showed a decline in severity of MI from 1983 to 1994. A key limitation in these studies has

been the lack of reliable data on accurate markers of AMI severity that can be consistently

measured over time.

Rosamond et al. [75] estimated race‐ and gender‐specific trends in the incidence of hospitalised

MI, case fatality, and CHD mortality from a community‐wide surveillance and validation of

patients discharged from hospital and of in‐ and out‐of‐hospital deaths among 35‐74‐year‐old

residents of four communities in the Atherosclerosis Risk in Communities (ARIC) Study.

Biomarker adjustment accounted for change from reliance on cardiac enzymes to widespread

use of troponin measurements over time. They observed significant declines in MI incidence,

primarily as a result of downward trends in rates between 1997 and 2008, although these

findings from four communities may not be directly generalisable to all races in the entire United

States.

In 2009, Myerson et al. [76] addressed the vital and understudied issue of trends in AMI severity

in the ARIC study between 1987 and 2002. They examined a large, multiracial population and

relied on several indicators, including the composite PREDICT score, and concluded that the

severity of infarction declined over time. Indeed, the proportion of infarctions with major ECG

abnormalities, ST‐segment elevation, and Q waves decreased, as did abnormal cardiac

biomarker results, and the proportion of patients presenting with shock. The PREDICT score also

improved over time. Hence, these findings demonstrated a consistent improvement across all

severity indicators, thereby suggesting that the declining severity of MI contributed to the

decline in CHD mortality. Since the upper age limit of the patients in ARIC is 74 years, these data

do not include the growing number of older patients who experience MI. Conversely, a distinct

strength of the ARIC data is its large and diverse cohort, allowing the examination of trends in

African‐Americans, which paralleled overall trends. These results are important as they support

24

and extend previous reports of a decline in the severity of MI as shown in studies that examined

severity indicators in Worcester and Olmsted County.[76‐78]

A study published by Kuch et al. in 2008 examined the extent to which evidence‐based beneficial

therapy was applied in practice, whether it was changing over time and whether it was

associated with improved outcomes.[79] The results showed that in the past 20 years, there

were significant changes in pharmacological and interventional therapies in AMI accompanied

by reductions in in‐hospital complications and 28‐day case fatality in all infarction types with

marked reductions in 28‐day case fatality in patients with bundle branch block. The latter

observation may primarily be due to the increased use of interventional therapy.[79]

Nishiyama et al. investigated the relationship between risk factor changes and AMI incidence in

a Japanese population. [80] Trends in AMI incidence (per 100,000 person‐years) were examined

using data from the Yamagata AMI Registry from 1993 to 2007. They found that the age‐adjusted

incidence of AMI increased significantly in men, but not in women. Younger men particularly

showed a significant increase in the incidence of AMI. The prevalence of hypertension and

diabetes increased in both genders; however, the prevalence of treatment for risk factors was

significantly lower in men than women. Younger men showed significant increases in obesity

and hypertriglyceridemia. Consequently, risk factors associated with the metabolic syndrome

had accumulated among younger men. The study showed that hypertension, diabetes,

hypercholesterolemia and current smoking were independent risk factors for AMI.[80]

However, the study looked at the prevalence of risk factors for AMI rather than measures of AMI

severity. This thesis will add to the literature and understanding of AMI by looking specifically at

measures of trends in markers of AMI severity rather than absolute numbers.

A recent study in Australia has shown that the improvement in CHD mortality was partially due

to better treatment of AMI.[36] It was shown that changes in severity of AMI did not contribute

significantly to the decline in CHD mortality. However, the study did not monitor the trends in

severity of AMI using updated, standardised international definitions. The study simply

examined differences in the distribution of demographic and clinical characteristics of those

surviving 28 days after the incident AMI. Measures of disease severity included a creatinine

kinase ratio, an abnormal ST segment deviation, an ECG score predicting the risk of death using

a cardiac disease tool and event complications (heart failure, cardiogenic shock, tachycardia

>100 beats per minute and SBP <100 mm Hg).

25

2.6 PREDICT Tool

The PREDICT tool [66], as described in section 2.4, is used to measure the severity of MI. The

score is based on a 30‐day, 2‐year, and 6‐year mortality experience after hospitalisation for MI

through the use of data collected in the Minnesota Heart Survey (MHS). The score components

include shock (0 to 4 points), clinical history (MI, stroke, angina; 0 to 2 points), age (0 to 3 points),

ECG findings (0 to 3 points), congestive heart failure (0 to 3 points), kidney function (0 to 3

points), and Charlson Comorbidity Index (0 to 6 points). A higher score indicates a greater

severity of AMI. Kidney function is based on blood urea nitrogen levels. The Charlson

Comorbidity index predicts the one‐year mortality of patients who have a range of comorbid

conditions such as cancer and heart disease. The maximum score is 24 points. The PREDICT score

is not largely affected by coding changes over time.[66, 68,69] The ARIC community surveillance

study, using a slight modification to the PREIDCT score, showed that a 1‐unit change in the

PREDICT score for AMI was associated with a 17% increased risk of 28‐day death (P<0.0001).[81]

Other tools have also been developed to measure the severity of AMI. For instance, Fox et al.

[82] developed a clinical risk prediction tool (GRACE score) for estimating the cumulative six

month risks of mortality, and mortality or MI, to facilitate triage and management of patients

with ACS. Nine factors were used to predict death and the combined end point of death or MI

in the period from admission to six months after discharge: age, development (or history) of

heart failure, peripheral vascular disease, SBP, Killip class, initial serum creatinine concentration,

elevated initial cardiac markers, cardiac arrest on admission, and ST segment deviation.

More recently, the Seattle Post Myocardial Infarction Model (SPIM) was developed to predict

survival 6 months to 2 years after an AMI with evidence of left ventricular dysfunction.[83] The

study demonstrated that the SPIM score provided a strong prediction of outcomes post MI with

left ventricular dysfunction, which could be helpful in post‐MI risk stratification and contribute

to the understanding of the survival of such patients.

2.7 Implications for Practice

2.7.1 Importance to Public Health

Trends in severity of AMI are useful in understanding the condition of AMI, especially in relation

to the decline in CHD mortality in the decades from the early 1980s to early 2000. CHD presents

a substantial financial drain on the public health system and creates massive costs for the

26

healthcare system, with the related direct healthcare costs exceeding those of any other

disease.[10] In 1993–94 the direct healthcare expenditure on CHD in Australia was $894 million

or 2.8% of the entire periodic heath expenditure.[84,85] Healthcare costs for CHD perhaps

accounted for more than this, given that CHD is a major risk factor for stroke and heart failure

and that the direct healthcare costs for these two diseases amounted to $1 billion in 1993–94.

The overall direct cost of CVD in Australia during 1993–94 was $3,719 million, with CHD

accounting for 24% of total CVD costs, stroke 17% and heart failure 11%.[84,85] In 2004–05,

close to $6 billion in health care expenditure was spent on CVD in Australia, representing 11%

of the total health expenditure.[13] Of the total expenditure on CVD, 40% was spent on

coronary heart disease ($1,813 million) and stroke ($546 million).

In spite of substantial developments in the treatment of CVD and some of its risk factors, CVD

remains the cause of the majority of deaths in Australia, with approximately 50,000 deaths in

2009. [11] A recent publication by the Australian Bureau of Statistics for the period 2011 to

2013 reports that CVD is still the main cause of death in Australia at 136 per 100,000 population.

It is the most expensive disease, costing about $5.9 billion in 2004‐05.[7] Not all segments of

Australian society are equally affected by CVD. People in lower socioeconomic groups,

Aboriginal and Torres Strait Islander people and those living in the isolated areas of Australia

are often more likely to be hospitalised with, or to die from CVD than others in the population.

[7, 84‐86] It accounts for a greater proportion of deaths in men (5.5%) than women (4.5%), and

the most disadvantaged (6.5%) as compared to the least disadvantaged (3.3%).[29]

Trends in AMI severity have yet to be studied in a systematic manner, using updated

standardised tools. It is of utmost importance to understand the trends in AMI severity and

gain valuable information on whether they significantly contribute to the cause of the decline

in CHD mortality. These trends in AMI severity are from an epidemiological perspective. They

will inform better management of CHD, which is a still significant concern to public health.

27

2.7.2 Potential for Public Health Strategy

The findings from the study of trends in AMI severity would allow for evidence‐based public

health policies to be implemented, through the understanding of the cause of the decline in

CHD mortality. Gaining a clear understanding of the policies and strategies that have worked

well also lays a stronger foundation for placing future cost‐effective policies and for better

utilisation of our scarce public health resources.

28

Chapter Three: Methodology

Chapter three details the methodology used for research in this study. It includes the design of

the study, the constituents of the study population and the period from which the data were

collected and analysed. The chapter concludes with a statement on the ethical approval for this

study.

3.1 Study Design

This is a population‐based retrospective study of three serial cohorts which are described in

section 1.1.

3.2 Study Population

The study population consisted of all residents of the Perth Statistical Division (population 1.19

million in 1991 and 1.43 million in 2003), aged 35‐79 years who were admitted to hospital with

a definite AMI and registered by the MOCHA and Perth MONICA studies during 1984‐2003.

The study population specifically consisted of the following age groups in the following three

cohorts: (i) admissions for incident definite AMI in patients aged 35‐64 years (MONICA cohort‐

1984 to 1996); (ii) admissions for incident or recurrent definite AMI in patients aged 35‐64 years

(MONICA cohort); and (iii) admissions for either incident or recurrent definite AMI in patients

aged 35‐79 years (MOCHA cohort – 1998 and 2003). Study (ii) includes the individuals in study

(i) as well as those individuals who went onto experience a recurrent event within four years of

the index event. The cases are treated as episodes rather than individuals to make this clear.

Hence, patients could be readmitted with a subsequent AMI after four years and be reclassified

as an index event.

3.3 Observation Period

The observation period of the study was from 1st January 1984 ‐ 31st December 2003. The specific

observation period for the three cohorts are as follows: (i) admissions for incident definite AMI

in patients aged 35‐64 years (MONICA cohort‐ 1984 to 1996); (ii) admissions for incident or

recurrent definite AMI in patients aged 35‐64 years (MONICA cohort); and (iii) admissions for

29

either incident or recurrent definite AMI in patients aged 35‐79 years (MOCHA cohort – 1998

and 2003).

3.4 Data Collection

This study relied on the analysis of routinely collected data stored in existing health databases

and available through the WADLS. The analysis used data from two separate studies previously

carried out in the School of Population Health (SPH) at The University of WA (UWA): the MOCHA

study and the Perth MONICA project as described in section 1.0. These studies used a

combination of clinical and administrative data. These datasets include incident (first‐ever) and

recurrent cases of hospitalised AMIs, as defined previously in section 2.2, classified as definite

MI by either the MONICA or AHA 2003 criteria.[31, 38] There were few values available for the

mean PREDICT Final Score for females with incident definite AMI in the year 1997.

In this study, incident and recurrent AMI cases were identified. Incident AMI cases refer to

patients with no admission for MI in the four years before the index (initial) admission. These

cases have been sub grouped as follows: (i) cases without admissions for other IHD in the

previous 4 years, and (ii) cases with admissions for other IHD in the previous 4 years. Recurrent

AMI cases refer to patients who have been admitted for AMI in the four years before the index,

i.e. first admission. IHD refers to the obstruction in the coronary artery which diminishes the

supply of blood to heart muscle. A definite AMI case is one with an evolving diagnostic ECG or a

diagnostic (i.e. positive) biomarker. If the biomarker is not troponin then the MI is downgraded

to possible rather than definite (ie. if ECG is non‐specific or normal/other findings and diagnostic

marker is CK and no signs or symptoms then MI is possible, not definite.[38] Only definite cases

of AMI are included in the study. AMI cases which do not fit the criterion of a definite AMI as

outlined above were not included in order to be consistent with the earlier definition of MI cases

in the MONICA study. These case definitions, from an epidemiological perspective, are critical

for accurate monitoring of trends in MI severity.

Events of interest were identified as 28‐day episodes of AMI based on hospital admissions with

a hierarchical discharge diagnosis of AMI, including cases of AMI with onset in the hospital. That

is, admissions for the same person were counted as new or recurrent events only if they

occurred more than 28 days from the date of a previous admission for AMI. [14] With regard to

hospital transfers, only the diagnosis of AMI at the first hospital is included as a case.

