1. health and fitness
2. disease
3. heart disease

NON-INVASIVE PREDICTORS
OF MORTALITY AFTER ACUTE
MYOCARDIAL INFARCTION
JARI
TAP A N A I N E N
Department of Internal Medicine,
University of Oulu
OULU 2003
JARI TAPANAINEN
NON-INVASIVE PREDICTORS OF
MORTALITY AFTER ACUTE
MYOCARDIAL INFARCTION
Academic Dissertation to be presented with the assent of
the Faculty of Medicine, University of Oulu, for public
discussion in the Auditorium 10 of the University Hospital
of Oulu, on May 3rd, 2003, at 12 noon.
O U L U N Y L I O P I S TO, O U L U 2 0 0 3
Copyright © 2003
University of Oulu, 2003
Supervised by
Professor Heikki Huikuri
Reviewed by
Docent Juha Hartikainen
Docent Juhani Koistinen
ISBN 951-42-7011-8
(URL: http://herkules.oulu.fi/isbn9514270118/)
ALSO AVAILABLE IN PRINTED FORMAT
Acta Univ. Oul. D 722, 2003
ISBN 951-42-7010-X
ISSN 0355-3221
(URL: http://herkules.oulu.fi/issn03553221/)
OULU UNIVERSITY PRESS
OULU 2003
Tapanainen, Jari, Non-invasive predictors of mortality after acute myocardial
infarction
Department of Internal Medicine, University of Oulu, P.O.Box 5000, FIN-90014 University of
Oulu, Finland
2003
Abstract
There is a need to identify patients with an increased risk of dying after acute myocardial infarction
(AMI), because sudden cardiac death (SCD) and potentially fatal ventricular tachyarrhythmias can be
prevented by an implantable cardioverter-defibrillator, or in some cases, with aggressively optimized
drug or revascularization therapy. The present study was designed to study the predictive power of
non-invasive risk markers and all-cause, cardiac and arrhythmic mortality in 700 consecutive postAMI patients discharged alive with optimal medication according to contemporary guidelines.
Detrended fluctuation analysis of heart rate variability (HRV) predicted all-cause mortality
beyond clinical variables as well as left ventricular function in post-AMI patients. The predictive
power of the short-term scaling exponent α1 was higher than that of the traditional indexes of HRV
(for α1 < 0.65, the risk ratio (RR) in multivariate analysis was 5.1, with 95% confidence intervals (CI)
2.9-8.9; p < 0.001). HRV results from a conventional 24-hour electrocardiographic (ECG) recording
system differed significantly when compared to a system with a higher sampling frequency. The
difference was generally more pronounced in post-AMI patients than in healthy subjects.
The presence of sustained T-wave alternans during a predischarge exercise test after AMI was not
a marker of mortality. However, the inability to perform an exercise test or to reach the heart rate of
105 beats/min predicted independently all-cause (RR 9.3, 95% CI 2.0-43.3, p < 0.01) and cardiac
mortality (RR 11.1, 95% CI 2.4-50.8, p < 0.01). High levels of natriuretic peptides were associated
with both sudden and non-sudden cardiac mortality. B-type natriuretic peptide provided more specific
independent information on the risk for subsequent SCD (RR 3.9, 95% CI 1.2-12.3, p < 0.05) than
non-SCD.
SCDs occurred mainly more than 18 months after AMI, and the proportion of SCD was less than
40% of all cardiac deaths. Common arrhythmia markers such as the presence of ventricular premature
beats or episodes of nonsustained ventricular tachycardia during ambulatory recordings, the time
domain parameters of HRV, baroreflex sensitivity, QT dispersion and QRS complex duration
provided only limited predictive power on the risk of SCD or arrhythmic events in patients with
optimized beta-blocking therapy. Many risk variables previously considered to predict SCD were
better predictors of non-SCD than SCD.
Conclusions: 1. The epidemiological pattern of SCD was different from that reported previously.
2. Many arrhythmia risk markers provided only limited information on the risk of SCD. 3. Short-term
fractal scaling exponent α1 provided potentially useful information on the risk for all-cause mortality,
and BNP was useful in predicting the risk of SCD in a post-AMI population with optimized therapy.
Keywords: ambulatory recordings, heart rate variability, natriuretic peptides, risk
stratification, T-wave alternans
Acknowledgements
This work was carried out at the Department of Internal Medicine, University of Oulu,
during the years 1996–2002. I wish to express my sincere gratitude to Professor Antero
Kesäniemi, M.D., Ph.D., Head of the Department, for his support and providing the
facilities for this study. My warmest thanks go to Professor Heikki Huikuri, M.D., Ph.D.,
for suggesting this research project and his excellent guidance throughout the study.
I owe my sincere thanks to Docent Juha Hartikainen, M.D., Ph.D., and Docent Juhani
Koistinen, M.D., Ph.D. for revising the manuscript and for their valuable comments. I
thank Ms. Sandra Hänninen, M.Sc. for promptly and efficiently revising the language of
this thesis.
I am most grateful to my co-workers Professor Juhani Airaksinen, M.D., Ph.D.,
Docent Raija Laukkanen, Ph.D., Polar Electro Ltd.; Dr. Kai Lindgren, M.D., Docent
Timo Mäkikallio, M.D., Ph.D., Docent Pekka Raatikainen, M.D., Ph.D., Professor Tapio
Seppänen, Ph.D., Dr. Aino-Maija Still, M.D., as well as other members of our research
team: Dr. Juha Perkiömäki, M.D., Ph.D, Dr. Sirkku Pikkujämsä, M.D., Ph.D., Dr. Vesa
Jokinen, M.D. and Mr. Mikko Tulppo, Ph.D. I also wish to express my gratitude to the
excellent group of Danish colleagues Professor Poul Erik Bloch Thomsen, M.D., Ph.D.,
Dr. Lars Køber, M.D., Ph.D. and Dr. Christian Torp-Pedersen, M.D., Ph.D. for their
support and for introducing me to wall-motion index analysis. I acknowledge the work of
Dr. Antti Loimaala, M.D., Ph.D. of the UKK Institute, Tampere; Professor Juhani
Leppäluoto, M.D., Ph.D, and Docent Olli Vuolteenaho, M.D., Ph.D. at the Department of
Physiology, University of Oulu.
I would also like to thank Ms. Pirkko Huikuri, R.N., Ms. Päivi Karjalainen, R.N., Ms.
Marja Hietaniemi, R.N., Ms. Eija Niemelä, R.N., Ms. Anne Lehtinen and Ms. Mirja Salo,
B.Sc for their valuable technical assistance throughout the study.
I am most grateful to all the colleagues and co-workers at the Department of Internal
Medicine and at the Division of Cardiology under the guidance of Prof. Markku
Ikäheimo, M.D., Ph.D. Finally, I owe my loving thanks to my family and to all my
friends.
This work was supported by grants from the Finnish Foundation for Cardiovascular
Research, Helsinki, Finland; Duodecim foundation, Ida Montin Foundation, Aarne and
Aili Turunen’s Foundation, Polar Electro Ltd., Kempele, Finland and Astra-Zeneca,
Masala, Finland.
Stockholm, March 2003
Jari Tapanainen
Abbreviations
α1
α2
β
ACE
AMI
ApEn
ATII
BRS
CAD
CI
DFA
ECG
EF
EP
HF
HRV
ICD
LF
nsVT
OR
PTCA
QRSd
QTc
QTd
RMSSD
ROC
RR
RR interval
SCD
SD
short-term scaling exponent of fractal-like correlation properties
intermediate-term scaling exponent of fractal-like correlation properties
slope of the power-law relationship
angiotensin-converting enzyme
acute myocardial infarction
approximate entropy
angiotensin II
baroreflex sensitivity
coronary artery disease
confidence interval
detrended fluctuation analysis
electrocardiogra/m, -phic, -phy
ejection fraction
electrophysiological
high frequency
heart rate variability
implantable cardioverter-defibrillator
low frequency
nonsustained ventricular tachycardia
odds ratio
percutaneous transluminal coronary angioplasty
filtered duration of QRS complex
corrected QT interval
QT dispersion
square root of the mean of the squared differences between successive
RR intervals
receiver-operating characteristic
risk ratio
R-peak-to-R-peak interval
sudden cardiac death
standard deviation
SDANN
SDNN
TWA
ULF
VF
VLF
VPB
VT
WMI
standard deviation of the averages of RR intervals in all 5 min
segments of the entire recording
standard deviation of all RR intervals
T-wave alternans
ultra low frequency
ventricular fibrillation
very low frequency
ventricular premature beat
ventricular tachycardia
wall motion index
List of original communications
This thesis is based on the following publications, which are referred to in the text by
their Roman numerals:
I Tapanainen JM, Seppänen T, Laukkanen R, Loimaala A, Huikuri HV (1999)
Significance of the accuracy of RR interval detection for the analysis of new
dynamic measures of heart rate variability. Ann Noninvas Electrocardiol 4:10–18.
II Tapanainen JM, Bloch Thomsen P E, Køber L, Torp-Pedersen C, Mäkikallio TH,
Still A-M, Lindgren KS, Huikuri HV (2002) Fractal Analysis of Heart Rate
Variability and Mortality After an Acute Myocardial Infarction. Am J Cardiol
90:347–352.
III Tapanainen JM, Still AM, Airaksinen KEJ, Huikuri HV (2001) Prognostic
significance of risk stratifiers of mortality, including T wave alternans, after acute
myocardial infarction: Results of a prospective follow-up study. J Cardiovasc
Electrophysiol 12:645–653.
IV Tapanainen JM, Lindgren KS, Mäkikallio TH, Vuolteenaho O, Leppäluoto J,
Huikuri HV (2003) Natriuretic peptides as predictors of non-sudden and sudden
cardiac death after acute myocardial infarction in the β-blocking era. Submitted for
publication.