Changes in cardiac biomarker testing (discussed in section 2.1) have made it difficult to

accurately measure and interpret trends in AMI severity. Troponin tests were introduced in the

30

mid‐1990s to increase the sensitivity and specificity of diagnosis of myocardial cell damage.

However, clinicians continued to request CK levels during our study period despite the

introduction and rapid uptake of troponin testing. This allowed CK levels to be incorporated in

our trend analysis.

Diagnosis codes are used as a means to group and identify diseases, disorders, symptoms,

poisonings, adverse effects of drugs and chemicals, injuries and other reasons for patient

encounters. Diagnostic coding is the translation of written descriptions of diseases, illnesses and

injuries into codes from a specific classification. In medical classification, diagnosis codes are

used as part of the clinical coding process together with intervention codes. Both diagnosis and

intervention codes are allocated by a clinical coder or Health Information Manager.[87,88]

Diagnoses in the HMDC are recorded in each hospital by clinical coders using the ICD codes and

rules available at the time of the discharge, and which are revised periodically. During 1980–

2004, there were three such revisions: [87, 89‐91] ICD 9 was used from 1980 to 1987; ICD 9,

Clinical Modification (ICD‐9‐CM) was used from 1988 to 30 June 1999 (the Australian Version

of ICD‐9‐CM was used from 1 July 1 1995 to 30 June 1999); and the International Classification

of Diseases, Tenth Revision, Australian Modification (ICD‐10‐AM) has been used in WA since 1

July 1999.[92] The following ICD codes were used to identify hospital admissions for AMI: 410

(ICD‐9 and ICD‐9‐CM) and I21 and I22 (ICD‐10‐AM).

Internal analysis of previously linked datasets held by the Cardiovascular Research Group

(School of Population Health, University of Western Australia), has shown that for fatal cases

coded as AMI in the HMDC, approximately 15% have the code for AMI in a secondary diagnosis

field. [34] Hence, limiting the analysis of AMI to the principal diagnosis field would miss a

significant number of relevant records in both the MONICA and MOCHA databases.

The HMDC linked data was also used to calculate the CCI [93] which predicts the one‐year

mortality for patients who have a range of comorbid conditions such as cancer and heart

disease. This was done by applying a fixed 5‐year look‐back period to the data in the HMDC for

admissions of AMI, and excluded AMI in the calculation of the score if it occurred in the index

admission. The 5‐year look‐back period was an arbitrary choice. The datasets were provided

from other studies (MONICA and MOCHA). The Dartmouth‐Manitoba ICD code assignments

were used in calculating the CCI based on the original 17 Charlson comorbidities. [93,94]

The outcome variables for this study were the PREDICT total score and its components: heart

rate/ pulse, SBP, individual components of ECG findings and cardiac biomarker test results.

As described in section 2.4, the PREDICT score components include shock (0 to 4 points), clinical

history (MI, stroke, angina; 0 to 2 points), age (0 to 3 points), ECG findings (0 to 3 points),

31

congestive heart failure (0 to 3 points), kidney function (0 to 3 points), and CCI (0 to 6 points).

The maximum PREDICT score is 24 points.

The CCI component includes an overall comorbidity score and a PREDICT point computation (see

Appendix 3). The following comorbidities of the CCI component are each given a score of 1: MI,

congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic

pulmonary disease, rheumatologic disease, peptic ulcer disease, mild liver disease and diabetes

(no complications). The following comorbidities each score 2 points: diabetes complications,

hemiplegia or paraplegia, renal disease and any malignancy. Moderate‐severe liver disease is

given a score of 3. The following two comorbidities are given a score of 6: AIDS and metastatic

solid tumour. The comorbidity scores for each person are then added for a total maximum score.

The PREDICT point computation is a categorical variable derived from the CCI and graded

according to severity by the following method: Normal if the Charlson score is 0; Moderate for

a Charlson score of 1; Severe if the Charlson score is 2; and very severe if the Charlson score is 3

or above. Comorbidities were identified from all diagnosis fields.

The kidney function score in PREDICT is usually based on blood urea nitrogen levels, but as these

data were incomplete (89.4% were missing blood urea nitrogen), serum creatinine levels were

used instead. Normal levels of serum creatinine are approximately 0.6 to 1.2 milligrams (mg)

per deciliter (dL) in males and 0.5 to 1.1 mg per dL in females.

The congestive heart failure component of the PREDICT score consists of four sub‐components:

use of digoxin at admission; suspicion of pulmonary congestion (chest X‐ray ordered by the

physician); pulmonary congestion on X‐ray at admission; and pulmonary oedema on X‐ray at

admission. Data were only available for one of these sub‐components: the use of digoxin at

admission.

The first PREDICT component of shock has two main sub‐components: (i) first observable SBP of

61‐99 mmHg, first blood pressure unobtainable, first recorded heart rate 100‐119 beats/min;

and (ii) first observable SBP <60 mmHg and first recorded heart rate of 120 or more beats/min.

The component of shock is graded according to severity using the following method: Normal

with a score of 0 points if a patient has none of the sub‐components of shock; Moderate with a

score of 2 if a patient has at least one of the first three sub‐components; and Severe with a score

of 4 if a patient has at least 2 of the first three sub‐components or at least one of the last two

sub‐components.

32

The second PREDICT component of clinical history has the following sub‐components: MI,

stroke, angina (greater than 8 weeks prior to admission), CABG, cardiac arrest and hypertension.

Clinical history is graded according to severity using the following method: Normal with a score

of 0 if there are none of the sub‐components; Mild with a score of 1 if there are 1 or 2 of the

sub‐components listed; and Moderate with a score of 2 if there are 3 or more of the sub‐

components.

The third PREDICT component, age, is scored as follows: 0 points if age is between 35‐59 years;

1 point if age is between 60 and 69 years; and 3 points if age is 70‐74 years.

The fourth PREDICT component, ECG severity score, has a preliminary assessment and then a

PREDICT point computation. The preliminary assessment has the sub‐components of Q wave

Infarction (transmural) and non Q wave Infarction (non‐transmural). Q wave Infarction is further

sub‐divided into major (score 2 points) and minor (score 1 point) categories. The major sub‐

division consists of the following: Q wave duration ≥0.03 seconds; Q/R amplitude ≥1/3;

anterolateral or anterior infarction. The minor sub‐division consists of the following: Q wave

duration ≥0.02 seconds and <0.03 seconds; Q/R amplitude ≥1/3; anterolateral or anterior

infarction. Non Q wave infarction is also further sub‐divided into major (2 points each) and

minor (score of 1 point each) categories. A major sub‐division comprises the following: ST

segment depression ≥1.0 mm, horizontal or downward sloping; anterolateral, anterior or

posterior/inferior infarction. A minor sub‐division consists of the following: ST segment

depression ≥0.5 mm and <1.0 mm; horizontal or downward sloping; anterolateral, anterior or

posterior/inferior infarction. The Q and ST scores are then added (with a range of 0 to 15) to

obtain a total Q/ST score.

The PREDICT point computation for ECG severity is scored using the following method. If there

is no bundle branch block (BBB) or infarction (Q/ST score=0) it is normal and scores a value of 0.

It is mild with a score of 1 if the Q/ST score is 1‐4 with no BBB or if the Q/ST score is 0 and there

is a right BBB. It is moderate with a score of 2 if the Q/ST score is ≥5 or if there is no left BBB. It

is severe with a score of 3 if there is an intraventricular block, or a right BBB and major Q waves.

In addition to the PREDICT tool components, data on heart rate, SBP, individual components of

ECG findings and cardiac biomarker test results were also analysed. Heart rate or pulse was

classified as abnormal if it was <60 or > 100 beats/minutes. SBP was classified as abnormal if it

was < 100 mmHg. Although the PREDICT component of ECG severity consists of Q waves and its

subdivisions, these findings were then grouped and validated with a separate analysis for trends

as simply the presence of Q waves. Furthermore, data on ST elevation AMI were also analysed.

An abnormal ST elevation consisted of the presence of ST elevation on the ECG report.

33

3.5 Data Analysis

Men and women, and each of the three cohorts, were analysed separately. Age was summarised

by mean, standard deviation (SD), median and interquartile range. Frequencies and proportions

were reported for patients with a history of diabetes, heart failure, stroke, hypertension, CABG

and use of digoxin, in the four years prior to the date of their diagnosis with incident definite

AMI. Differences in demographic and clinical characteristics between men and women were

examined using t‐tests for continuous variables and chi‐squared tests for categorical variables.

Frequencies and percentages, aggregated over the study period for each of the first two cohorts,

are also reported for categories of PREDICT components, and each value of the PREDICT final

score. Likewise, frequencies and percentages are provided for each of the two years of data that

were available for the third cohort.

The outcome variables in the regression models used to estimate trends were the PREDICT total

score and its multi‐categorical and binary components: heart rate, SBP, individual components

of ECG findings and cardiac biomarker test results (troponin, CK‐MB and CK). Linear [97], ordinal

[98] and binary logistic regression models [99] were used according to whether the outcome

was continuous, multi‐categorical or binary, respectively. Initial models included calendar year

as the independent variable, and these models were subsequently adjusted for age. Variables

were categorised according to the PREDICT tool method. [66]

Specifically, the PREDICT total scores were modelled using linear regression. In these models,

the coefficient for year estimated the mean change in the PREDICT total score per annum.

Ordinal regression was used for PREDICT components with more than two score categories. The

results from these models are expressed as odds ratios, which estimate the factor change in the

odds of the highest severity, compared to the combined moderate and mild/normal severity of

the PREDICT component, associated with a 1 year increase in time. When the PREDICT

component had only two categories, logistic regression was used. The results from these models

are expressed as odds ratios, which indicate the factor change in the odds of the higher severity,

compared to the lower/normal severity of the PREDICT component, associated with a 1 year

increase in time. For ease of comparison and consistency in interpretation, multi‐category

components were also reduced into two categories and modelled using logistic regression. Both

the ordinal and logistic regression results are reported in this thesis.

To test if the trend for calendar year differed according to whether the event was incident or

recurrent (case type), an interaction term for year and case type was included in the regression

34

models for the second cohort. Similarly, to test whether the trend differed between younger

(35‐64 years) and older (65‐79 years) patients, an interaction term for year and age group was

included in the regression models for the third cohort. If there was no significant interaction,

the model was refitted to determine whether there was a main effect for case type or age group,

in the respective cohorts. Tests for interaction were conducted as part of the analysis. As there

were none, interaction terms were not included.

Data were analysed using SPSS (Version 21.0) with two‐sided tests and statistical significance

at the 5% level.[100] Missing data were computed as null values in the analysis.

3.6 Ethics

The use of MONICA, MOCHA and linked data file for the epidemiological research described in

this thesis has current ethical approval (Appendix 4) and adheres to the requirements of the

Privacy Act 1988 and the National Health and Medical Research Council (NHMRC) National

Statement on Ethical Conduct in Research Involving Humans 2007. The Head of School (SPH,

UWA) has certified that all necessary approvals have been obtained.

35

Chapter Four: Results

The trends in indicators of AMI severity using the PREDICT Tool and individual markers of

severity are presented in this chapter. The three main sections of this chapter contain

descriptions of the patient characteristics and results from analyses that estimate trends in AMI

severity in patients hospitalised in Perth for the following time periods and groups of patients:

(i) 1984 to 2003 for patients aged 35‐64 years with incident definite AMI; (ii) 1984 to 2003 for

patients aged 35‐64 years with incident or recurrent definite AMI; and (iii) 1998 and 2003 for

patients aged 35‐79 years with incident or recurrent definite AMI.

Within each section, the characteristics of the set of patients are presented, followed by the

results of regression analyses, which are divided into three sub‐sections: (i) analysis of separate

PREDICT components; (ii) analysis of the combination of components in the final PREDICT score;

and (iii) analysis of the individual key markers of AMI severity that have not been included as

individual components in the PREDICT tool.

4.1 Trends in AMI Severity from 1984 to 2003 for Patients Aged 35‐64 years

Hospitalised in Perth with Incident Definite AMI

4.1.1 Subject Characteristics

In the cohort of patients aged 35‐64 years hospitalised in Perth with incident definite AMI

between 1984 and 2003, mean ages were 54.1 years (SD 7.3 years) for men and 56.4 years (SD

6.7 years) for women. The median age for men was 56.0 years with an interquartile range (IQR)

of 11.0 years. In women, the median age was 58.0 years with an IQR of 10.0 years. On average,

women were 2.3 years (95% CI 1.8‐2.7 years; p<0.001) older than men. The mean and median

age for the cohort (men and women together) was 55.3 years (SD 6.9 years) and 57.0 years

respectively with an IQR of 10.0 years.

Table 4.1 presents the numbers and percentages of men and women with histories, in the four

years prior to the date of diagnosis with incident definite AMI, of diabetes, heart failure, stroke,

hypertension, CABG, and use of digoxin (used in the treatment of congestive/chronic heart

failure). The highest prevalent comorbidity was 14.3% for hypertension in women. Percentages

of comorbidities in men and women differed significantly, with women more likely than men to

have a history of diabetes, heart failure, stroke and/or hypertension. Women were also more

likely to have used digoxin.

36

Table 4.1 Patient characteristics of history of diabetes, heart failure, stroke, hypertension, CABG and use of digoxin in the four years prior to their date of diagnosis with incident definite AMI

Subject Characteristics

Men N=5100

Women N=1238

*P‐value

n (%) n (%)

Diabetes No 4931 (96.7) 1139 (92.0) Yes 169 (3.3) 99 (8.0) <0.001

Heart Failure No 5027 (98.6) 1208 (97.6) Yes 73 (1.4) 30 (2.4) 0.012

Stroke No 4993 (97.9) 1196 (96.6) Yes 107 (2.1) 42 (3.4) 0.006

Hypertension No 4762 (93.4) 1061 (85.7) Yes 338 (6.6) 177 (14.3) <0.001

CABG No 5098 (99.96) 1238 (100.0) Yes 2 (0.04) 0 (0.0) 0.647

Use of digoxin No 4569 (89.6) 1092 (88.2) Yes 105 (2.1) 41 (3.3) Missing 426 (8.4) 105 (8.5) 0.007

*Chi‐squared test

CABG= Coronary Artery Bypass Graft, AMI= Acute Myocardial Infarction

4.1.2 PREDICT Components

This section presents the frequency distribution and the results of trend analysis for each of the

following seven PREDICT components: Charlson comorbidity score, shock, clinical history, age,

ECG severity, renal and heart failure. First, Table 4.2 shows the frequency distribution of the

PREDICT component points and how they have been grouped for binary coding. Heart failure

was the only component with missing data (3.4% in total). For most of the PREDICT components

there was a large percentage of subjects with normal and therefore low PREDICT point scores.

37

Table 4.2 PREDICT component points, binary coding and frequency distribution from 1984 to 2003

PREDICT component

PREDICT points

Binary coding

Men N=5100

Women N=1238

Total N=6338

n (%) n (%) n (%)

CCI

Normal 0 0 0 0 0 Low 2 0 3616 (70.9) 766 (61.9) 4382 (69.1) Moderate 4 1 1092 (21.4) 293 (23.7) 1385 (21.9) Severe 6 1 392 (7.7) 179 (14.5) 571 (9.0)

Shock Normal 0 0 3100 (60.8) 679 (54.8) 3779 (59.6) Moderate 2 1 1347 (26.4) 349 (28.2) 1696 (26.8) Severe 4 1 653 (12.8) 210 (17.0) 863 (13.6)

Clinical history Normal 0 0 4715 (92.5) 1047 (84.6) 5762 (90.9) Mild 1 1 385 (7.5) 191 (15.4) 576 (9.1)

Age 35‐59 years 0 0 3594 (70.5) 714 (57.7) 4308 (68.0) 60‐64 years 1 1 1506 (29.5) 524 (42.3) 2030 (32.0)

ECG severity No BBB/infarction 0 0 2782 (54.5) 625 (50.5) 3407 (53.8) Mild 1 1 2102 (41.2) 561 (45.3) 2663 (42.0) Moderate 2 1 127 (2.5) 39 (3.2) 166 (2.6) Severe 3 1 89 (1.7) 13 (1.1) 102 (1.6)

Renal failure No 0 0 5062 (99.3) 1214 (98.1) 6276 (99.0) Yes 1 1 38 (0.7) 24 (1.9) 62 (1.0)

Heart failure No 0 0 4787 (93.9) 1124 (90.80) 5911 (93.3) Yes 1 1 146 (2.9) 65 (5.3) 211 (3.3) Missing ‐ ‐ 167 (3.3) 49 (4.0) 216 (3.4)

CCI = Charlson Co‐morbidity Index; Clinical history as per the PREDICT Tool = stroke, angina, coronary

artery bypass graft surgery, cardiac arrest and hypertension; BBB = bundle branch block;

ECG=Electrocardiogram.

38

Table 4.3 shows the crude and age‐adjusted odds ratio (ORs), with their 95% confidence

intervals (CI), for men and women, from logistic regression analyses of the components when

they are considered as binary variables. Each OR in Table 4.3 indicates the factor change in the

odds of the higher severity category of the PREDICT component associated with a 1 year increase

in time.

Table 4.3 Results from logistic regression analysis of PREDICT components for cohort 1 as binary variables from 1984 to 2003

Variable Men N=5100

Women N=1238

Crude OR (95% CI)

Age‐adjusted OR (95% CI)

Crude OR (95% CI)


CCI 1.05 (1.03, 1.06) p<0.001

1.05 (1.04, 1.07) p<0.001

1.08 (1.06, 1.11) p<0.001

1.09 (1.06, 1.12) p<0.001

Shock 0.98 (0.97, 0.997) p=0.013

0.98 (0.97, 0.997) p=0.014

1.01 (0.98, 1.03) p=0.566

1.01 (0.98, 1.03) p=0.574

Clinical history

1.03 (1.01, 1.05) p=0.008

1.03 (1.01, 1.06) p=0.004

1.02 (0.99, 1.05) p=0.252

1.02 (0.99, 1.06) p=0.226

Age 0.98 (0.98, 1.01) p=0.348

‐ 0.99 (0.96, 1.01) p=0.357

‐

ECG severity 0.97 (0.96, 0.98) p<0.001

0.97 (0.96, 0.98) p<0.001

0.95 (0.93, 0.98) p<0.001

0.95 (0.93, 0.98) p<0.001

Renal failure

1.12 (1.06, 1.18) p<0.001

1.12 (1.06, 1.18) p<0.001

1.13 (1.05, 1.21) p=0.001

1.12 (1.05, 1.21) p=0.001

Heart failure

0.99 (0.95, 1.03) p=0.688

0.99 (0.95, 1.04) p=0.811

1.03 (0.97, 1.08) p=0.354

1.03 (0.97, 1.09) p=0.309


artery bypass graft surgery, cardiac arrest and hypertension. OR = odds ratio; 95% CI = 95% confidence

interval; ECG= Electrocardiogram.

Table 4.4 shows the results of ordinal regression analyses for three PREDICT components that

have more than two point categories: Charlson comorbidity score, shock and ECG severity. Each

OR in this table indicates the factor change in the odds of the highest severity of PREDICT

component associated with a 1 year increase in time.

39

Table 4.4 Results from ordinal regression analysis for PREDICT components with more than 2

categories from 1984 to 2003

Variable Men N=5100

Women N=1238

Crude OR (95% CI)


Crude OR (95% CI)


CCI 1.05 (1.04, 1.07) p<0.001

1.06 (1.04, 1.07) p<0.001

1.09 (1.06, 1.12) p<0.001

1.09 (1.07, 1.12) p<0.001

Shock 0.99 (0.97, 0.999) p=0.033

0.99 (0.97, 0.999) p=0.034

1.00 (0.98, 1.03) p=0.875

1.00 (0.98, 1.03) p=0.887

ECG severity

0.97 (0.96, 0.98) p<0.001

0.97 (0.96, 0.98) p<0.001

0.96 (0.93, 0.98) p<0.001

0.96 (0.93, 0.98) p=0.001


artery bypass graft surgery, cardiac arrest and hypertension OR = odds ratio; 95% CI = 95% confidence

interval; ECG= Electrocardiogram

There was a small but statistically significant increase in Charlson score severity over the period

from 1984 to 2003 (Tables 4.3 and 4.4). The age‐adjusted odds of having a high (moderate or

severe) Charlson score increased by a factor of 1.05 (95% CI 1.04‐1.07) per year for men, and

1.09 (95% CI 1.06‐1.12) for women (Table 4.3). These results were similar to those from ordinal

regression in which the age‐adjusted odds of having a severe Charlson score increased by a

factor of 1.06 (95% CI 1.04‐1.07) per year for men and 1.09 (95% CI 1.07‐1.12) for women (Table

4.4).

There was a small but significant decrease in ECG severity over the period from 1984 to 2003.

The age‐adjusted odds of having mild or severe BBB or MI decreased – the factor change was

0.97 (95% CI 0.96‐0.98) per year for men and 0.95 (95% CI 0.93‐0.98) for women (Table 4.3). The

results were similar to those from ordinal regression in which the odds of having a severe rating

for ECG severity changed by a factor of 0.97 (95% CI 0.96‐0.98) per year for men and 0.96 (95%

CI 0.93‐0.98) for women (Table 4.4).

Shock and clinical history had small but significant opposing changes in severity over the period

from 1984 to 2003 for men only, with age‐adjusted odds ratios of 0.98 (95% CI 0.97‐0.997) and

1.03 (1.01‐1.06) respectively, for each additional year (Table 4.3). There was a significant

increase in the prevalence of renal failure over time. The age‐adjusted odds of having renal

failure increased by a factor of 1.12 (95% CI 1.06‐1.18) per year for men (Table 4.3). The results

were similar for women. Patient age and the prevalence of heart failure did not change

significantly over time.

40

4.1.3 PREDICT Final Score

Table 4.5 presents the frequencies of patients aged 35‐64 years hospitalised in Perth with

incident definite AMI aggregated over the study period from 1984 to 2003 for each component

of the PREDICT final score. A higher score indicates a greater severity of AMI. The modal score

was 2 for men with frequency 20.1%, and thereafter frequencies decreased consistently as

scores increased. A similar pattern was seen in the women’s scores, except that their modal

score was 3 with frequency 16.8%.


from 1984 to 2003 in patients aged 35‐64 years with incident definite AMI

PREDICT Final

Score

Male

N=5100

Female

N=1238

Total

N=6338

n (%) n (%) n (%)

2 1023 (20.1) 144 (11.6) 1167 (18.4)

3 992 (19.5) 208 (16.8) 1200 (18.9)

4 749 (14.7) 176 (14.2) 925 (14.6)

5 649 (12.7) 162 (13.1) 811 (12.8)

6 498 (9.8) 133 (10.7) 631 (10.0)

7 375 (7.4) 98 (7.9) 473 (7.5)

8 229 (4.5) 93 (7.5) 322 (5.1)

9 171 (3.4) 63 (5.1) 234 (3.7)

10 95 (1.9) 42 (3.4) 137 (2.2)

11 75 (1.5) 33 (2.7) 108 (1.7)

12 45 (0.9) 18 (1.5) 63 (1.0)

13 23 (0.5) 15 (1.2) 38 (0.6)

14 7 (0.1) 2 (0.2) 9 (0.1)

15 1 (0.02) 2 (0.2) 3 (0.05)

16 1 (0.02) 0 (0.0) 1 (0.02)

Missing 167 (3.3) 49 (4.0) 216 (3.4)

Percentages may not sum to 100% due to rounding. AMI= Acute Myocardial Infarction

Figure 1 displays the mean PREDICT Final Score for males and females with incident definite

AMI from the years 1984 to 2003. For instance in the year 1984, the mean PREDICT Final Score

was 4.41 and 4.71 for males and females respectively. At the end of the time period, in 2003,

the mean PREDICT final score declined to 4.33 for males but increased to 5.91 for females.

41

Overall, there was a very small gradual increase in the mean PREDICT Final Score for men, with

a slightly larger increase in women from the years 1984 to 2003. There were few values

available for the mean PREDICT Final Score for females with incident definite AMI in the year

1997.