V Huikuri HV, Tapanainen JM, Lindgren K, Raatikainen P, Mäkikallio TH, Airaksinen
KEJ, Myerburg RJ (2003) Prediction of sudden cardiac death after myocardial
infarction in the beta-blocking era. J Am Coll Cardiol (In press)
Contents
Abstract
Acknowledgements
Abbreviations
List of original communications
Contents
1 Introduction ...................................................................................................................15
2 Review of the literature .................................................................................................16
2.1 Mortality after acute myocardial infarction ............................................................16
2.1.1 Total mortality .................................................................................................16
2.1.2 Cardiac and arrhythmic mortality ....................................................................17
2.1.3 Sudden cardiac death after acute myocardial infarction ..................................17
2.2 Mechanisms of ventricular tachyarrhythmias after acute myocardial infarction ....18
2.3 Non-invasive risk stratification after acute myocardial infarction with
special emphasis on arrhythmic mortality ..............................................................19
2.3.1 Clinical risk factors in general population .......................................................20
2.3.2 Clinical risk factors after acute myocardial infarction.....................................20
2.3.3 Left ventricular ejection fraction .....................................................................21
2.3.4 Autonomic markers..........................................................................................22
2.3.4.1 Heart rate variability............................................................................22
2.3.4.2 Baroreflex sensitivity ..........................................................................27
2.3.4.3 Heart rate turbulence ...........................................................................28
2.3.5 Changes in repolarization ................................................................................28
2.3.5.1 QT interval ..........................................................................................29
2.3.5.2 QT dispersion ......................................................................................30
2.3.5.3 T-Wave alternans .................................................................................32
2.3.5.4 T-wave morphology and T-wave vector loop analysis ........................33
2.3.6 Ambulatory ECG recordings ...........................................................................34
2.3.6.1 Ventricular premature beats.................................................................34
2.3.6.2 Nonsustained ventricular tachycardia and sustained ventricular
tachycardia ..........................................................................................35
2.3.7 Heart rate .........................................................................................................35
2.3.8 Exercise test.....................................................................................................36
2.3.9 Atrioventricular and intraventricular conduction abnormalities ......................37
2.3.10 Signal-averaged ECG ....................................................................................38
2.3.11 Body surface mapping and magnetocardiographic mapping .........................39
2.3.12 Natriuretic peptides and other vasoactive markers ........................................39
2.4 Invasive risk stratification after acute myocardial infarction..................................40
2.4.1 Electrophysiological testing.............................................................................40
2.4.2 Cardiac catheterization ....................................................................................41
2.5 Combining test results in risk stratification after acute myocardial infarction .......41
2.6 Prevention of mortality after acute myocardial infarction ......................................42
2.6.1 Optimizing medication ....................................................................................42
2.6.2 Revascularization therapy................................................................................45
2.6.3 Implantable cardioverter-defibrillator..............................................................46
2.6.4 Catheter ablation and surgical treatment of ventricular tachyarrhythmias.......47
3 Aims of study.................................................................................................................48
4 Subjects and methods ....................................................................................................49
4.1 Subjects...................................................................................................................49
4.2 Methods ..................................................................................................................51
4.2.1 Follow-up and endpoints .................................................................................51
4.2.2 Left ventricular ejection fraction .....................................................................51
4.2.3 Heart rate variability........................................................................................52
4.2.3.1 Recording of heart rate variability (I–III, V).......................................52
4.2.3.2 Analysis of heart rate variability .........................................................52
4.2.4 Baroreflex sensitivity (III, V) ..........................................................................54
4.2.5 ECG measurements (QRS duration, QT-interval and QT dispersion) (III, V).54
4.2.6 Exercise test and T-wave alternans analysis (III).............................................55
4.2.7 Signal-averaged ECG (II, III, V) .....................................................................55
4.2.8 Ambulatory ECG recording (III, V) ................................................................56
4.2.9 Natriuretic peptides (IV)..................................................................................56
4.2.10 Statistical methods .........................................................................................56
5 Results ...........................................................................................................................58
5.1 Effect of sampling frequency on the analysis of heart rate variability (I)...............58
5.2 Medication after acute myocardial infarction .........................................................59
5.3 Mortality after acute myocardial infarction ............................................................59
5.4 Clinical parameters .................................................................................................61
5.5 Heart rate variability as a risk predictor (II, III, V).................................................61
5.5.1 Time and frequency domain parameters (II, III, V).........................................61
5.5.2 Dynamic parameters (II)..................................................................................63
5.6 T-wave alternans as a risk predictor (III) ................................................................64
5.7 Natriuretic peptides as risk predictors (IV).............................................................66
5.8 Other risk predictors ...............................................................................................67
5.8.1 Left ventricular function (II–V).......................................................................67
5.8.2 Baroreflex sensitivity (II, V)............................................................................67
5.8.3 QTc interval (III) and QT dispersion (III, V)...................................................67
5.8.4 Signal-averaged ECG (III, V)..........................................................................68
5.8.5 Chronotropic insufficiency (III).......................................................................68
5.9 Univariate predictors of all-cause, cardiac and sudden cardiac mortality (II–V) ...69
5.10 Multivariate predictors of all-cause, cardiac and sudden cardiac mortality..........73
5.11 Combination of risk predictors .............................................................................73
6 Discussion .....................................................................................................................76
6.1 Effect of sampling frequency on heart rate variability analysis (I).........................76
6.2 Optimization of medication ....................................................................................77
6.3 Proportion and temporal distribution of sudden cardiac death after acute
myocardial infarction..............................................................................................78
6.4 Heart rate variability as a risk predictor..................................................................78
6.4.1 Time and frequency domain parameters as predictors of mortality (II,III,V)..78
6.4.2 Fractal analysis of heart rate variability and mortality (II) ..............................79
6.5 T-wave alternans .....................................................................................................80
6.6 Natriuretic peptides.................................................................................................81
6.7 Other predictors of mortality ..................................................................................82
6.7.1 Clinical parameters as predictors of all-cause and cardiac mortality (II, III) ..82
6.7.2 Functional impairment and chronotropic incompetence (II, III) .....................83
6.7.3 Other test results as predictors of mortality .....................................................83
6.7.4 Combination of tests........................................................................................84
6.8 Statistical considerations.........................................................................................84
6.8.1 Accuracy of diagnostic tests ............................................................................84
6.8.2 Incomplete/nondiagnostic tests........................................................................85
6.8.3 Limitations and biases of observational study designs ....................................86
6.9 Prediction of susceptibility to life-threatening ventricular arrhythmias after
myocardial infarction..............................................................................................87
7 Conclusions ...................................................................................................................89
References
1 Introduction
Acute myocardial infarction (AMI) is an important manifestation of coronary artery
disease. Its sequelae lead to significant morbidity and mortality, and despite improving
medical therapies and evolving techniques in both percutaneous and surgical treatment, it
remains a major cause of death among adults in the Western world. It has been estimated
that about one half of coronary artery disease (CAD) deaths are attributable to AMI
(Goldberg et al. 1999), and epidemiological data suggest that sudden cardiac death (SCD)
would account for approximately 50% of all cardiac patients in AMI survivors (Bigger et
al. 1984, Myerburg et al. 1984, Myerburg et al. 1992, Myerburg et al. 1997, Michaels &
Goldschlager 2000).
There is a need to identify patients with an increased risk of dying after AMI,
especially patients with the risk of suffering from a malignant arrhythmia or experiencing
an SCD, since great advances have been made in preventive therapies during the past few
decades. SCD can be prevented and potentially fatal ventricular tachyarrhythmias treated
by offering the patient an implantable cardioverter-defibrillator (ICD), and some patients
can be treated with aggressive optimized drug or revascularization therapy.
Much work has been done in order to find specific and sensitive non-invasive risk
predictors of mortality among survivors of AMI. Unfortunately, most tests suffer from a
low positive predictive value (PPV). The gold standard for detecting an increased risk for
malignant arrhythmias has been electrophysiological (EP) study, but it has obvious
limitations: it is an expensive and invasive examination with potential risks, and even its
specificity and sensitivity are far from perfect. On the other hand, today’s patient
populations differ in terms of treatment and thus of risk from the older ones. Therefore, it
is possible that some of the previously established risk indicators may lose some of their
predictive value in the current era of treatment.
This study was designed to investigate both cardiac and arrhythmic mortality and the
value of several non-invasive risk predictors in post-AMI patients who were discharged
alive from hospital and whose medical treatment was aimed to be optimal according to
contemporary guidelines during the course of study.
2 Review of the literature
2.1 Mortality after acute myocardial infarction
2.1.1 Total mortality
Mortality after acute myocardial infarction (AMI) has declined consistently during the
last decades, but is still relatively high (up to 5%–11%) during the first 6 to 12 months
after discharge (Volpi et al. 1993, Rouleau et al. 1996, McGovern et al. 1997, Goldberg
et al. 1999, Herlitz et al. 2002). Both in-hospital and long-term mortality after an AMI
are decreasing: in a large meta-analysis, the 1-month overall mortality rate decreased
from 31% during the 1960’s to 25% in the 1970’s and to 18% in the 1980’s (de Vreede et
al. 1991). This analysis did not show any decrease in the 5-year mortality, which
remained at 33% between 1960–1969 and 1970–1979 (de Vreede et al. 1991), but in a
recent report both the 28-day and 3-year mortality were reduced by 35% between 1985
and 1995 (McGovern et al. 2001). The decline in mortality is attributed to the advent of
coronary care units (Peterson et al. 1972), effective treatment with acetosalisylic acid
(ASA) (ISIS-2 Collaborative Group 1988, The RISC Group 1990), beta-blockers
(Hjalmarson et al. 1981, Norwegian Multicenter Study Group 1981, Beta-Blocker Heart
Attack Trial Group (BHAT) 1982, Dargie 2001) ACE-inhibitors (Pfeffer et al. 1992, The
Acute Infarction Ramipril Efficacy (AIRE) Study Investigators 1993, GISSI-3 Study
Group 1994), statins (The Scandinavian Simvastatin Survival Study Group 1994, Sacks
et al. 1996, The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID)
Study Group 1998), anticoagulant (Smith et al. 1990) or newer antithrombotic therapies
(CAPRIE Steering Committee 1996, Platelet Receptor Inhibition in Ischemic Syndrome
Management (PRISM) Study Investigators 1998) and efficient reperfusion treatment,
either by thrombolysis (GISSI 1987, Wilcox et al. 1990), percutaneous transluminal
coronary angioplasty (PTCA) (Stone et al. 1995, Madsen et al. 1997, Weaver et al. 1997,
Kaltoft et al. 2002) and stenting, or coronary artery bypass grafting (CABG) (Madsen et
17
al. 1997). Despite this on-going decline, CAD causes more deaths in western countries
than any other disease.