Figure 1: Graph of the mean PREDICT Final Score for males and females with incident definite AMI over the period from 1984 to 2003

AMI=Acute Myocardial Infarction. The gap for females is due to missing data.

Table 4.6 shows the unadjusted and age‐adjusted coefficients from linear regression analyses

that estimate the changes in mean PREDICT Final scores with a 1 year increase in time over the

period from 1984 to 2003. After adjustment for age, there was a small but significant increase

of 0.02 (95% CI 0.001‐0.03) per year in the mean PREDICT final score for men. For women, the

increase was approximately double that of men, with an age‐adjusted increase of 0.05 (95% CI

0.02‐0.09).

0

1

2

3

4

5

6

7

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

MEA

N PRED

ICT FINAL SCORE

YEAR

Female Male

42


score 1984‐2003

Variable Men

N=5100

Women

N=1238

β

(95% CI)

Age‐adjusted β

(95% CI)

β

(95% CI)

Age‐adjusted β

(95% CI)

PREDICT

final score

0.01 (‐0.005, 0.03)

p=0.18

0.02 (0.001, 0.03)

p=0.048

0.047 (0.01, 0.08)

p=0.011

0.05 (0.02, 0.09)

p=0.004

β is the coefficient that indicates the change in mean PREDICT final score per year,

95% CI = 95% confidence interval, ECG= Electrocardiogram

4.1.4 Other Markers of AMI Severity

Table 4.7 shows the score categories and frequency distribution aggregated over the study

period from 1984 to 2003 of other common markers of AMI severity: heart rate, SBP, ST

elevation, Q‐waves, cTn, CK‐MB and CK tests. Abnormal heart rate and abnormal SBP were the

only markers of AMI severity without any missing values.

43

Table 4.7 Categories of other markers of AMI severity and frequency distribution 1984‐2003

in patients aged 35‐64 years with incident definite AMI

Markers of AMI Severity

Men N=5100

Women N=1238

Total N=6338

n (%) n (%) n (%)

Abnormal Heart Rate

No 3515 (68.9) 801 (64.7) 4316 (68.1) Yes 1585 (31.1) 437 (35.3) 2022 (31.9)

Abnormal SBP No 455 (8.9) 137 (11.1) 592 (9.3) Yes 4645 (91.1) 1101 (88.9) 5746 (90.7)

ST Elevation No 2586 (50.7) 524 (42.3) 3110 (49.1) Yes 2047 (40.1) 586 (47.3) 2663 (41.5) Missing 467 (9.2) 128 (10.3) 594 (9.4)

Q‐waves No 1989 (39.0) 381 (30.8) 2370 (37.4) Yes 2712 (53.2) 742 (59.9) 3454 (54.5) Missing 399 (7.8) 115 (9.3) 514 (8.1)

Positive Troponin

No 1 (0.0) 2 (0.2) 3 (0.05) Yes 145 (2.9) 36 (2.9) 181 (2.85) Missing 4594 (97.1) 1200 (96.9) 6154 (97.1)

Positive CK‐MB No 1499 (29.4) 371 (30.0) 1870 (29.5) Yes 3220 (62.1) 754 (60.9) 3924 (61.9) Missing 431 (8.5) 113 (9.1) 544 (8.6)

Positive CK No 346 (6.8) 171 (13.8) 519 (8.2) Yes 4712 (92.4) 1061 (85.7) 5768 (91.0) Missing 42 (0.8) 6 (0.5) 51 (0.8)

AMI = Acute myocardial infarction, SBP = systolic blood pressure, CK = Creatinine Kinase, MB = Muscle Brain

Table 4.8 shows the crude and age‐adjusted ORs, with their 95% CIs, for men and women from

logistic regression analyses of the other markers of AMI severity. The ORs indicate the factor

change per year in the odds of having an abnormal result for each marker. There were no

significant changes for heart rate, Q‐wave and troponin tests.

There was a small but significant decrease in the odds of having abnormal SBP for men and

women. There was also a small but significant increase in the odds of ST elevation, but for

women only. The crude and age‐adjusted values were similar, with the age‐adjusted odds of ST

elevation increasing by a factor of 1.04 (95% CI 1.002‐1.07) per year for women.

44

There was an increase in the odds of having a positive CK‐MB result in both men and women.

The age‐adjusted ORs were 1.15 (95% CI 1.14‐1.16) and 1.18 (95% CI 1.17‐1.19) for men and

women respectively. There were significant increases in the odds of having an abnormal result

for CK for both men and women, with age‐adjusted ORs of 1.38 (95% CI 1.36‐1.39) and 1.21 (95%

CI 1.20‐1.23), respectively.

Table 4.8 Results from logistic regression analysis: Changes in ORs of other markers of AMI

severity associated with a 1 year increase in time

Variable Men N=5100

Women N=1238

Crude OR (95% CI)


Crude OR (95% CI)


Abnormal Heart Rate

1.00 (0.99, 1.01) p=0.982

1.00 (0.99, 1.01) p=0.974

1.01 (0.98, 1.03) p=0.585

1.01 (0.98, 1.03) p=0.566

Abnormal SBP

0.98 (0.96, 0.998) p=0.031

0.98 (0.96, 0.997) p=0.025

0.94 (0.91, 0.98) p=0.001

0.94 (0.91, 0.98) p=0.001

ST elevation

1.01 (0.99, 1.02) p=0.332

1.01 (0.99, 1.02) p=0.306

1.04 (1.003, 1.07) p=0.034

1.04 (1.002, 1.07) p=0.035

Q‐waves 1.01 (0.996, 1.03) p=0.154

1.01 (0.996, 1.03) p=0.139

1.02 (0.99, 1.06) p=0.248

1.02 (0.99,1.06)

p=0.248

Positive Troponin

1.02 (0.98, 1.04) p=0.734

1.02 (0.98, 1.04) p=0.745

1.01 (0.99, 1.03) p=0.524

1.01 (0.99, 1.03) p=0.546

Positive CK‐MB

1.16 (1.14, 1.17) p<0.001

1.15 (1.14, 1.16) p<0.001

1.18 (1.17, 1.19) p<0.001

1.18 (1.17, 1.19) p<0.001

Positive CK 1.38 (1.36, 1.39) p<0.001

1.38 (1.36, 1.39) p<0.001

1.21 (1.20, 1.23) p<0.001

1.21 (1.20, 1.23) p<0.001

OR = odds ratio; 95% CI = 95% confidence interval, ECG= Electrocardiogram, SBP = systolic blood

pressure, CK = Creatinine Kinase, MB = Muscle Brain

4.2 Trends in AMI Severity from 1984 to 2003 for Patients Aged 35‐64 Years

Hospitalised in Perth with Incident or Recurrent Definite AMI


In the cohort of patients aged 35‐64 years hospitalised in Perth with incident or recurrent

definite AMI between 1984 and 2003, mean ages were 54.3 years (SD 7.3 years) for men and

56.7 years (SD 6.6 years) for women. The IQR for men was 11.0 years, with a median of 56.0

years. The IQR for women was 9.0 years, with a median of 59.0 years. Women were 2.4 years

(95% CI 1.98‐2.79, p<0.001) older than men. The mean and median age for the cohort (men and

women together) was 55.1 years (SD 7.0 years) and 58.0 years respectively with an IQR of 9.0

years.

45


years prior to the date of diagnosis with incident or recurrent definite AMI, of diabetes, heart

failure, stroke, hypertension, CABG and use of digoxin. The majority of patients had no history

of diabetes, heart failure, stroke, hypertension, CABG or use of digoxin. The highest prevalence

for comorbidities was 23.2% for women with a history of hypertension in the last four years.

Except for history of CABG, percentages of comorbidities and interventions in men and women

with incident or recurrent definite AMI differed significantly, with women more likely to have a

history of these characteristics (Table 4.9).

Table 4.9 Patient characteristics for history of diabetes, heart failure, stroke, hypertension,

coronary artery bypass graft surgery and use of digoxin in the four years prior to their date

of diagnosis with incident or recurrent definite AMI


Men N=6156

Women N=1515

*P‐value

n (%) n (%)

Diabetes No 5853 (95.1) 1314 (86.7) Yes 303 (4.9) 201 (13.3) <0.001

Heart Failure No 5931 (96.3) 1429 (94.3) Yes 225 (3.7) 86 (5.7) <0.001

Stroke No 5990 (97.3) 1445 (95.4) Yes 166 (2.7) 70 (4.6) <0.001

Hypertension No 5386 (87.5) 1164 (76.8) Yes 770 (12.5) 351 (23.2) <0.001

CABG No 6069 (98.6) 1493 (98.5) Yes 87 (1.4) 22 (1.5) 0.493

Use of digoxin No 5486 (89.1) 1329 (87.7) Yes 168 (2.7) 59 (3.9) Missing 502 (8.2) 127 (8.4) 0. 012

*Chi‐squared test

CABG = Coronary Artery Bypass Graft, AMI=Acute Myocardial Infarction


This section contains the results of trend analysis for each of the following seven PREDICT

components: CCI, shock, clinical history, age, ECG severity, renal and heart failure.

46

Table 4.10 shows the frequency distribution of the PREDICT component points and how they

have been coded into binary variables. Heart failure and shock were the only components with

missing data.

Table 4.10 PREDICT component points, binary coding and frequency distribution from 1984

to 2003 for incident or recurrent definite AMI

PREDICT component

PREDICT points

Binary coding

Men N=6156

Women N=1515

Total N=7671

n (%) n (%) n (%)

CCI

Normal 0 0 0 0 0 Low 2 0 4301 (69.9) 895 (59.1) 5196 (67.7) Moderate 4 1 1337 (21.7) 386 (25.5) 1723 (22.5) Severe 6 1 518 (8.4) 234 (15.4) 752 (9.8)

Shock Normal 0 0 3729 (60.6) 820 (54.1) 4549 (59.3) Moderate 2 1 1629 (26.5) 439 (29.0) 2068 (27.0) Severe 4 1 797 (12.9) 256 (16.9) 1053 (13.7) Missing 1 (0.02) ‐ ‐

Clinical History Normal 0 0 4906 (79.7) 1084 (71.6) 5990 (78.1) Mild 1 1 1163 (18.9) 400 (26.4) 1563 (20.4) Moderate 2 1 87 (1.4) 31 (2.0) 118 (1.5)

Age 35‐59 years 0 0 4292 (69.7) 859 (56.7) 5151 (67.1) 60‐69 years 1 1 1864 (30.3) 656 (43.3) 2520 (32.9)

ECG severity No BBB/infarction

0 0 3333 (54.1) 755 (49.8) 4088 (53.3)

Mild 1 1 2527 (41.0) 691 (45.6) 3218 (42.0) Moderate 2 1 183 (3.0) 54 (3.6) 237 (3.1) Severe 3 1 113 (1.8) 15 (1.0) 128 (1.7)

Renal Failure No 0 0 6090 (98.9) 1470 (97.0) 7560 (98.6) Yes 1 1 66 (1.1) 45 (3.0) 111 (1.4)

Heart Failure No 0 0 5769 (93.7) 1354 (89.4) 7123 (92.9) Yes 1 1 185 (3.0) 99 (6.5) 284 (3.7) Missing 202 (3.3) 62 (4.1) 264 (3.4)

CCI = Charlson Co‐morbidity Index, AMI = Acute myocardial infarction, Clinical history as per the PREDICT

Tool = stroke, angina, coronary artery bypass graft surgery, cardiac arrest and hypertension, ECG=

Electrocardiogram, BBB = bundle branch block

Table 4.11 shows the crude and age‐adjusted ORs, with their 95% CIs, by gender from logistic

regression analyses of the components when they are considered as binary variables. Each OR

in Table 4.11 indicates the factor change in the odds of the higher severity category of the

PREDICT component associated with a 1 year increase in time.

47

There was a small but significant increase in Charlson score severity over the period from 1984

to 2003. The age‐adjusted odds of having high (moderate/severe) compared with normal/low

Charlson scores increased by a factor of 1.05 (95% CI 1.04‐1.07) per year for men and 1.08 (95%

CI 1.06‐1.11) for women.