2.1.2 Cardiac and arrhythmic mortality
In the pre-thrombolytic era, more than one half of deaths after AMI were considered
arrhythmic. In the study by Marcus et al. of 867 patients, arrhythmic mortality reached
9.2% during the follow-up time of 48 months when the total mortality was 17% (Marcus
et al. 1988). In more recent estimates, more than one half of deaths after infarction are
classified as cardiac, and, again, half of them are believed to be arrhythmic in origin
(Myerburg et al. 1992, Myerburg et al. 1993, Myerburg et al. 1997, Zipes & Wellens
1998, Michaels & Goldschlager 2000, Huikuri et al. 2001). On the other hand, many
deaths defined as non-arrhythmic may be due to tachyarrhythmias (Moss et al. 1996), and
it is know that a large proportion of all SCDs takes place among patients with a previous
AMI (Huikuri et al. 2001). In a more recent study of 325 consecutive patients, cardiac
mortality was 5% and arrhythmic mortality 2% after a follow-up of 2.5 years (Hohnloser
et al. 1999). In the placebo group of recent amiodarone studies on patients with a higher
risk expressed either by reduced left ventricular systolic function or increased ventricular
arrhythmic activity, non-arrhythmic cardiac mortality has been about 5–6% at 2 years,
and arrhythmic mortality approximately 6% at 2 years (Cairns et al. 1997, Julian et al.
1997).
2.1.3 Sudden cardiac death after acute myocardial infarction
Sudden cardiac death (SCD) is defined as “natural death due to cardiac causes, heralded
by abrupt loss of consciousness, within one hour of the onset of acute symptoms;
preexisting heart disease may have been known to be present but the time and mode of
death are unexpected” (Myerburg et al. 1992, Priori et al. 2001). This definition underlies
the fact that classifying a death as SCD is not clear-cut, and between clinical trials, there
has been an inconsistency in definitions. Finding out the mechanism of SCD without an
autopsy is an intricate issue. (Hinkle, Jr. & Thaler 1982, Pratt et al. 1996). Up to 50%
discordance in the interobserver classification of deaths as sudden has been found in
studies of cardiac failure, so that the agreement has been slightly better than by chance
(Ziesche et al. 1995). The most commonly accepted chain of events leading to SCD
consists of ventricular tachycardia, which first changes into ventricular fibrillation, and
then to asystole (Wit & Janse 1992, Mehta et al. 1997, Zipes & Wellens 1998). In autopsy
data, the frequency of active coronary lesions has been variable, but there is evidence that
myocardial ischemia is a major cause of SCD in CAD patients (Di Maio & Di Maio,
Leach et al. 1995, Farb et al. 1995).
Instantaneous death can be also caused by various non-cardiac reasons with vascular
emergencies and massive pulmonary embolisms being the most common ones. In
18
addition, by the definition mentioned above, SCD includes not only arrhythmic deaths
but also cardiac deaths caused by valvular emergencies and free wall ruptures, for
example. Still, a majority of cardiac deaths occurring instantaneously are arrhythmic in
origin. After an AMI, most of the arrhythmias are tachyarrhythmias, and only a minority
fall into the category of bradyarrhythmias. Bradyarrhythmias are usually seen in patients
with advanced heart disease (Luu et al. 1989), even though the proportion of
bradyarrhythmic SCD may be up to 15–20% in general populations (Priori et al. 2001).
The data of the presenting arrhythmia in SCD patients are scarce: a report of seven series
of 24-hour ECG recordings in 157 ambulatory cases with various cardiac diagnoses
revealed VF in 62%, VT in 8%, torsades-de-pointes VT in 13% and bradyarrhythmia in
16.5% as the cause (Bayes et al. 1989). In ICD patients and patients with advanced heart
failure, bradyarrhythmia, asystole or electro-mechanic dissociation can be responsible for
the SCD in up to 25–62% of the cases (Myerburg et al. 1982, Luu et al. 1989). In an 834patient database with ICDs, 17 out of 109 deaths were classified as sudden cardiac during
the mean follow-up of 1.6 years, and only seven of them were associated with ICD
discharges (Pratt et al. 1996). In a small study among patients with advanced heart
failure, all the cardiac arrests due to VT or VF occurred in patients with a prior
myocardial infarction (Luu et al. 1989). It is likely, however, that patients suffering from
severe congestive heart failure due to CAD (EF ≤ 30% with NYHA II–IV symptoms)
have a re-infarction as the cause of instantaneous death; the prevalence of acute coronary
findings was 54% in such patients experiencing sudden death (Uretsky et al. 2000).
Nevertheless, an arrhythmia can lead to a non-instantaneous death as syncope caused by
arrhythmia may lead to fatal injuries.
A clear time dependency for SCDs after an AMI has been shown in various studies, so
that most of the SCDs occur within the first 6–18 months after the AMI (Bigger et al.
1984, Myerburg et al. 1984, Myerburg et al. 1992, Myerburg et al. 1997, Zipes &
Wellens 1998, Michaels & Goldschlager 2000, La Rovere et al. 2001). In the MADIT II
trial, the survival benefit was not seen until nine months after the device was implanted
(Moss et al. 2002).
2.2 Mechanisms of ventricular tachyarrhythmias after acute
myocardial infarction
Ventricular tachyarrhythmias are generated by three basic mechanisms: triggered activity,
automaticity or re-entry. A widely accepted model of ventricular tachyarrhythmias
consists of the presence of a substrate for tachyarrhythmia, such as structural arrhythmic
abnormalities – fibrosis or scarring after myocardial infarction, the presence of ischemic
left ventricular dysfunction, or left ventricular hypertrophy (LVH). The substrate enables
the tachyarrhythmia to develop. Modulating factors or transient initiating events consist
of disturbances in the autonomic nervous system, electrolyte imbalance, acidosis,
ischemia and hemodynamic dysfunction, drug effects, and endocrine and psychosocial
stress factors. Finally, a factor triggering the tachyarrhythmia could be a ventricular
premature beat (VPB), variation in cycle length and heart rate, for example. The substrate
19
and the modulating factors can render the myocardial membrane unstable, thus preparing
the ground for a fatal tachyarrhythmia (Myerburg et al. 1992). In a failing heart, volume
overload causing stretch may slow conduction, alter refractoriness, and trigger
afterpolarization and ventricular ectopic beats (Franz 1996, Reiter 1996, Zhu et al. 1997,
Berger et al. 2002).
If a major coronary artery is acutely occluded, the ischemia may lead to ventricular
tachycardia and ventricular fibrillation, or to bradycardia, asystole or atrioventricular
block. After AMI, the most common cause of SCD is arrhythmia; in the pre-thrombolytic
era, symptoms of ischemia preceded the arrhythmic event in about 60% of patients
(Marcus et al. 1988). Ischemia may trigger ventricular fibrillation. Acute myocardial
ischemia is generally considered to be the most common trigger of fatal arrhythmias
(Myerburg et al. 1992). In a canine model, an ischemic VF could be induced at a site
remote from previous myocardial infarction (Patterson et al. 1982).
2.3 Non-invasive risk stratification after acute myocardial infarction
with special emphasis on arrhythmic mortality
Finding patients after AMI with a high risk of dying is essential. In the non-optimally
treated population the mortality risk is high, and even in the optimally treated patient it is
obviously much higher than in a control population of the same age. An ideal test or test
pattern should be noninvasive, inexpensive, and easy to perform and analyze before
discharge from hospital, even though the majority of the deaths in conjunction with AMI
take place before reaching hospital and during the immediate hospitalization period.
Several risk markers have been identified and tested as predictors of mortality in postAMI patients. The positive predictive accuracy of these tests has been relatively low.
There is a continuous need for tests that would provide more accurate prognostic
screening. Some of the studies often referred to were performed in the prethrombolytic
era (Kleiger et al. 1987, Bigger et al. 1992, Bigger et al. 1996), and so far, there is little
data on the prognostic value of various risk predictors in patients with whom the medical
therapy was optimized according to current guidelines. It has been believed that some
arrhythmia risk variables specifically predict SCD after AMI (the Multicenter PostInfarction Research Group 1983, Kleiger et al. 1987, Maggioni et al. 1993, McClements
& Adgey 1993, El Sherif et al. 1995, Glancy et al. 1995, Perkiömäki et al. 1995,
Hartikainen et al. 1996, Zuanetti et al. 1996, La Rovere et al. 2001). It is obvious that
modern therapy has modified the predictive value of some if not all previously valid risk
factors. Furthermore, it is likely that the predictive value of risk variables changes
depending on the time of measurement after AMI.
20
2.3.1 Clinical risk factors in general population
In general population studies, the risk factors for SCD are largely the same as for CAD,
and their risk predictive power lies partly in their ability to predict the probability of the
underlying disease: age (Wannamethee et al. 1995), male sex, a family history of CAD
(Sexton et al. 1997), increased levels of total cholesterol (Wannamethee et al. 1995), or
apolipoprotein CIII (Tsuji et al. 1999) LDL cholesterol (Hiserodt et al. 1995), high
triglyceride levels (Hiserodt et al. 1995), hypertension (Kannel et al. 1988, Weijenberg et
al. 1996, Escobedo & Caspersen 1997, Jouven et al. 1999), systolic blood pressure in
men (Cupples et al. 1992, Wannamethee et al. 1995, Weijenberg et al. 1996, Jouven et al.