There was a small but significant decrease in ECG severity over the period from 1984 to 2003.

The age‐adjusted odds of having mild or severe BBB or MI decreased – the factor change was

0.97 (95% CI 0.96‐0.98) per year for men and 0.96 (95% CI 0.94‐0.99) for women.

There was a small but significant decline in severity of shock over the period from 1984 to 2003

for men only, with an age‐adjusted factor change of 0.98 (95% CI 0.97‐0.99) per year. There was

a significant increase in renal failure severity over the study period for both men and women.

The age‐adjusted odds increased by a factor of 1.11 (95% CI 1.06‐1.17) per year for women, and

a similar amount for men (OR 1.11, 95% CI 1.06‐1.16).


men and women for the PREDICT components of Charlson score, shock, clinical history, age,

ECG severity, renal and heart failure

PREDICT component

Men N=6156

Women N=1515

Crude OR (95% CI)


Crude OR (95% CI)


CCI 1.05 (1.04, 1.06) p<0.001

1.05 (1.04, 1.07) p<0.001

1.08 (1.05, 1.10) p<0.001

1.08 (1.06, 1.11) p<0.001

Shock 0.98 (0.97, 0.995) p=0.06

0.98 (0.97,0.99) p=0.007

1.01 (0.99, 1.03) p=0.50

1.01 (0.99, 1.03) p=0.50

Clinical history 1.01 (0.99, 1.02) p=0.28

1.01 (0.99, 1.02) p=0.23

1.01 (0.99, 1.04) p=0.27

1.02 (0.99, 1.04) p=0.21

Age 0.99 (0.98, 1.01) p=0.38

‐ 0.99 (0.97, 1.02) p=0.51

‐

ECG severity 0.97 (0.95, 0.98) p<0.001

0.97 (0.96, 0.98) p<0.001

0.96 (0.94, 0.98) p=0.001

0.96 (0.94, 0.99) p=0.001

Renal failure 1.11 (1.06, 1.16) p<0.001

1.11 (1.06, 1.16) p<0.001

1.11 (1.06, 1.18) p<0.001

1.11 (1.06, 1.17) p<0.001

Heart failure 0.99 (0.96, 1.03) p=0.81

0.99 (0.96, 1.04) p=0.90

1.01 (0.96, 1.06) p=0.78

1.01 (0.96, 1.06) p=0.72

CCI = Charlson Co‐morbidity Index, Clinical history as per the PREDICT Tool = stroke, angina, coronary


interval, ECG= Electrocardiogram

Table 4.12 shows the results of ordinal regression analyses for the four components that have

more than two PREDICT point categories: Charlson score, shock, clinical history and ECG severity.

48

Each OR in this table indicates the factor change in the odds of the highest severity of PREDICT

component associated with a 1 year increase in time.

These results were similar to those from the logistic regression with the age‐adjusted odds of

having a severe Charlson PREDICT score increasing by a factor of 1.06 (95% CI 1.04‐1.07) per

year for men and 1.08 (95% CI 1.06‐1.11) for women. Likewise, the age‐adjusted odds of having

a severe ECG PREDICT score decreased by a factor of 0.97 (95% CI 0.96‐0.98) per year for men,

and 0.97 (95% CI 0.95‐0.99) for women.

Table 4.12 Crude and age‐adjusted odds ratios from ordinal regression for variables with more

than two PREDICT categories.

PREDICT component

Men N=6156

Women N=1515

Crude OR (95% CI)


Crude OR (95% CI)


CCI 1.05 (1.04,1.07) p<0.001

1.06 (1.04,1.07) p<0.01

1.08 (1.06,1.11) p<0.01

1.08 (1.06,1.11) p<0.001

Shock 0.99 (0.97,0.997) p=0.016

0.99 (0.97,0.997) p=0.017

1.00 (0.98,1.02) p=0.887

1.00 (0.98,1.02) p=0.895

Clinical History

1.01 (0.99,1.02) p=0.236

1.02 (0.995,1.02) p=0.189

1.03 (0.99,1.04) p=0.192

1.02 (0.99,1.04) p=0.144

ECG severity 0.97 (0.96,0.98) p<0.001

0.97 (0.96,0.98) p<0.001

0.97 (0.95,0.99) p=0.003

0.97 (0.95,0.99) p=0.005



interval, ECG= Electrocardiogram


Table 4.13 presents the frequencies of patients aged 35‐64 years hospitalised in Perth with

incident or recurrent definite AMI aggregated over the study period from 1984 to 2003 for each

value of the PREDICT Final Score. A higher score indicates a greater severity of AMI. The

frequency distribution shows that the majority of men and women have lower rather than

higher PREDICT Final Scores. The modal score is 3 for men with 18.9%, and thereafter

frequencies decrease consistently as scores increase. A similar pattern is seen in the women’s

scores, with 13.3% having a modal score of 3.

49


from 1984 to 2003 for patients with incident or recurrent definite AMI

PREDICT

Final Score

Males

N=6156

Females

N=1515

Total

N=7671

n (%) n (%) n (%)

2 1056 (17.2) 147 (9.7) 1203 (15.7)

3 1161 (18.9) 232 (15.3) 1393 (18.2)

4 942 (15.3) 208 (13.7) 1150 (15.0)

5 793 (12.9) 197 (13.0) 990 (12.9)

6 630 (10.2) 174 (11.5) 804 (10.5)

7 474 (7.7) 129 (8.5) 603 (7.9)

8 305 (5.0) 116 (7.7) 421 (5.5)

9 229 (3.7) 84 (5.5) 313 (4.1)

10 149 (2.4) 59 (3.9) 208 (2.7)

11 106 (1.7) 49 (3.2) 155 (2.0)

12 53 (0.9) 30 (2.0) 83 (1.1)

13 33 (0.5) 19 (1.3) 52 (0.7)

14 16 (0.3) 6 (0.4) 22 (0.3)

15 4 (0.1) 3 (0.2) 7 (0.1)

16 2 (0.03) 0 (0.0) 2 (0.03)

Missing 203 (3.3) 62 (4.1) 265 (3.5)

Percentages may not sum to 100% due to rounding. AMI=Acute Myocardial Infarction

Figure 2 displays the mean PREDICT Final Score for males and females with incident and

recurrent definite AMI from the years 1984 to 2003. In 1984, the mean PREDICT Final Score was

4.59 and 4.94 for males and females respectively. In 2003, the mean PREDICT Final Score was

lower in males by 0.03 at a score of 4.56. The mean PREDICT Final Score in females increased to

6.27. The mean PREDICT final scores are similar to Figure 1. The relatively small proportion of

recurrent cases did not have marked changes in the trends, although the means are slightly

higher in Figure 2.

50

Figure 2: Graph of the mean PREDICT Final Score for males and females with incident and recurrent definite AMI over the period from 1984 to 2003

There were no values available for the mean PREDICT Final Score for females with incident and

recurrent definite AMI in year 1997. AMI = Acute Myocardial Infarction


that estimate the changes in mean PREDICT Final scores with a 1 year increase in time over the

period from 1984 to 2003. There were no significant changes per year in the mean PREDICT final

score for men. However, there was a significant change in the mean PREDICT Final score for

women with an age‐adjusted increase of 0.07 (95% CI 0.01‐0.08) points per year.

0

1

2

3

4

5

6

7

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

MEA

N PRED

ICT FINAL SCORE

YEAR

Male Female

51

Table 4.14 Coefficients (β) and 95% CIs from linear regression analyses of the PREDICT final

score in patients with incident or recurrent definite AMI from 1984 to 2003

Variable Men

N=6156

Women

N=1515

β

(95% CI)

Age‐adjusted β

(95% CI)

β

(95% CI)

Age‐adjusted β

(95% CI)

PREDICT

final score

0.01 (‐0.01, 0.03)

p=0.180

0.02 (‐0.001, 0.03)

p=0.059

0.04 (0.01, 0.07)

p=0.024

0.07 (0.01, 0.08)

p=0.009

β is the coefficient that indicates the change in mean PREDICT final score per year,

95% CI = 95% confidence interval, AMI=Acute Myocardial Infarction

Table 4.15 shows the score categories and frequency distribution from 1984 to 2003 of other

common markers of AMI severity: heart rate, SBP, ST elevation, Q‐waves, troponin tests, CK‐MB

and CK tests. There were no significant differences in the proportion of each marker between

men and women.

52

Table 4.15 Categories of other markers of AMI severity and frequency distribution from 1984

to 2003 in patients aged 35‐64 with incident or recurrent definite AMI


MenN=6156

WomenN=1515

Total N=7671

n (%) n (%) n (%)

Abnormal Heart Rate

No 4246 (69.0) 974 (64.3) 5220 (68.0) Yes 1910 (31.0) 541 (35.7) 2451 (32.0)

Abnormal SBP No 595 (9.7) 176 (11.6) 771 (10.1) Yes 5561 (90.3) 1339 (88.4) 6900 (89.9)

ST Elevation No 2998 (48.7) 622 (41.1) 3620 (47.2) Yes 2573 (41.8) 720 (47.5) 3293 (42.9) Missing 585 (9.5) 173 (11.4) 758 (9.9)

Q‐waves No 2378 (38.6) 472 (31.2) 2850 (37.2) Yes 3279 (53.3) 895 (59.1) 4174 (54.4) Missing 499 (8.1) 148 (9.8) 647 (8.4)

Positive Troponin

No 3(0.0) 3 (0.2) 6 (0.1) Yes 167 (2.8) 147 (3.1) 214 (2.8) Missing 5986 (97.2) 1465 (96.7) 7451 (97.1)

Positive CK‐MB No 1849 (30.0) 459 (30.3) 2308 (30.1) Yes 3804 (61.8) 919 (60.7) 4673 (61.6) Missing 503 (8.2) 137 (9.0) 640 (8.3)

Positive CK No 486 (7.9) 229 (15.1) 713 (9.3) Yes 5621 (91.3) 1279(84.4) 6904 (90.0) Missing 49 (0.8) 7 (0.5) 54 (0.7) AMI = Acute myocardial infarction, SBP = systolic blood pressure; CK‐MB = MB isoenzyme fraction of

creatine kinase, CK=Creatinine Kinase.


logistic regression analysis of the other components of AMI severity. The ORs indicate the factor

change per year in the odds of having an abnormal result for the following components: heart

rate, SBP, ST elevation, Q‐waves, troponin tests, CK‐MB tests and CK tests. There were no

significant changes for heart rate, ST elevation, Q‐wave components and positive troponin test

results.

There was a small but statistically significant decrease in the odds of having abnormal SBP for

men and women. The crude and age‐adjusted values were similar, with the age‐adjusted odds

53

changing by factors of 0.98 (95% CI 0.96‐0.997) and 0.94 (95% CI 0.91‐0.97) per year for men

and women respectively.

There was a significant increase in the odds of having a positive CK‐MB test in both men and

women. The age‐adjusted odds changed by factors of 1.16 (95% CI 1.15‐1.18) and 1.19 (95% CI

1.17‐1.20) per year for men and women, respectively. There were significant increases in the

odds of having an abnormal result for CK for both men and women, with age‐adjusted odds

changing by factors of 1.21 (95% CI 1.19‐1.22) and 1.19 (95% CI 1.18‐1.21), respectively.