1999), glucose intolerance in both sexes (Cupples et al. 1992, Curb et al. 1995), obesity
(Cupples et al. 1992, Jouven et al. 1999, Rea et al. 2001), physical inactivity
(Wannamethee et al. 1995), diabetes (Curb et al. 1995, Sexton et al. 1997, Escobedo &
Caspersen 1997), and cigarette smoking (Kannel & Schatzkin 1985, Hallstrom et al.
1986, Kannel et al. 1990, Cupples et al. 1992, Escobedo & Caspersen 1997, Sexton et al.
1997). Diets high in saturated fats and low in polyunsaturated fats are associated with
increased morbidity in CAD, thus leading indirectly to an increased risk of an SCD
(National Cholesterol Education Program. 1994). Heavy alcohol consumption increases
the risk of SCD in the general population (Aberg et al. 1986, Wannamethee et al. 1995).
In general population studies, family history of SCD is an important independent risk
marker for future SCD (Friedlander et al. 1998, Jouven et al. 1999).
2.3.2 Clinical risk factors after acute myocardial infarction
In various post-AMI patient series, most of the factors listed above preserve their
predictive power. According to the recent task force report on SCD, a meta-analysis of
the placebo arms of patient data from the EMIAT (European Myocardial Infarct
Amiodarone Trial), CAMIAT (Canadian Amiodarone Myocardial Infarct Arrhythmia
Trial), SWORD (Survival with Oral D-Sotalol), TRACE (Trandolapril Cardiac
Evaluation) and DIAMOND-MI (Danish Investigations of Arrhythmia and Mortality on
Dofetilide among patients with Myocardial Infarction) studies (Kober et al. 1996, Waldo
et al. 1996, Cairns et al. 1997, Julian et al. 1997, Danish Investigations of Arrhythmia
and Mortality ON Dofetilide 1997) showed that advanced age, a history of previous
myocardial infarction or angina and a history of hypertension were associated with an
increased risk of both all-cause and arrhythmic mortality. Diabetes was associated with
only all-cause mortality, and male sex with arrhythmic mortality (Priori et al. 2001). The
prevalence of arrhythmic endpoints (resuscitated VF or arrhythmic death) was higher in
the placebo arm of CAMIAT study, in elderly patients ( > 70 years), or in patients with a
history of diabetes, previous congestive heart failure, or previous myocardial infarction
(Cairns et al. 1997). Previous myocardial infarction has been linked to unfavorable
prognosis after AMI (Brugada et al. 1991). This probably reflects the extent of damaged
myocardium and thus the decrease in systolic function and/or the presence of substrates
for arrhythmias, or simply the severity of CAD. Severe renal insufficiency was an
21
independent risk factor of mortality (Sorensen et al. 2002) in a recent report of the
TRACE post-infarction population (Group TTS 1994).
Reperfusion treatment in the acute setting of myocardial infarction leads to a decrease
in both short- and long-term mortality. Thrombolytic therapy is associated with a
reduction in the risk of all-cause mortality (GISSI 1987, Wilcox et al. 1990), and it seems
to be associated with a decrease in the risk of arrhythmias (Cairns et al. 1997). Patients
with pulmonary edema during hospitalization show an increased incidence of arrhythmic
events (Cairns et al. 1997). Post-MI patients who suffer from severe arrhythmias causing
syncope or who have been resuscitated from a cardiac arrest more than 48 hours after
infarction are at a high risk for SCD (Priori et al. 2001).
The New York Heart Association functional class is an important tool in assessing a
patient’s status after AMI, and it has been proven to be a predictive factor. In the abovementioned pooled meta-analysis, the risk of both all-cause and arrhythmic mortality was
increased, according to the NYHA class (Priori et al. 2001).
2.3.3 Left ventricular ejection fraction
Decreased left ventricular systolic function is one of the most widely used risk stratifiers
in post-AMI patients, and it should be assessed in all patients who have an infarction.
(Peterson et al. 1997). Systolic function can be analyzed by measuring the EF either by
echocardiography, scintigraphy or during cardiac catherization. Echocardiography is
generally available, but there is a wide inter- and intraobserver variability in the EF
analysis when performed in the conventional 2D-mode.
Decreased left ventricular systolic function is considered to be the single most
important risk factor for overall mortality and SCD (the Multicenter Post-Infarction
Research Group 1983, Bigger et al. 1984). Cut-off points for EF of 30–40% have been
suggested for risk stratification purposes (Kober et al. 1997). According to a recent metaanalysis of pooled data from EMIAT, CAMIAT, SWORD, TRACE and DIAMOND-MI
(Kober et al. 1996, Waldo et al. 1996, Cairns et al. 1997, Julian et al. 1997, Danish
Investigations of Arrhythmia and Mortality ON Dofetilide 1997, Priori et al. 2001), the
yearly arrhythmic mortality was 9.4%, if the EF was < 20%; 7.7%, if it was 21–30%, and
3.2%, if the EF was > 30%. In the treatment arm of the SAVE (Survival and Ventricular
Enlargement) study among 1115 patients with left ventricular EF < 40%, the 3.5-yearmortality was 20%, and half of these deaths were classified as sudden (Pfeffer et al. 1992,
Stevenson & Ridker 1996). Most recently, the results from MADIT II trial, which
included 1232 post-MI patients with an EF < 30%, showed a benefit of 31% (95% CI
0.07–0.49) in all-cause mortality from ICD implantation. In this study, there was a nonsignificant trend for a bigger benefit of ICD therapy, if the EF was ≤ 25% (Moss et al.
2002).
There have been some reports of patients with congestive heart failure in which the
incidence of SCD is proportionately lower in the most severe heart failures as measured
by the NYHA class (Kjekshus 1990, MERIT-HF investigators 1999). This finding is
probably explained by the fact that people suffering from severe symptoms of heart
22
failure are likely to develop terminal decompensation. The cutoff point for severely
depressed left ventricular systolic function has been suggested to be below an EF of 15–
20% (Priori et al. 2001).
It must be taken into account that a subgroup of patients with preserved left ventricular
systolic function fall into the category of increased mortality risk. For example, left
ventricular hypertrophy (LVH) predisposes independently to SCD, whether the diagnosis
is made by ECG (Kannel 1991) or echocardiography (Aronow et al. 1988, Levy et al.
1990). LVH, in turn, is associated with hypertension, age, obesity, glucose intolerance,
stature and genetic factors, which can also be associated with an adverse prognosis.
2.3.4 Autonomic markers
Heart rate variability (HRV) and baroreflex sensitivity (BRS) are often quoted as markers
reflecting sympathovagal balance. The shift in the sympathovagal balance towards
increased sympathetic activity and/or decreased parasympathetic activity seems to be
associated with an increased risk of adverse events after an AMI (Schwartz & Priori
1990).
2.3.4.1 Heart rate variability
HRV is a physiological phenomenon defined as variation in the normal-to-normal RRintervals during normal sinus rhythm. It reflects the effects of the autonomic nervous
system and other physiological control mechanisms on cardiac function. HRV is not a
clearly periodic or regular phenomenon, and the HRV signal is sooner nonlinear than
linear. It has been suggested that HRV complies with the laws observed in chaotic signals
(Sayers 1973, Goldberger 1996). The measurement of HRV is non-invasive, often
reproducible and rather easy to perform, which has lead to the popularity of HRV analysis
as an elegant method for the measurement of neuroautonomic control of heartbeat as
autonomic cardiovascular regulation is impaired in many clinical situations, such as
hypertension, diabetes or CAD (Eckberg 1979, Airaksinen et al. 1987). Wolf et al.
discovered that decreased HRV was correlated with an increase in mortality after acute
myocardial infarction (Wolf et al. 1978), and soon thereafter, HRV was shown to be a
strong independent marker of mortality after AMI (Kleiger et al. 1987, Farrell et al. 1991,
Bigger et al. 1992).
Physiology of HRV. In spectral HRV analysis, respiration is related to the high
frequency (HF) power (Pagani et al. 1986). Reflecting the predominantly
parasympathetic origin of the HF component, this band can be minimized with atropine
or vagotomy (Akselrod et al. 1981, Pomeranz et al. 1985, Pagani et al. 1986). On the
other hand, the low frequency peak of HRV has been attributed to sympathetic activity.
Studies on muscle sympathetic nerve activity have suggested that LF and HF may,
respectively, reflect sympathetic and vagal outflow (Pagani et al. 1997, Montano et al.
23
1998). As a result, the relationship of LF power to HF power (LF/HF ratio) has often
been referred to as a specific marker of sympathovagal balance (Pagani et al. 1986,
Malliani et al. 1991), but this approach has been questioned (Eckberg 1997). The
variation in the very low frequencies (VLF) and ultra low frequencies (ULF) constitutes
the major part of the HRV total power. Vagal activity (Akselrod et al. 1981, Pagani et al.
1986), thermoregulation and peripheral vascular resistance have been suggested as the
cause for fluctuation in the VLF region (Rosenbaum & Race 1968, Sayers 1973, Kitney
1975, Lindqvist et al. 1989). The renin-angiotensin system may also play a minor role in
the genesis of HRV (Akselrod et al. 1981), as well as baro- and chemoreceptors, which
affect heart rate through rapid control mechanisms (Ravenswaaij-Arts et al. 1993).
Technical requirements for HRV measurements. For the analysis of HRV, RR interval
data should be stationary and sufficiently long (Malliani et al. 1991, Ori et al. 1992).
Stationary situations, however, are usually not physiological. To make data under
biological conditions more stationary, linear detrending and filtering can be performed
before the analysis.
In long-term recordings, i.e. during 24 hours, better reproducibility is obtained than
during short-term recordings. The recording should include one whole night and day, and
it should last at least 18 hours. The use of such recordings enables HRV parameters in
very low frequency regions — or with long-term variation — to be analyzed, in addition
to the traditionally used time domain methods. The effect of the surroundings is
emphasized in long-term recordings. (Task Force of the European Society of Cardiology
and the North American Society of Pacing and Electrophysiology 1996).
Short-term recordings of HRV should be done in controlled situations, in order to
improve reproducibility. These recordings are often applied with maneuvers affecting the
activity of the autonomic nervous system, e.g. during the Valsalva maneuver, orthostatic
test or deep breathing. In such situations, frequency domain methods are often preferred.
The length of the recording should be at least ten times the wavelength measured (Task
Force of the European Society of Cardiology and the North American Society of Pacing
and Electrophysiology 1996). On the other hand, in order to stabilize the signal, the
recording should not be lengthened too much. Recordings of up to 1 min. have been
suggested for HF measurements and up to 2 min. for LF measurements (Task Force of the
European Society of Cardiology and the North American Society of Pacing and
Electrophysiology 1996)
The validity of editing HRV data cannot be overemphasized. According to the general
consensus, artifacts, premature beats and non-sinus tachycardia episodes should be edited
out of the data before running the analyses (Task Force of the European Society of
Cardiology and the North American Society of Pacing and Electrophysiology 1996). All
time domain measurements are very sensitive for such artifacts and episodes. The
commercial automated editing systems are not standardized, and their use can be
accompanied with both the sparing of some of the ectopic beats or artifacts present in the
recording and the loss of normal sinus RR intervals because of over-editing. This will
interfere with the analysis of HRV. Depending on the material, up to 20–30% of the
recordings have to be rejected because of technical artifacts, excessive ectopy and/or
atrial arrhythmias. These patients are often left out of the analyses, and their mortality has
not been studied in the follow-up studies.
24
The effect of the sampling frequency of the recording system on HRV results has not
been extensively studied. A sampling frequency of 250–500 Hz has been recommended
(Task Force of the European Society of Cardiology and the North American Society of
Pacing and Electrophysiology 1996), but in most studies the sampling frequency has not
been reported. Most of the older studies of HRV have gathered data using 24-hour Holter
ECG systems with a sampling frequency of 128 Hz, which can cause errors in dynamic
HRV analysis. This analysis is based predominantly on quantifying subtle changes in
beat-to-beat RR interval dynamics. A 2% error was detected in the nonlinear HRV
analysis when a conventional system was compared to a high resolution ECG system
with a sampling frequency of 2000 Hz (Voss et al. 1996b). A low sampling frequency can
also cause a jitter in the recognition of the QRS complex, creating an error in the RR
interval measurement (Merri et al. 1990).
HRV parameters. Traditional methods of HRV analysis include time and frequency
domain analysis, often being referred to as linear methods. Because nonlinear
mechanisms can be involved in the genesis of HRV, several new methods have been
developed to quantify complex heart rate dynamics. These methods are referred to as
nonlinear or dynamic measures of HRV. The new dynamic parameters seem to be
superior in reflecting changes in beat-to-beat variability.
HRV parameters that are obtained during time domain analysis are based on simple
statistical methods, either derived from the RR intervals or the differences between them.
Average heart rate over a time window can be considered as the simplest parameter. The
length of the recording for time domain analysis can be either short (0.5 to 5 minutes) or
long (up to 24–48 hrs). Depending on the time window used and the length of the
recording, these parameters measure either short-term (beat-to-beat) or long-term
variability (Algra et al. 1993).
SDNN (standard deviation of the normal-to-normal RR interval). SDNN is defined as
the standard deviation of the normal-to-normal RR intervals. The variance of RR
intervals reflects the power of the HRV signal, and thus, SDNN is affected by all the
cyclic components leading to variability of the signal, and can be regarded as an estimate
of overall HRV. If SDNN is analyzed in a 24-hour recording, it is a marker of both shortand long-term variation. The shorter the monitoring period, the more SDNN will mirror
short-term variability, and as the length of recording is increased, the total variance will
likewise increase. The SDNN values from recordings of variable lengths should not be
compared to each other (Task Force of the European Society of Cardiology and the North
American Society of Pacing and Electrophysiology 1996). The simplicity and tangibility
of this measurement have made it one of the most widely used. In a Dutch study where
HRV was analyzed from short-term ECG recordings, low SDNN predicted 5-year allcause mortality, but the association with CAD deaths or SCDs was not strong (Dekker et
al. 1997). Thus, low HRV can simply be an indicator of compromised health in a general
population.
SDNN as well as other time domain parameters have been shown to predict mortality
when they are measured in the convalescent phase after an AMI. In the landmark study of
Kleiger et al., an SDNN < 50 ms was associated with a 5.3-fold mortality when compared
to patients with preserved HRV (SDNN > 100 ms) (Kleiger et al. 1987). Most of the
studies are from the pre-thrombolytic era. In the GISSI-2 (Gruppo Italiano per lo Studio
della Sopravvivenza nell’Infarto miocardico) database, an SDNN < 70 ms predicted total
25
mortality (RR 3.0, 95% CI 1.5–5.9) and cardiovascular mortality (RR 2.6, 95% CI 1.3–
5.3) (Zuanetti et al. 1996). In the ATRAMI (Autonomic Tone and Reflexes After
Myocardial Infarction) study, an SDNN < 70 ms measured during the first 4 weeks after
an AMI was a marker of increased risk of experiencing cardiac death (RR 3.2, 95% CI
1.42–7.36) during the follow-up time of 21 months among 1284 patients (La Rovere et
al. 1998).
Other time domain parameters. Other time domain parameters have not gained much
popularity as risk markers. The standard deviation of the average RR intervals (SDANN)
and the mean of the 5-minute standard deviations of RR intervals (SDNN index) estimate
long-term components of HRV. The square root of the mean squared differences between
successive RR intervals (RMSSD), the number of interval differences of successive RR
intervals greater than 50 ms (NN50), and the ratio of such differences to the total number
of RR intervals (pNN50) are all based on the differences between RR intervals and thus
are closely intercorrelated, and they all estimate the short-term components of HRV
(Bigger, Jr. et al. 1989, Task Force of the European Society of Cardiology and the North
American Society of Pacing and Electrophysiology 1996).
Frequency domain analysis. Spectral HRV analysis allows the frequency-specific
study of HRV (Akselrod et al. 1981, Kay & Marple 1981). The RR interval signal is
disintegrated into numerous sinusoidal functions of different frequencies, and a power
spectrum is created in which the amplitude is plotted as a function of each frequency.
Spectral analysis methods are based on either nonparametric (fast Fourier transformation,
FFT) or parametric techniques (autoregressive model estimation). In most cases, the
results are comparable regardless of the technique applied. The following limits of the
frequency bands have been recommended: ultra low frequency (ULF) < 0.0033 Hz, very
low frequency (VLF) 0.0033–0.04 Hz, low frequency (LF) 0.04–0.15 Hz and high
frequency (HF) 0.15–0.4 Hz (Task Force of the European Society of Cardiology and the
North American Society of Pacing and Electrophysiology 1996). The power of the bands
can be expressed either in absolute units (ms2), after logarithmic conversion, or in
normalized units for LF and HF. The ability of some frequency domain parameters to
predict mortality in post-infarction patients has been well-established in the prethrombolytic era. The reduction in ULF and VLF power (Bigger et al. 1992) as well as
LF power (Vaishnav et al. 1994) has been associated with an adverse prognosis. More
recently, VLF and LF power were shown to be reduced in patients with a risk of dying,
but they did not predict mortality in the multivariate analysis. In the same study of 239
patients, a reduced LF/HF ratio < 1.05 was associated with a borderline risk for SCD in
the multivariate analysis (Lanza et al. 1998). In a subgroup of GUSTO-I (Global Use of
Strategies to open Occluded arteries) trial patients, LF/HF < 1.2 24 hours after infarction
was a predictor of both 30-day and 1-year all-cause mortality (Singh et al. 1996).
Geometrical methods are techniques in which RR intervals are converted into various
geometrical forms. These methods are more insensitive to artifacts and ectopic beats, but
they are not applicable to very short-term recordings because of the considerable number
of RR intervals needed. In the triangular HRV index analysis, the histogram of recorded
RR intervals is interpolated to the shape of a triangle making an uncomplicated
mathematical analysis possible (Malik et al. 1989, Task Force of the European Society of
Cardiology and the North American Society of Pacing and Electrophysiology 1996). A
triangular index showing reduced HRV has been associated with both arrhythmic and
26
nonarrhythmic death (Hartikainen et al. 1996). In the Poincaré scatterograms, each RR
interval is plotted as a function of the previous one. The plots can be either visually or
quantitatively interpreted (Tulppo et al. 1996, Huikuri et al. 1996b). The standard
deviation of the longitudinal axis of the plot (SD2) is a marker of long-term HRV
(depending on the length of the recording), and the standard deviation of the vertical axis
is a marker of short-term beat-to-beat variability (SD1) (Huikuri et al. 1996b). A ball-like
or stick-like shape in 2-dimensional Poincaré plots has been linked to an adverse
prognosis (Woo et al. 1994, Huikuri et al. 1996b).
Nonlinear dynamic methods of HRV. There is evidence that heart rate is not generated
by simple periodic oscillations but that nonlinear phenomena are involved in this process
(Sayers 1973). Several HRV analysis methods based on the application of nonlinear
dynamics and chaos theory have been introduced (Goldberger et al. 1990, Goldberger
1996).
Detrended fluctuation analysis (DFA) is a nonlinear method which is used to
determine the self-similar properties, i.e. fractal-like correlation properties, of the RR
interval signal. The DFA technique was originally based on the modified root-meansquare analysis of random walk (Peng et al. 1995, Hausdorff et al. 1995, Iyengar et al.
1996, Hausdorff et al. 1996). This method is rather insensitive to the effects of noise and
nonstationarity, and its considerable advantage is that scaling exponents can be calculated
even without editing the RR intervals caused by ectopic beats.