Table 4.16 Results from logistic regression analysis of other indicators of AMI severity in

patients with incident or recurrent definite AMI, 1984‐2003

Variable Men N=6156

Women N=1515

Crude OR (95% CI)


Crude OR (95% CI)


Heart Rate 1.00 (0.99, 1.01) p=0.784

1.00 (0.99, 1.01) p=0.800

1.00 (0.98, 1.03) p=0.829

1.00 (0.98, 1.03) p=0.814

Abnormal SBP

0.98 (0.96, 0.996) p=0.017

0.98 (0.96, 0.997) p=0.020

0.94 (0.91, 0.97) p<0.001

0.94 (0.91, 0.97) p<0.001

ST elevation

1.01 (0.995, 1.02) p=0.196

1.01 (0.996, 1.02) p=0.177

1.02 (0.99, 1.05) p=0.142

1.02 (0.99, 1.05) p=0.142

Q‐waves 1.01 (0.998, 1.03) p=0.087

1.02 (0.999, 1.03) p=0.079

1.01 (0.98, 1.04) p=0.595

1.02 (0.98, 1.04) p=0.595

Positive Troponin

1.02 (0.98, 1.04) p=0.894

1.02 (0.98, 1.04) p=0.845

1.03 (0.99,1.05) p=0.892

1.03 (0.99, 1.05) p=0.896

CK‐MB 1.16 (1.15, 1.18) p<0.001

1.16 (1.15, 1.18) p<0.001

1.18 (1.16, 1.20) p<0.001

1.19 (1.17, 1.20) p<0.001

CK 1.21 (1.19, 1.22) p<0.001

1.21 (1.19, 1.22) p<0.001

1.19 (1.1.8, 1.21) p<0.001

1.19 (1.18, 1.21) p<0.001

OR = odds ratio; 95% CI = 95% confidence interval, SBP = systolic blood pressure; CK‐MB = MB isoenzyme

fraction of creatine kinase, CK= Creatinine Kinase, AMI=Acute Myocardial Infarction

4.2.5 PREDICT components and other markers of AMI severity

Table 4.17 presents the p‐values from regression models that tested whether there was an

interaction between trend (year) and case type (incident or recurrent). It also shows the p‐values

for testing whether case type had a main effect, in the absence of any significant interactions.

54

The results showed no significant interactions between trends and the case type (incident or

recurrent) did not have a significant effect on severity.

Table 4.17 P‐values for analysis of trends of markers of AMI severity with respect to incident

and recurrent case types

Variable P‐value for interactiona P‐value for case typeb

CCI 0.82 0.76 Shock 0.09 0.11 Clinical history 0.32 0.45 Age 0.09 0.08 ECG severity 0.41 0.62 Renal failure 0.98 0.87 Heart failure 0.23 0.31 Heart rate 0.82 0.75 Abnormal SBP 0.06 0.06 ST elevation 0.21 0.19 Q‐waves 0.09 0.07 Positive troponin 0.89 0.76 CK‐MB 0.07 0.06 CK 0.05 0.07 PREDICT final score 0.27 0.29

a: P‐value for testing whether the coefficient of the interaction term for year and case type was equal to

zero in model that included year, age, case type and year x case type interaction term as independent

variables.

b: P‐value for testing whether the coefficient of the case type variable was equal to zero in model that

included the year, age and case type as independent variables.

4.3 Trends in AMI Severity between the two years, 1998 and 2003, for Patients Aged

35‐79 years Hospitalised in Perth with Incident or Recurrent Definite AMI


In the cohort of patients aged 35‐79 years and hospitalised in Perth with incident or recurrent

definite AMI for the years 1998 and 2003, the mean age for men was 63.6 years (SD 10.9 years)

in 1998 and 65.3 years (SD 10.9 years) in 2003 and for women it was 67.7 years (SD 10.1 years)

in 1998 and 69.0 years (SD 9.1 years) in 2003. In 1998, the IQR for men was 17.0 years, with a

median of 64.0 years. In 1998, the IQR for women was 13.0 years, with a median of 71.0 years.

In 2003, the IQR for men was 16.3 years, with a median of 68.0 years. In 2003, the IQR for women

was 12.0 years, with a median of 71.0 years. On average, men were 2.3 years (95% CI 1.80‐2.70;

p<0.001) younger than women for the years 1998 and 2003. The mean and median age for the

55

cohort (men and women together) was 64.9 years (SD 10.6 years) and 71.0 years respectively

with an IQR of 15.0 years.


years prior to the date of diagnosis with incident or recurrent definite AMI, of diabetes, heart

failure, stroke and hypertension. The majority of patients had no history of diabetes, heart

failure, stroke and hypertension. Men had a lower proportion with a history of hypertension in

2003 than in 1998, whilst women had a higher proportion with a history of diabetes in 2003 than

in 1998.


Table 4.18 Subject characteristics of history of diabetes, heart failure, stroke and hypertension

in the four years prior to their date of diagnosis with incident or recurrent definite AMI

*Chi‐squared test, AMI=Acute Myocardial Infarction

This section contains the frequency distributions for each of the following seven PREDICT

components: CCI, shock, clinical history, age, ECG severity, renal and heart failure; and presents

the results of trend analysis.


Men Women

1998 N=823 n (%)

2003 N=510 n (%)

*P‐value

1998 N=350 n (%)

2003 N=239 n (%)

*P‐value

Diabetes No 702 (85.3) 419 (82.2) 0.128 281 (80.3) 173 (72.4) 0.025 Yes 121 (14.7) 91 (17.8) 69 (19.7) 66 (27.6)

Heart failure No 737 (89.6) 440 (86.3) 0.071 297 (84.9) 197 (82.4) 0.431 Yes 86 (10.4) 70 (13.7) 53 (15.1) 42 (17.6)

Stroke No 747 (90.8) 475 (93.1) 0.128 324 (92.6) 211 (88.3) 0.077 Yes 76 (9.2) 35 (6.9) 26 (7.4) 28 (11.7)

Hypertension No 581 (70.6) 400 (78.4) 0.002 203 (58.0) 157 (65.7) 0.060 Yes 242 (29.4) 110 (21.6) 147 (42.0) 82 (34.3)

56

Table 4.19 PREDICT component points for 1998 and 2003, including their binary coding and

frequency distribution

PREDICT component

PREDICT points

Binary coding

Men 1998

2003

Women 1998

2003

N=823 N=510 N=350 N=239 n (%) n (%) n (%) n (%)

CCI

Normal 0 0 0 0 0 0

Low 2 0 351 (42.6) 222 (43.5) 115 (32.9) 73 (30.5)

Moderate 4 1 200 (24.3) 123 (24.1) 114 (32.6) 62 (25.9)

Severe 6 1 272 (33.0) 165 (32.4) 121 (34.6) 104 (43.5)

Shock

Normal 0 0 427 (51.9) 303 (59.4) 165 (47.1) 109 (45.6)

Moderate 2 1 227 (27.6) 150 (29.4) 109 (31.1) 89 (37.2)

Severe 4 1 168 (20.4) 57 (11.2) 76 (21.7) 40 (16.7)

Missing 1 (0.1) ‐ ‐ 1 (0.4)

Clinical history

Normal 0 0 519 (63.1) 359 (70.4) 183 (52.3) 144 (60.3)

Mild 1 1 304 (36.9) 151 (29.6) 167 (47.7) 95 (39.7)

Age 35‐59 years 0 0 290 (35.2) 148 (29.0) 66 (18.9) 41 (17.2)

60‐69 years 1 1 219 (26.6) 137 (26.9) 88 (25.1) 54 (22.6)

70‐79 years 3 1 172 (20.9) 98 (19.2) 83 (23.7) 55 (23.0)

Missing 142 (17.3) 127 (24.9) 113 (32.3) 89 (37.2)

ECG severity

No BBB/infarction

0 0 535 (65.0) 265 (52.0) 207 (59.1) 123 (51.5)

Mild 1 1 256 (31.1) 188 (36.9) 120 (34.3) 87 (36.4)

Moderate 2 1 24 (2.9) 33 (6.5) 20 (5.7) 23 (9.6)

Severe 3 1 8 (1.0) 24 (4.7) 3 (0.9) 6 (2.5)

Renal failure

No 0 0 767 (93.2) 468 (91.8) 321 (91.7) 201 (84.1)

Yes 1 1 56 (6.8) 42 (8.2) 29 (8.3) 38 (15.9)

Heart failure No 0 0 674 (81.9) 219 (42.9) 282 (80.6) 89 (37.2)

Yes 1 1 49 (6.0) 26 (5.1) 25 (7.1) 7 (2.9)

Missing ‐ ‐ 100 (12.2) 265 (52.0) 43 (12.3) 143 (59.8)


artery bypass graft surgery, cardiac arrest and hypertension, ECG= Electrocardiogram, BBB = bundle

branch block.

Table 4.20 shows the crude and age‐adjusted ORs, with their 95% CIs, by gender from binary

logistic regression analyses. Each OR in the table compares the odds of having the higher

severity category of the PREDICT component in 2003 to the odds in 1998; that is, the OR is the

factor change in the odds of the higher severity category of the PREDICT component associated

with a 5 year increase in time.

57


men and women for the PREDICT components in 1998 and 2003 for patients with incident or

recurrent definite AMI

Variable Men N=1333

Women N=589

Crude OR (95% CI)


Crude OR (95% CI)


CCI 0.97 (0.77, 1.21) p=0.75

0.84 (0.66, 1.06) p=0.14

1.11 (0.78, 1.59) p=0.55

1.06 (0.74, 1.52) p=0.74

Shock 0.74 (0.59,0.92) p=0.007

0.68 (0.54, 0.85) p=0.001

1.06 (0.77, 1.48) p=0.71

1.04 (0.75, 1.45) p=0.81

Clinical history

0.72 (0.57, 0.91) p=0.006

0.65 (0.51, 0.83) p<0.001

0.72 (0.52, 1.01) p=0.056

0.69 (0.49, 0.97) p=0.032

Age 1.33 (1.05, 1.69) p=0.019

‐ 1.12 (0.73, 1.73) p=0.59

‐

ECG severity

1.72 (1.37,2.15) p<0.001

1.67 (1.33, 2.09) p<0.001

1.37 (0.98, 1.90) p=0.06

1.34 (0.96, 1.87) p=0.08

Renal failure

1.23 (0.81,1.86) p=0.33

1.12 (0.73, 1.71) p=0.60

2.09 (1.25, 3.50) p=0.005

2.07 (1.24, 3.47) p=0.006

Heart failure

0.61 (0.37, 1.01) p=0.054

1.21 (0.86, 1.71) p= 0.28

1.13 (0.47, 1.27) p=0.78

1.13 (0.72, 1.77) p=0.60



interval, ECG= Electrocardiogram, AMI=Acute Myocardial Infarction

There was a significant decrease in shock severity in men from 1998 to 2003. In men, the age‐

adjusted odds of having high (moderate or severe) shock changed by a factor of 0.68 (95% CI

0.54‐0.85) over the 5 year period between 1998 and 2003. This is equivalent to an annual change

of 0.93 (95% CI 0.88‐0.97). There was also a decrease in the odds of having a mild clinical history

in men and women. The age‐adjusted odds of having a mild clinical history changed by a factor

of 0.65 (95% CI 0.51‐0.83) between 1998 and 2003 in men and by 0.69 (95% CI 0.49‐0.97) in

women between 1998 and 2003.

There was a significant increase in crude and age‐adjusted odds of ECG severity in men, but not

women, from 1998 to 2003. The factor change in age‐adjusted odds of having mild or severe

BBB or MI in men was 1.67 (95% CI 1.33‐2.09) between 1998 and 2003.

There was a two‐fold increase in renal failure in women from 1998 to 2003, but no change in

men. In women, the age‐adjusted odds of having a history of renal failure changed by a factor

of 2.07 (95% CI 1.24‐3.47) between 1998 and 2003.

58


Table 4.21 presents the frequencies aggregated over 1998 and 2003 of patients aged 35‐79 years

for each value of the PREDICT final score. A higher score indicates a greater severity of AMI. The

frequency distribution shows that the majority of men and women have lower rather than

higher PREDICT Final Scores. The modal score is 2 for men with frequency 20.1%, and thereafter

frequencies decrease consistently as scores increase. A similar pattern is seen in women’s scores,

except that their modal score was 3 with frequency 16.8%.