If the analyzed scaling exponent α is near 1, it indicates fractal-like behavior of the
signal. It is also a marker of the roughness of the time series. Low values of the scaling
exponents correspond to dynamics where the magnitude of beat-to-beat HRV is close to
the magnitude of longer-term variability. In contrast to this, a higher value corresponds to
situations where the magnitude of long-term variability is substantially higher than the
short-term or beat-to-beat variability. The short-term scaling exponent correlates with the
LF/HF spectral ratio in controlled conditions (Tulppo et al. 2001a). Fractal-like analysis
of HRV has been used as a new approach to evaluate the mortality risk in various patient
groups, after the short-term scaling exponent (α1) was shown to be decreased in patients
with congestive heart failure (Peng et al. 1995, Ho et al. 1997). In selected patient
populations with depressed left ventricular function and/or heart failure, the decreased
short-term scaling exponent (α1) has been shown to be a strong predictor of cardiac and
total mortality (Mäkikallio et al. 1999b, Huikuri et al. 2000, Mäkikallio et al. 2001,
Perkiömäki et al. 2001).
The physiologic background between abnormal fractal correlation properties and
mortality risk has not been fully established. There is evidence that sympathetic
activation can cause this impairment. It has been shown that intravenous infusion of
norepinephrine leads to the reduction of short-term fractal scaling exponent values, and it
seems that high intrinsic levels of norepinephrine reflecting sympathoexcitation are
related to random RR interval dynamics in patients with heart failure (Tulppo et al.
2001b). This suggests that an increase in the randomness of short-term heart rate behavior
can be a specific marker of neurohumoral and sympathetic activation and thus be
associated with an increased risk for adverse cardiovascular events.
Power-law relationship. The distribution of the power spectral density can be
described by the linear inverse power-law relationship of power (on a logarithmic scale)
to frequency (on a logarithmic scale) (Saul et al. 1987, Bigger et al. 1996). This 1/f
27
fluctuation can be quantified by calculating the slope (β) of the power-law relationship,
which is usually done over the VLF and ULF bands (10−4–10−2), and it is also a marker of
the fractal-like correlation properties of RR interval data in this power range. A steep
slope (a high absolute value of β) reflects a high relative power of the low frequency as
compared to the higher frequency ranges. The power-law slope has been shown to be
lower in post-AMI patients either with reduced systolic function (Lombardi et al. 1996)
or who suffered from ventricular fibrillation as compared to controls without arrhythmia
risk (Mäkikallio et al. 1999c). A low power-law slope has predicted mortality in a general
elderly population (Huikuri et al. 1998), as well as all-cause but not arrhythmic mortality
in post-AMI patients with reduced systolic function (Mäkikallio et al. 1999a, Huikuri et
al. 2000).
Approximate entropy. Approximate entropy (ApEn) is a quantitative measure of the
regularity of RR interval data, or an index of complexity of the signal. The greater the
unpredictability, or complexity, in the RR interval time series, the larger the value of
ApEn. (Pincus & Viscarello 1992, Pincus & Goldberger 1994).
Other nonlinear methods based on chaos theory either have not gained wider
popularity or have not been proved predictive after myocardial infarction. These methods
include the so-called Lyapunov numerical methods, the characterization of correlation
dimension or Kolmogorov entropy and Coarse Graining Spectral Analysis (Goldberger
1996).
2.3.4.2 Baroreflex sensitivity
Baroreflex is a vagally mediated control system between heart rate and blood pressure,
where the RR interval is changed in response to changes in arterial pressure. The
response function is sigmoid, and a linear correlation is seen in the range of physiological
blood pressure changes (Rea & Eckberg 1987). The relationship between the rise in blood
pressure and decrease in RR intervals is called baroreflex sensitivity (BRS, ∆RRi/∆BP).
(Eckberg et al. 1971, Vanoli & Adamson 1994). The most popular method of measuring
BRS is by injecting boluses of phenylephrine intravenously (Smyth et al. 1969, Eckberg
et al. 1971), but it can be also measured during controlled respiration (Parati et al. 1988,
Parlow et al. 1995), with a Valsalva maneuver (Eckberg 1980, Airaksinen et al. 1993), by
performing cross-spectral analysis in a long-term recording of blood pressure and heart
rate (deBoer et al. 1987, Airaksinen et al. 1993, Airaksinen et al. 1997), by measuring the
rate-pressure response after injection of a vasodilator, such as sodium nitroprusside
(Pickering et al. 1972, Chen et al. 1982) or by the neck chamber technique (Eckberg
1980).
BRS cannot be reliably measured in a variety of post-MI patient groups, because the
measurement is sensitive to various common arrhythmias, such as atrial fibrillation and
ectopic beats. In some patients with poor peripheral circulation, noninvasive blood
pressure measurements are not reliable. The validity of the BRS value depends on the
correlation coefficient between RR intervals and blood pressure values. When the BRS is
clearly diminished or is absent, the correlation coefficient will be reduced, which makes
28
the classification of such tests more difficult. Therefore, the distinction between a truly
low BRS value and a unacceptable test result may complicated.
BRS is attenuated in hypertensive patients (Parati et al. 1988, Ylitalo et al. 1997) and
in patients with congestive heart failure (Eckberg et al. 1971, Grassi et al. 1995), in
whom reduced BRS is a marker of increased risk for nsVT (Mortara et al. 1997). It has
been suggested that a preserved baroreflex could protect against ventricular
tachyarrhythmias, as depressed BRS has been shown to be associated with increased
mortality after AMI (Farrell et al. 1992, Vanoli & Adamson 1994). In the ATRAMI study
(Autonomic Tone And Reflexes After Myocardial Infarction), decreased baroreflex
sensitivity ( < 3.0ms/mmHg) measured at < 28 days after an AMI predicted cardiac
mortality (RR 2.8, 95% CI 1.24–6.16) independently among a sample of 1284 patients
with a follow-up of 21 months. In patients over the age of 65, the prognostic value of the
BRS result was decreased (La Rovere et al. 1998). The predictive value of BRS has not
been confirmed in consecutive patient studies.
2.3.4.3 Heart rate turbulence
Heart rate turbulence (HRT) is a promising new method of analyzing heart rate behavior
after a ventricular ectopic beat in a 24-hour ECG recording. In HRT analysis the
variability of cycle length after a spontaneous post-ectopic pause is measured. Most
commonly, HRT is characterized by two parameters, turbulence onset (TO) and
turbulence slope (TS) (Schmidt et al. 1999). HRT is clearly correlated to BRS, i.e. it
represents vagal activity (Mrowka et al. 2000, Davies et al. 2001, Wichterle et al. 2002,
Lin et al. 2002). The flaw in HRT analysis is that at least one VPB is needed in the
recording and the patient has to be in sinus rhythm (Guzik & Schmidt 2002). It is likely
that the validity of the test is reduced if the number of VPBs is very low. This reduces the
number of patients in whom HRT analysis can be applied. HRT analysis has been applied
to older MPIP (Multicenter Post-Infarction Program) (Bigger, Jr. 1986), EMIAT (Julian et
al. 1997) and ATRAMI (La Rovere et al. 1998) databases: the combination of abnormal
onset and slope was the most powerful multivariate risk stratifier (all-cause mortality in
MPIP RR 3.2, 95% CI 1.7–6.0, p < 0.0001 and in EMIAT RR 3.2, 95% CI 1.8–5.6,
p < 0.0001; and fatal and non-fatal cardiac arrests in ATRAMI 6.87, 95% CI 3.1–15.5,
p < 0.0001) (Schmidt et al. 1999, Ghuran et al. 2002). HRT has not been studied in a
healthy population and a large prospective study is still needed for validation of this
method.
2.3.5 Changes in repolarization
Repolarization is the phase during which a myocardial cell transmembrane potential
returns to its baseline level following a passing depolarizing electrical impulse.
Repolarization takes place in wide areas simultaneously. The dispersion of repolarization
29
refers to the heterogeneity of this process, and it has been shown to be present in various
pathological cardiac disorders, creating a substrate for ventricular arrhythmias (Kuo et al.
1983).
2.3.5.1 QT interval
The QT interval in 12-lead ECG reflects the phases of de- and repolarization in the
myocardium. The principles of QT interval measurements have been described by
Lepeschkin and Surawicz (Lepeschkin & Surawicz 1952, Lepeschkin & Surawicz 1953).
There are plenty of confounding factors in the analysis of the QT interval, and no
accepted standardization of measurement of either QT interval or dispersion exists
(Statters et al. 1994). The reproducibility of both manual and automated measurements is
uncertain; in one report the reproducibility of automated interval measurements was high
but that of dispersion remained low (Savelieva et al. 1998). A relative error of 25–42%
has been published in inter- and intraobserver reproducibility (Priori et al. 2001), and so
far, no method has been standardized.
Despite its presumable simplicity and wide applicability, the measurement of the QT
interval and, especially QT dispersion (QTd), is impeded by the difficulties in defining
the exact end of the T-wave or the U-wave. In numerous ECG recordings, the end of the
T-wave cannot be reliably defined (Lepeschkin & Surawicz 1952, Lepeschkin &
Surawicz 1953, Campbell et al. 1985). This problem can be partly overcome by
measuring the distance from the beginning of the QRS complex to the apex of the T-wave
(QT peak or QT apex) instead of measuring the distance to the end of the T-wave (QT
end). In a study by Rudehill and colleagues, a close correlation was seen between the QT
peak and QT intervals of healthy subjects (Rudehill et al. 1986). Some automated
analysis systems are available, and they seem to be fairly reproducible, but they have not
been accepted for general use. Another problem in the analysis of the QT interval or QTd
is the effect of QRS prolongation. Therefore, measurement of the JT interval instead of
the QT interval has been recommended (Spodick 1992, Chaudry et al. 1994). However,
the JT interval has not been shown to be independent of the ventricular repolarization, but
it was paradoxically shorter in patients with QRS prolongation (Banker et al. 1997).