59

Table 4.21 Frequency distribution of PREDICT Final Scores aggregated for years 1998 and

2003

PREDICT

Final Score

Men Women

1998

N=823

n (%)

2003

N=510

n (%)

1998

N=350

n (%)

2003

N=239

n (%)

2 63 (7.7)

78 (9.5)

56 (6.8)

63 (7.7)

58 (7.0)

47 (5.7)

43 (5.2)

48 (5.8)

35 (4.3)

31 (3.8)

29 (3.5)

20 (2.4)

18 (2.2)

8 (1.0)

3 (0.4)

1 (0.1)

38 (7.5) 8 (2.3) 8 (3.3)

3 30 (5.9) 12 (3.4) 3 (1.3)

4 27 (5.3) 21 (6.0) 6 (2.5)

5 28 (5.5) 27 (7.7) 10 (4.2)

6 26 (5.1) 21 (6.0) 3 (1.3)

7 8 (1.6) 21 (6.0) 10 (4.2)

8 11 (2.2) 16 (4.6) 7 (2.9)

9 10 (2.0) 15 (4.3) 5 (2.1)

10 10 (2.0) 17 (4.9) 3 (1.3)

11 7 (1.4) 13 (3.7) 4 (1.7)

12 8 (1.6) 14 (4.0) 3 (1.3)

13 5 (1.0) 9 (2.6) 3 (1.3)

14 ‐ 7 (2.0) 1 (0.4)

15 2 (0.4) 5 (1.4) ‐

16 1 (0.2) 3 (0.9) 2 (0.8)

17 ‐ 3 (0.9) ‐

18 1 (0.1) ‐ ‐ ‐ Missing 221 (26.9) 299 (58.6) 138 (39.4) 171 (71.5)


that estimate the changes in mean PREDICT Final scores from 1998 to 2003. After adjustment

for age, there was a small but significant decrease of 0.10 (95% CI 0.17, 0.03) over the 5 year

period in the mean PREDICT final score for men. This is equivalent to an annual decrease of 0.02

(95% CI 0.03, 0.06). For women, there was no significant change.

60


score in 1998 and 2003 for patients with incident or recurrent definite AMI

Variable Men

N=1333

Women

N=589

β

(95% CI)

Age‐adjusted β

(95% CI)

β

(95% CI)

Age‐adjusted β

(95% CI)

PREDICT

final score

‐0.07 (‐0.16, 0.02)

p=0.11

‐0.10 (‐0.17, ‐0.03)

p=0.005

0.07 (‐0.07, 0.21)

p=0.34

0.04 (‐0.09, 0.17)

p=0.52

β is the coefficient that indicates the change in mean PREDICT final score per year.

95% CI = 95% confidence interval, AMI= Acute Myocardial Infarction


Table 4.23 shows the score categories and frequency distribution for the years 1998 and 2003

for other common markers of AMI severity: heart rate, SBP, ST elevation, Q‐waves, troponin

test results and CK‐MB values.

61

Table 4.23 Categories of other markers of AMI severity and frequency distribution for the

years 1998 and 2003

CK‐MB negative values (0) available for 2003. No positive Q‐waves (0) available for 2003. AMI =

Acute myocardial infarction, SBP = Systolic blood pressure, CK‐MB = MB isoenzyme fraction of

creatine kinase, MB = Muscle Brain


logistic regression analysis of the other components of AMI severity. The ORs indicate the factor

change from 1998 to 2003 in the odds of having an abnormal result for the following

components: heart rate, SBP, ST elevation, Q‐waves and troponin test results. There were no

significant changes for abnormal SBP, ST elevation, Q‐wave and CK‐MB values. However, heart


Men1998 N=823

2003 N=510

Women 1998 N=350

2003 N=239

n (%) n (%) n (%) n (%)

Abnormal Heart Rate

No 501 (60.9) 338 (66.3) 197 (56.3) 140 (58.6) Yes 322 (39.1) 172 (33.7) 153 (43.7) 99 (41.4)

Abnormal SBP No 133 (16.2) 100 (19.6) 48 (13.7) 55 (23.0) Yes 690 (83.8) 410 (80.4) 302 (86.3) 184 (77.0)

ST Elevation No 246 (29.9) 393 (77.1) 96(27.4) 202 (84.5) Yes 363 (44.1) 117 (22.9) 159 (45.4) 37 (15.5) Missing 214 (26.0) ‐ 95 (27.1) ‐

Q‐waves No 217 (26.4) 124 (24.3) 73 (28.3) 41 (17.2) Yes 404 (49.1) 0 (0.0) 185 (71.7) 0 (0.0) Missing 202 (24.5) 386 (75.7) 92 (26.3) 198 (82.8)

Positive Troponin

No 1 (0.1) 6 (1.2) 2 (0.6) 1 (0.4) Yes 117 (14.2) 253 (49.6) 34 (9.7) 127 (53.2) Missing 705 (85.7) 251 (49.2) 314 (89.7) 111 (46.4)

Positive CK‐MB No 374 (45.4) 0 (0.0) 148 (42.3) 0 (0.0) Yes 281 (34.2) 8 (1.6) 141 (40.3) 2 (0.8) Missing 168 (20.4) 502 (98.4) 61 (17.4) 237 (99.2)

Positive CK No 86 (10.4) 95 (18.7) 57 (16.3) 79 (33.2) Yes 703 (85.5) 380 (74.4) 281 (80.3) 144(60.1) Missing 34 (4.1) 35 (6.9) 12 (3.4) 16 (6.7)

62

rate decreased significantly by a factor of 0.75 (95% CI 0.59‐0.94) in men from 1998 to 2003,

whilst there was no change in women. There was a significant increase in the odds of having a

positive troponin in both men and women. The age‐adjusted ORs of have a positive troponin

were 1.04 (95% CI 1.02‐1.05) and 1.03 (95% CI 1.02‐1.05) for men and women respectively. The

age‐adjusted odds of having an abnormal CK result for both men and women increased by

factors of 1.05 (95% CI 1.04‐1.06) and 1.07 (95% CI 1.02‐1.10) respectively over the 5 year period.


men and women for other indicators of AMI severity in 1998 and 2003 for patients with

incident or recurrent definite AMI

Variable Men N=1333

Women N=589

Crude OR (95% CI)


Crude OR (95% CI)


Heart Rate

0.79 (0.63, 0.99)p=0.047

0.75 (0.59, 0.94) p=0.015

0.91 (0.65, 1.27) p=0.58

0.88 (0.63, 1.24) p=0.47

Abnormal SBP

0.79 (0.59, 1.05)p=0.11

0.83 (0.62, 1.11) p=0.19

0.53 (0.35, 0.82) p=0.004

0.52 (0.34, 0.80) p=0.003

ST elevation

1.04 (0.89, 1.18)p=0.99

1.02 (1.02, 1.05) p=0.99

1.03 (0.92, 1.38) p=0.99

0.99 (0.97, 1.02) p=0.87

Q‐waves 1.61 (0.99, 1.83)p=0.99

1.01 (0.99, 1.03) p=0.21

1.03 (0.96,1.04) p=0.99

0.99 (0.97, 1.03) p=0.82

Positive Troponin

1.04 (1.02, 1.05)p<0.001

1.04 (1.02, 1.05) p<0.001

1.03 (1.02, 1.05) p<0.001

1.03 (1.02, 1.05) p=0.001

Positive CK‐MB

‐ ‐ ‐ ‐

Positive CK

1.05 (1.04, 1.06)p<0.001

1.05 (1.04, 1.06) p<0.001

1.06 (1.03, 1.09) p<0.001

1.07 (1.02, 1.10) p<0.001

OR = Odds ratio, 95% CI = 95% confidence interval, SBP = systolic blood pressure, CK‐MB = MB isoenzyme

fraction of creatine kinase, MB = Muscle Brain, CK= Creatinine Kinase, AMI= Acute Myocardial Infarction

4.3.5 PREDICT components and other markers of AMI severity

Table 4.25 presents the p‐values from regression models that tested whether there was an

interaction between trend (year) and age group (35‐64 years or older: 65‐79 years). It also shows

the p‐values for testing whether, in the absence of significant interaction, age group had a main

effect. The results showed no significant interactions between year and whether or not the

patient was in the younger or older age group, nor did age group have a significant main effect

63

on severity in the absence of an interaction. That is, the trends for patients aged 35‐64 years

and 65‐79 years can be considered to be the same.

Table 4.25 P‐values for analysis of trends of markers of AMI severity with respect to younger

(age 35 to 64 years) and older (age 65‐79 years) age groups

Variable P‐value for interactiona

P‐value for case typeb

CCI 0.38 0.33 Shock 0.93 0.95 Clinical history 0.52 0.48 ECG severity 0.06 0.07 Renal failure 0.35 0.38 Heart failure 0.48 0.47 Heart rate 0.54 0.56 Abnormal SBP 0.87 0.84 ST elevation 0.07 0.08 Q‐waves 0.06 0.06 Positive troponin 0.21 0.19 CK‐MB 0.36 0.34 CK 0.13 0.11 PREDICT final score 0.54 0.49

a: P‐value for testing whether the coefficient of the year: case type variable was equal to zero in model

that included the year, age, case type and year x case type interaction as independent variables.

b: P‐value for testing whether the coefficient of the case type variable was equal to zero in model that

included the year, age and case type as independent variables.

4.4 Summary

This chapter presented the subject characteristics, frequency distributions and trend analysis of

the various markers of AMI for the three cohorts. The results in trends of AMI severity for the

study period were mixed, with contrasting results in markers of AMI severity. There were no

specific gender or age findings of note. Chapter five provides a discussion of the results from

these analyses, in the context of published literature on similar studies.

64

Chapter Five: Discussion

5.1 Introduction

This chapter presents the research findings and discusses the practical implications for Public

Health measures. The research findings are discussed as per the three research objectives: first

is to consider the findings for the trends in AMI severity from 1984 to 2003 for patients aged 35‐

64 years hospitalised in Perth with incident definite AMI. Next, to examine the results of trends

in AMI severity from 1984 to 2003 for patients aged 35‐64 years hospitalised in Perth with

incident and recurrent definite AMI. Lastly, the trends in AMI severity from 1998 and 2003 for

patients aged 35‐79 years hospitalised in Perth with incident and recurrent definite AMI are

discussed. The strengths and limitations of the thesis are also discussed. The chapter concludes

with suggestions for future research.

5.2 Research Findings and Significance of Thesis

5.2.1 PREDICT Components and Final Score

For PREDICT components, trends in incident definite AMI, and incident or recurrent definite AMI,

from 1984 to 2003 for patients aged 35‐64 hospitalised in Perth, there was a small increase in

Charlson comorbidity severity. There was an increase in the prevalence of renal failure over time

and a small increase in clinical history. There was a small but significant decrease in ECG severity

and a decrease in shock severity.

For patients with incident and recurrent definite AMI for patients aged 35‐79 years, from 1998

to 2003, there was an increase in the crude and age‐adjusted odds of ECG severity in men. There

was a small two‐fold increase in renal failure in women from 1998 to 3002, but there was no

change in men and a small increase in clinical history for men. There was a decrease in the

severity of shock in men and a decrease in the odds of having a mild clinical history of co‐

morbidities for men.

With regards to the PREDICT Final Score, for patients with incident definite AMI there was a

small increase per year in the mean PREDICT final score for men and for women. The increase

in the score for women was approximately double that of men. For patients with incident and

recurrent definite AMI aged 35‐64 years, there was an increase in the score for women, but

there were no significant changes per year in the mean score for men. For patients with incident

65

and recurrent definite AMI patients aged 35‐79 years hospitalised from 1998 to 2003, there was

a small decrease over the 5 year period in the score for men but there was no significant change

for women.


With regards to other markers of AMI Severity, for patients with incident definite AMI aged 35‐

64 years, there was a small increase in the odds in ST elevation for women only. For patients

with incident or recurrent definite AMI for the same age group, there was an increase in the

odds of having a positive CK‐MB test and an abnormal result for CK in both men and women.

Both groups had a small decrease in the odds of having abnormal SBP for men and women and

there were no changes for the components of heart rate, ST elevation, Q‐wave components and

troponin test results. For patients with incident and recurrent definite AMI aged 35‐79 years,

from 1998 to 2003, there was an increase in the odds of having a positive troponin in both men

and women. The age‐adjusted odds, over a period of 5 years, of having an abnormal CK result

for both men and women were, 1.05 (95% CI 1.04‐1.06) and 1.07 (95% CI 1.02‐1.10) respectively.

However, heart rate decreased significantly in men, whilst there was no change in women. There

were no significant change for abnormal SBP, ST elevation, Q‐wave components and CK‐MB

values.

5.2.3 Relevance of research findings to published literature

The findings of a decrease in trends in ECG severity in all the cohorts, and opposing changes in

haemodynamic risk factors (for e.g., shock and heart rate) were consistent with the findings

from the study by Hellermann et al. [74] That study looked at markers of MI severity including

Killip class, ECG, and peak CK values in a population‐based, MI incident cohort to test the premise

that the severity of MI declined over time. The study period was from 1983 to 1994, with 1,295

incident cases of MI (mean age of 67 years, SD of 6 years; 43% women) and was based in Olmsted

County, Minnesota. The results showed that there were no significant variations in trends in

hemodynamic presentation of patients. However, there was a decrease in the number of people

with ST‐segment elevation in addition to the incidence of Q waves and highest CK values. These

trends, pointed to a decrease in the severity of MI over time.

The findings by Roger et al. are important to note as it includes data analysis, incorporating the

introduction of cTn which was similar to our study [101]. The study consisted of 2816 patients

66

hospitalized with incident MI from 1987 to 2006 in Olmsted County, Minnesota. It involved

prospective measurements of cTn and CK‐MB from August 2000 onwards. Outcomes were MI

incidence, severity, and survival. After cTn was introduced, 278 (25%) of 1127 incident MI met

only cTn‐based criteria. When cases meeting only cTn criteria were included, incidence did not

change between 1987 and 2006. When restricted to cases defined by CK/CK‐MB, the incidence

of MI declined by 20%. The incidence of non‐ST‐segment elevation MI increased markedly by

relying on cTn, whereas that of ST‐segment elevation MI declined regardless of cTn. The results

from this thesis add to the findings from previous reports that document a significant rise in the

incidence of MI paralleled to what would have been identified with the use of criteria from the

WHO [102] and ARIC study. [62] The results from this thesis also adds to the report from the

earlier Framingham Heart Study of 1960–1999 in which the proportions of MI diagnosed by ECG

only dropped by 50%, while proportions of MI diagnosed by biomarkers doubled. [103] This

highlights the need to consider the differing sensitivity and reliability of markers of diagnostic

tools of MI when interpreting the trends in markers of AMI severity. There needs to be pragmatic

interpretation of the longitudinal trends in cardiac enzymes as markers of AMI severity with the

introduction and preference in the use of cTn.

There was a small increase in Charlson comorbidity severity, in incident definite AMI, and

incident or recurrent definite AMI, from 1984 to 2003 for patients aged 35‐64 hospitalised in

Perth. This is consistent with the TRACE study which found that patients hospitalized with AMI

had a large burden of comorbid cardiovascular disease that negatively impacts their 30‐day and

longer‐term survival. [104] The aims of this community‐based study were to examine the overall

and changing (1990–2007) frequency and impact on 30‐day and 1‐year death rates from

cardiovascular comorbidities in adults from a large central New England metropolitan area

hospitalized with AMI. This is also consistent with the findings from the Worcester Heart Attack

Study [105] and the report from the Second National Registry of Myocardial Infarction (NRMI‐

2) which looked at hospital outcomes in patients presenting with congestive heart failure

complicating acute myocardial infarction. [106]

These research findings of mixed results in trends of AMI severity are consistent with the

findings by Goff et al. in 2002.[67] The study by Goff et al. showed no change in hemodynamic

indicators, worsening electrocardiographic indicators, but an improvement in cardiac enzyme

indicators. However studies by Myerson et al. in 2009[76], and Rosamond et al. in 1998 and

2012 show a general decline in trends in AMI severity.[71,75] This trends are also evident

beyond Western Countries with similar socio demographics. For instance in Estonia, the

67

incidence of first AMI started to decline after 1993, and the declines have continued until

2005k.[104]

5.3 Strengths and limitations

The greatest strength of this study is that I was able to use clinical data that was available from

previous studies collected from medical notes at various time points. These data were linked

with extracts from the WA HMDC using the research capacity of the WADLS. Understandably,

such data may not be easily accessible via a data linkage system but it can still be collected with

relative ease from clinical records globally through routine information collected at medical

consultations. This suggests the potential for my study to be replicated at other research centres

where high quality administrative and clinical data sets exist.

In addition, my study used updated standardized international definitions to analyse the vast

historical clinical data. The international definitions allows generalization of the research

findings beyond WA to countries with similar socio‐demographics and population distribution

such as New Zealand and the United Kingdom.

Studies using these surrogate measures of AMI severity need to be validated periodically for

accuracy of strength of an indirect relationship as they do not provide evidence of a direct

cause–effect relationship. Nevertheless they do provide an indication of changes in CHD

severity. This is a limitation in my study in which I used markers of association to measure

changes in severity of AMI as a means to monitor changes in severity of CHD.

Trends in AMI severity of patients admitted and treated in non‐metro WA hospitals are not

included in the study. However, since the majority of non‐metro patients with AMI are

transferred to Perth for treatment, I was still able to capture their trends in AMI severity using

the databases for the Perth MONICA and MOCHA studies. The study analyzes trends in both

troponin and CK values to reflect severity of AMI. This is a strength of the study due to the

continued availability of data on CK levels to analyse trends. In this study, troponin values were

only available for the years of 1996‐1998 and 2003 which though useful, does not provide a

consistent availability of data for trend analysis and interpretation.

Furthermore, the use of largely consistent criteria during the study period is a key benefit in the

study. This allows useful interpretation of the trends of markers AMI calculated. It allows for the

68

comparison of these trends with national and international literature which use similar markers

of AMI severity.

We have yet to develop disease registers that apply standardised objective diagnostic criteria

over time in Australia. Therefore, I used surrogate measures as the best alternative to measure

disease incidence. For instance, there were insufficient and missing data for the PREDICT

components of Congestive Heart Failure and Kidney Function. These were therefore replaced

with acute left ventricular failure (from the MONICA and MOCHA studies) and renal disease

(from the CCI) as surrogate measures, which made it possible to analyse their trends as markers

of AMI severity. With regards to missing data, there were few values available for the mean

PREDICT Final Score for females with incident and recurrent definite AMI in the year 1997. This

has a minor compromise on the quality of the trend analysis of markers of AMI severity and is

an issue which affects most studies dealing with large disease registers. Missing data is a

limitation in our study, which could perhaps be addressed with more robust and comprehensive

collection of data in future studies.

An additional limitation is that trends in other markers of AMI severity such as the degree of

occlusion of the coronary artery, the time period of the occlusion and the availability or lack of

collateral circulation could have been included in the study were it collected at the time.

However, such data are not readily available from basic clinical records as it is not routinely

collected on initial presentation to the hospital. The analysis of time periods was also limited

because the data available only covered the period from 1984 to 2003.

It would be of benefit to incorporate the benefits of this study and to address the limitations in

future studies to improve the understanding of trends in AMI severity.

5.4 Implications for practice

It would be reasonable to deduce from research findings that public health measures have been

able to control the severity of AMI over the time period of 1984 to 2003 in Australia.

Nevertheless, there needs to be a continuous effort to maintain this control and improve the

severity of AMI. In countries as the United Kingdom and New Zealand, screening and treatment

based practices are based on a patient’s absolute risk. [107] This absolute risk comprises of risk

factors, for example age, sex, diabetes and smoking, in addition to lipid levels and blood

pressure. Similar changes have been proposed for guidelines in Australia given the effectiveness

of such an approach to the prevention of AMI. [106,107]

69

Despite the rapid advances that have been made in the treatment of coronary artery disease

and risk factor control, MI is still a key cause of death in the developed world and an emerging

problem for developing countries. In order to effectively address this problem, an approach

aimed at prevention of events in high‐risk individuals is vital. [108] There is a continuous need

for improvements in research to better comprehend the incidence of and risks linked with

markers of AMI severity. Existing management guidelines on the prevention of AMI need to be

reviewed and updated in light of the research findings in an effort to improve markers of AMI

severity.[109‐111]

5.5 Suggestions for future research

It would be prudent to collect data from 2003 onwards and to study similar trends in markers of

AMI severity over the last decade. It would also be important to address the limitations of the

study in future trials. This would involve the development of disease registers that apply

standardised objective diagnostic criteria over time in Australia, taking a more comprehensive

approach to analysing the trends in AMI severity. However, disease registers are costly to set up

and maintain. Furthermore, loss to follow up, hence attrition rates would be a limitation in

studying trends in AMI severity.

70

Chapter Six: Conclusion

This study shows that there is no clear correlation that there has been a decline in the trends of

markers for the severity of AMI during the study period which would contribute to the decline

in CHD mortality in hospitalised patients since the late 1960s, in Western Australia.

The primary research findings showed mixed results with regards to trends in markers of AMI

severity during the study period. The secondary research findings showed similar mixed results

in trend analysis of markers of AMI severity. There were adequate data, with good reliability and

validity, which allowed for suitable analysis and interpretation. The research findings of this

study could extend beyond Australia, as CHD is a leading cause of death, and a health and

economic burden, not just in developed countries with a similar socioeconomic profile but in

developing countries, such as India, as well.

71

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Appendix 1

79

Appendix 2

Classifications of ECG abnormalities used by the Minnesota Code

Minnesota

Code

ECG Abnormality

1‐1‐1 to 1‐3‐6 Q waves

2‐1 to 2‐5 QRS axis deviation

3‐1 to 3‐3 High amplitude R waves

4‐1‐1 to 4‐4 ST junction (J) and segment depression

5‐1 to 5‐4 T wave items

6‐1 to 6‐8 A‐V conduction defect

7‐1‐1 to 7‐8 Ventricular conduction defect

8‐1‐1 to 8‐9 Arrhythmias

9‐1 to 9‐8‐2 Miscellaneous including ST segment elevation (9‐2)

Codes 1, 4, 5, and 9‐2 (ST elevation) are grouped by leads, resulting in three

Sub‐classifications of anterolateral, posterior (inferior) and anterior.

Macfarlane P. Minnesota coding and the prevalence of ECG abnormalities, Heart 2000; 84(6): 582–584.

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Appendix 3

Predict score components, definitions and risk computation.

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Appendix 4

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Appendix 5

Patient characteristics of history of diabetes, heart failure, stroke, hypertension, CABG and use of digoxin in the four years prior to their date of diagnosis with incident definite AMI


Total N=6338

*P‐value

n (%)

Diabetes No 6070 (95.7) Yes 268 (4.3) <0.001

Heart Failure No 6235 (98.4) Yes 103 (1.6) 0.014

Stroke No 6189 (97.6) Yes 149 (2.4) 0.005

Hypertension No 5823 (91.9) Yes 515 (8.1) <0.001

CABG No 6336 (99.9) Yes 2 (0.1) 0.795

Use of digoxin No 5661 (89.3) Yes 146 (2.3) Missing 531 (8.4) 0.006

*Chi‐squared test

CABG= Coronary Artery Bypass Graft, AMI= Acute Myocardial Infarction

84

Appendix 6

Patient characteristics for history of diabetes, heart failure, stroke, hypertension, coronary

artery bypass graft surgery and use of digoxin in the four years prior to their date of

diagnosis with incident or recurrent definite AMI


Total N=7671

*P‐value

n (%)

Diabetes No 7167 (93.4) Yes 504 (6.6) <0.001

Heart Failure No 7360 (95.9) Yes 311 (4.1) <0.001

Stroke No 7435 (96.9) Yes 236 (3.1) <0.001

Hypertension No 6550 (85.4) Yes 1121 (14.6) <0.001

CABG No 7562 (98.6) Yes 109 (1.4) 0.512

Use of digoxin No 6815 (88.8) Yes 227 (3.0) Missing 629 (8.2) 0. 014

*Chi‐squared test

CABG = Coronary Artery Bypass Graft, AMI=Acute Myocardial Infarction

85

Appendix 7

Subject characteristics of history of diabetes, heart failure, stroke and hypertension in the four

years prior to their date of diagnosis with incident or recurrent definite AMI

*Chi‐squared test, AMI=Acute Myocardial Infarction


Total

1998 N=1173 n (%)

2003 N=749 n (%)

*P‐value

Diabetes No 983 (83.8) 592 (79.0) 0.125 Yes 190 (16.2) 157 (21.0)

Heart failure No 1034 (88.2) 637 (85.0) 0.069 Yes 139 (11.8) 102 (15.0)

Stroke No 1071(91.3) 686 (71.6) 0.126 Yes 102 (8.7) 63 (8.4)

Hypertension No 784 (66.8) 557 (74.4) 0.003 Yes 389 (33.2) 192 (25.6)

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