Quite often, patients with intraventricular conduction defects are simply excluded from
QT interval analysis.
The QT interval is longer in women (Lepeschkin & Surawicz 1953) and is shortened
as the heart rate is increased. It has been recommended that the QT interval be ratecorrected, and several rate-correction formulas have been reported. The most widely used
correction formula has been reported by Bazett (QTc = QT/√RR, where RR is the mean
R-to-R (Bazett 1920) and Friedricia (QTc = QT/3√RR) (Fridericia 1920). Linear formulas
have also been published, which have been derived from large populations (Sagie et al.
1992, Karjalainen et al. 1994).
The upper limits of normal values for corrected QT intervals have been suggested to
vary between 370–460 ms in men and 400–470 ms in women (Bazett 1920, Lepeschkin
30
& Surawicz 1953). A cutoff point at 440 ms has been suggested for the QTc interval as a
risk predictor (Schwartz & Wolf 1978, Ahnve et al. 1984, Algra et al. 1991).
Prolonged QT interval is associated with increased cardiac mortality in general
populations (Dekker et al. 1994, Mänttäri et al. 1997, Burton & Cobbe 2001), and its
prolongation has been associated with increased arrhythmic mortality after an AMI
(Schwartz & Wolf 1978, Ahnve et al. 1984). Underlying cardiac disease, such as LVH
(Oikarinen et al. 2001), may prolong the QT interval. The QT interval is lengthened in
patients with a history of myocardial infarction (Macfarlane et al. 1998). In the prethrombolytic era, the prolongation of a corrected QT interval was linked to a 2.16-fold
increase in SCD in a small case-control study (Schwartz & Wolf 1978). In the Rotterdam
QT study, a QTc ≥ 440 ms predicted sudden death in consecutive patients that were
referred to a 24-hour ECG recording (RR 2.3, 95% CI 1.4–3.9), but it was not related to
the SCD risk in patients with symptomatic heart failure or a an left ventricular EF ≤ 40%.
(Algra et al. 1991). The QTc interval has been shown to be prolonged in CAD patients
resuscitated from out-of-hospital ventricular fibrillation when compared to post-MI
control patients (Haynes et al. 1978). In another study, only the QT-peak but not QT-end
interval was shown to be lengthened in the ECG of patients suffering from SCD when
compared to controls (Mänttäri et al. 1997).
The QT interval is a dynamic parameter that changes constantly in the same subject
depending on various factors. The change in QT interval caused by heart rate is not
instantaneous, and it causes variance in the measurements (Sarma et al. 1987). Changes
in autonomic nervous system activity affect both depolarization and repolarization phases
in addition to heart rate, and thus act on the QT interval (Toivonen et al. 1997).
Therefore, the “normal” QT interval measurements should be performed under stable
conditions.
Various methods assessing QT interval dynamics during Holter recordings have been
proposed (Merri et al. 1992, Viitasalo & Karjalainen 1992). It has been suggested that
abnormal QT interval dynamics could be a marker of heart disease and increased risk for
ventricular tachyarrhythmias (Marti et al. 1992, Gill et al. 1993). In retrospective casecontrol studies, exercise induced prolongation of the QTc interval but not that of QTc
dispersion could differentiate high-risk post-AMI patients from those with a low risk (Yi
et al. 1998a), and during a Holter recording blunted circadian variation in QT or QTc
intervals and prolonged 24h-mean QTc intervals could suggest an increased risk of MI
after AMI (Yi et al. 1998b). In another small study, Homs et al have obtained similar
results (Homs et al. 1997). These findings have not been confirmed in larger consecutive
trials.
2.3.5.2 QT dispersion
QT or JT dispersion (QTd) is defined as the difference between the longest and the
shortest QT interval in the 12-lead ECG, and it has been suggested to represent
heterogeneity of repolarization in the cardiac muscle. The increase in dispersion is
attributed both to changes in activation time and duration of activation potential, and it
31
has been linked to ventricular arrhythmias. The arrhythmogenic mechanism is probably
due to the facilitation of re-entry (Spargias et al. 1999).
Like the QT interval, QTd is affected by a large number of factors. However, it is not
influenced by age or sex (Macfarlane 1998) and it is not influenced by changes in heart
rate during pacing in rabbits or healthy people (Zabel et al. 1997, Sporton et al. 1997).
There is evidence that QTd is increased when sympathovagal balance is shifted towards
sympathetic predominance (Ishida et al. 1997). In experimental animals with LVH, acute
ventricular dilatation or prolonged ventricular myocardial stretch, the dispersion of
repolarization is increased (Reiter et al. 1988, Kowey et al. 1991, Zabel et al. 1996). In
hypertensive patients LVH is associated with increased QTc or QTd (Perkiömäki et al.
1996, Ichkhan et al. 1997), and regression of LVH caused by antihypertensive treatment
leads in turn to a decrease in QTd (Karpanou et al. 1998). In addition, hypertrophic or
dilated cardiomyopathy as well as intraventricular conduction defects are independently
associated with increased QTc dispersion (Zaidi et al. 1997b).
Ischemia provoked by incremental atrial pacing (Sporton et al. 1997), an exercise test
(Stoletniy & Pai 1995, Stoletniy & Pai 1997, Naka et al. 1997) or balloon angioplasty
(Michelucci et al. 1996), as well as both acute and late myocardial infarction are likewise
associated with increased QTd (Gough et al. 1985, Kimura et al. 1986, Macfarlane et al.
1998). Reperfusion after balloon inflation causes an increase in QTd (Michelucci et al.
1996). After successful coronary angioplasty, QTd decreases (Yunus et al. 1997, Kelly et
al. 1997), and increases again if restenosis develops (Yunus et al. 1997). After AMI, the
effect of thrombolytic therapy and the patency of the infarct-related artery, history of
reinfarction, the size of the infarction scar and left ventricular EF are linked to QTd
(Moreno et al. 1994, Karagounis et al. 1998, Puljevic et al. 1998). After myocardial
infarction, sympathetic stimulation, acute volume load, reischaemia, and compensatory
ventricular hypertrophy of noninfarcted regions, have all been shown to increase QTd
(Zaidi et al. 1996) in relation to the site of the infarction (Mirvis 1985). According to a
study by Glancy et al., QTd is at its maximum at 3 days after the AMI, and decreases
thereafter (Glancy et al. 1996).
In a healthy population of 1501 adults, the QTd was measured with automated
analysis and varied around 24.5 ± 8.5 ms. Thus, for automated analysis a threshold value
of 50 ms is recommended (Macfarlane 1998, Macfarlane et al. 1998). In most studies,
where QTd has been measured manually, the average value was ≤ 40 ms (Surawicz
1996), but normal values of up to 71 ms have been reported (Davey et al. 1994). An
upper normal limit for a person with a structurally normal heart, has been suggested to be
70 ms ((Cowan et al. 1988, Kautzner et al. 1994, Perkiömäki et al. 1995). As a cutoff
point for risk stratification, Bogun et al. showed in their study that a QTd > 120 ms
differentiated the CAD patients with VT episodes, in comparison with patients with a
QTd < 90, in whom VT episodes were not seen (Bogun et al. 1996).
In 1994, Zareba et al. found that increased JT dispersion measured 1 to 6 months after
the index event predicted arrhythmic mortality (p < 0.01) in patients with unstable angina
or acute myocardial infarction (Zareba et al. 1994). In the Helsinki Heart Study, QT apex
dispersion was associated with a fatal event but not QT end dispersion (Mänttäri et al.
1997). In the study by Glancy et al., QTd did not predict mortality during a 5-year
follow-up time if measured 2 or 3 days after the infarction, but at 4 weeks it was
associated with increased mortality which reflected the fact that QTd did not normalize in
32
patients with a higher risk of dying (Glancy et al. 1995). In a retrospective population
study among patients with previous myocardial infarction, increased QTd was predictive
of both cardiac and sudden cardiac mortality, with a threshold value of 60 ms (TruszGluza et al. 1996), but in another study by Kautzner and Malik, QTd measured 3 to 15
days after the AMI was not predictive of SCD in a 1-year follow-up (Kautzner & Malik
1997). Perkiömäki et al. have shown that increased QTc dispersion is an independent
predictor of inducibility into sustained monomorphic VT after AMI, and that
postmyocardial infarction patients who had experienced VF or VT had a broader QTd
than the post-AMI controls (Perkiömäki et al. 1995, Perkiömäki et al. 1997). In a small
retrospective study, the majority of the patients whose myocardial infarction was
complicated with VT showed increased QTd in their 12-lead ECG when compared to
those without arrhythmias (Zaidi et al. 1997a).
In patients with chronic heart failure, those who experienced sudden death had greater
QTd than the survivors or patients experiencing death from heart failure or the survivors
(Barr et al. 1994). Fu et al. followed 163 patients with an EF ≤ 40% either caused by
ischaemic heart disease (126 out of 163 patients) or dilated cardiomyopathy and showed
that in these patients, JTc dispersion was a predictor of SCD or ventricular
tachyarrhythmia (Fu et al. 1997). In a parallel French study, the arrhythmia predicting
ability of QTd was limited only to patients with dilated cardiomyopathy and not to
patients with ischaemic cardiomyopathy (Galinier et al. 1998). Most recently, in a postAMI study of 280 patients, digitized measurement of QTd failed to predict adverse events
(Zabel et al. 1998), as opposed to the study by Spargias et al, in which greater QT or QTc
dispersion measured at a median of 2 days predicted increased long-term mortality in
patients with recent myocardial infarction and clinical signs of heart failure (Spargias et
al. 1999).
There are few studies on the effect of drugs on the QT interval effects of drugs that
have been shown to improve prognosis after AMI. Atenolol has been shown to decrease
QTd in patients with syndrome X (Leonardo et al. 1997), and the effects of sotalol or
amiodarone in patients with CAD have been more controversial, with either no change or
decrease in QTd (Day et al. 1991, Cui et al. 1994, Grimm et al. 1997), the work by Day
being the only post-AMI study (Day et al. 1991). Of ACE inhibitors, Barr et al. showed
that enalapril decreases QTc dispersion in patients with mild congestive heart failure
secondary to CAD (Barr et al. 1997).
2.3.5.3 T-Wave alternans
T-wave alternans (TWA) is both a physiological and pathological phenomenon, during
which the magnitude of the T wave alternates from beat to beat. Macroscopic TWA has
been associated with malignant arrhythmias (Raeder et al. 1992), and more recently,
analysis of TWA at the microvolt level has been introduced as a powerful approach to
evaluate the risk of life-threatening arrhythmias (Rosenbaum et al. 1994), though the first
patient series have been selected and rather small in size. The occurrence of TWA during
bicycle exercise has been shown to predict inducibility during EP study as well as
33
occurrence of ventricular tachyarrhythmias during one-year follow-up in 313 patients
referred for EP study (Gold et al. 2000).
TWA can be measured with special microvolt electrodes during pacing, either atrial or
ventricular, or exercise. In most patients with pathological TWA, the heart rate has to be
raised above a specific threshold level for alternans to appear (Hohnloser et al. 1997).
The most popular method of measurement is done using spectral analysis of 128
measurements taken on corresponding points of 128 consecutive T-waves. The peak at
0.5 cycles per beat (c/b) in the spectrum reflects TWA. TWA power (µV2) is quantified by
subtracting the power at the so-called noise frequency band from the total power at the
0.5 c/b, and the alternans voltage (µV) is calculated as the square root of the alternans
power. The alternans power has to exceed the standard deviation of the noise by threefold
or more (K-score ≥ 3) (Smith et al. 1988, Bloomfield et al. 2002).
The mechanism for the association of TWA with ventricular arrhythmias may be
explained by a transmural repolarization gradient, which in turn is critical to re-entrant
arrhythmogenesis (Yan & Antzelevitch 1998). The transmural polarization gradient in
turn is the result of intrinsic differences in the action potential duration of the cells
spanning the ventricular wall from endocardium to epicardium. The action potential
duration can be prolonged disproportionately in the M cells of the myocardium in
response to various agents and slowing of rate (Antzelevitch et al. 1991, Yan et al. 1998).
It has been shown that beta blocking therapy reduces the amplitude of TWA
(Klingenheben et al. 2001) and that amiodarone therapy reduces the prevalence of TWA
in a chronic ventricular tachyarrhythmic population (Groh et al. 1999).
In a selected high-risk population the occurrence of TWA at specific heart rate levels is
associated with increased vulnerability to ventricular tachyarrhythmias (Rosenbaum et al.
1994, Estes, III et al. 1997, Armoundas et al. 1998a, Armoundas et al. 1998b). In postAMI patients, there is evidence that TWA during the bicycle exercise test about three
weeks after the AMI could be used as a risk stratifier for ventricular tachyarrhythmias in
combination with late potentials (Ikeda et al. 2000). In a larger study of 850 post-AMI
patients, the positive predictive value of TWA performed 2.7 ± 5.4 months after the AMI
for SCD was only 7%, even if the RR was 5.9 (p = 0.007) in Cox multivariate analysis.
Only 13% of the patients in this study were on beta-blocking medication (Ikeda et al.
2002). So far, no TWA based treatment study has been published.
2.3.5.4 T-wave morphology and T-wave vector loop analysis
In the general population, ST-segment and T-wave abnormalities carry a risk of increased
cardiovascular mortality. In the novel quantitative T-wave analysis, the inaccuracies of
the conventional QT interval parameters are avoided, and vector loop analysis shows a
high reproducibility (Franz & Zabel 2000). It has been suggested that the information of
QTd is contained in the spatial T-wave loop (Badilini et al. 1995) and that QTd would be
a mere reflection of the interlead variation in the precordial projection of the T-wave loop
(Kors et al. 1999). In a small study, the 3D T-wave loops of post-MI patients were
rounder and showed a loss of planarity when compared with healthy controls (Badilini et
34
al. 1995), and similar findings have been seen in T-wave loops during ischemia
(Nowinski et al. 2000) T-wave loop dispersion measures the dissimilarities between Twaves from individual ECG leads, and thus reflects the T-wave loop vector variability
(Acar et al. 1999) In the elderly, abnormal T-loop morphology has been shown to be
associated with both cardiovascular and SCDs. The risk for fatal cardiac events was 4.3
(95% CI 3.0–6.4) in the Rotterdam study of 5781 elderly subjects (Kors et al. 1998). In a
German study of 280 AMI survivors, both T-wave loop dispersion and the cosine between
the depolarization and the repolarization gradient (TCRT) were independent predictors of
arrhythmic events in patients without LBBB (Zabel et al. 2002).
2.3.6 Ambulatory ECG recordings
2.3.6.1 Ventricular premature beats
Ventricular premature beats occur in healthy hearts, but their increased frequency has
been linked to cardiac and arrhythmic mortality. Without a structural heart disease,
however, their association with a fatal arrhythmia is weak (Kennedy et al. 1985,
Myerburg et al. 1989). A VPB can act as a trigger of ventricular tachycardia in a suitable
setting. A multitude of VPBs, on the other hand, may reflect the extent of damaged
myocardium and, thus, be a marker of the presence of arrhythmogenic substrate. In the
prethrombolytic era, frequent or complex premature beats were associated with SCD after
the infarction (Kotler et al. 1973, Ruberman et al. 1977, Ruberman et al. 1981, the
Multicenter Post-Infarction Research Group 1983, Bigger et al. 1984, Mukharji et al.
1984). The cutoff point for dichotomization in most studies has been 10 VPBs or more
per hour (the Multicenter Post-Infarction Research Group 1983, Bigger et al. 1984,
Mukharji et al. 1984). In the thrombolytic era, the GISSI-2 trial of 8676 patients with 24hour ECG recordings performed before discharge, showed more than 10 VPBs/hr in
19.7% of the patients. After adjusting for several risk factors, increased ectopy remained
a significant predictor of total 6-month mortality (RR 1.62; 95% CI 1.16–2.26) and of
sudden deaths (RR 2.24; 95% CI 1.22–4.08) (Maggioni et al. 1993). In a study of 680
patients, in whom the 24-hour ECG recording was carried out in a drug-free state from 6
to 10 days after AMI, the number of VPBs was higher in patients who died suddenly or of
cardiac causes as well as in patients with arrhythmic events during the follow-up time of
one year. The positive predictive accuracy was higher in patients who had received
thrombolysis (25.8% for thrombolyzed and 19.4% for patients not receiving
thrombolysis, at a sensitivity level of 40%) (Statters et al. 1996).
35
2.3.6.2 Nonsustained ventricular tachycardia and sustained ventricular
tachycardia
Nonsustained ventricular tachycardia (nsVT) is most commonly defined as at least 3
consecutive beats at the rate of more than 100/min, which are ventricular in origin,
ending spontaneously within 30 seconds (Buxton & Duc 2002). Even nsVT does not
seem to be associated with an increased risk for sudden death in healthy individuals
(Kennedy et al. 1985, Buxton & Duc 2002). In the thrombolytic era, the value of nsVT as
a risk predictor in post-MI patients has become controversial. In the GISSI-2 trial, nsVT
was seen in 6.8% of patients, and its presence did not worsen the prognosis (RR 1.20,
95% CI 0.80–1.79) (Maggioni et al. 1993). In a study of 325 consecutive AMI survivors,
the prevalence of nsVT was 9%, and nsVT predicted the combined endpoint of cardiac
death, sustained VT or resuscitated VF but not independently (RR 2.6; 95% CI 1.0–6.3;
p = 0.043) (Hohnloser et al. 1999). However, a post-AMI patient with nsVT and poor left
ventricular function who can be induced into sustained VT, has a high risk of SCD (Moss
et al. 1996, Buxton et al. 1999). Patients with symptomatic VT and also
hemodynamically stable sustained VT represent a high-risk group, which has been shown
in the Canadian Implantable Defibrillator Study (CIDS)(Connolly et al. 2000) and in the
Antiarrhythmics Versus Implantable Defibrillators study (AVID) (Antiarrhythmics versus
Implantable Defibrillators (AVID) Investigators 1997).
Ventricular fibrillation (VF) during hospitalization was not associated with a worse
outcome in patients in an Israeli study when compared to patients without VF (Behar et
al. 1993). If the patient experiences ventricular fibrillation in the absence of an ischemic
event or a triggering cause, the risk of a recurrent event is high (Baum et al. 1974).
2.3.7 Heart rate
It has been shown that the mean 24-hour heart rate has a moderately strong correlation
with the traditional HRV parameters, and an increased heart rate is an independent
predictor of SCD in general populations (Kreger et al. 1987, Kannel et al. 1990, Algra et
al. 1993, Wannamethee et al. 1995, Jouven et al. 1999, Palatini et al. 1999), as well as in
patients with ischemic heart disease. In an 8-year follow-up study, an increased heart rate
of ≥ 90/min was associated with a 5-fold increase in the rate of SCDs when compared to
a heart rate of ≤ 60/min (Shaper et al. 1993). In GISSI-2 patients, the increased heart rate
as measured at discharge from a 12-lead ECG predicted independently total mortality at 6
months after AMI among 7831 patients. The mortality was 0.6%, if the heart rate
was < 60/min, as compared to 14.3% for a heart rate of > 100/min (Zuanetti et al. 1998).
To a certain extent, high heart rates can be indicative of ventricular dysfunction and
decompensation, or inadequate beta-blocking treatment. High pulse levels can also
indicate increased sympathetic activity and a lack of parasympathetic activity.
Heart rate during exercise. During exercise, both sympathetic tone and the
concentrations of circulating catecholamines are increased. Together with vagal
withdrawal, these changes will lead to a rise in heart rate. In various populations, it has
36
been shown that chronotropic incompetence during exercise is a predictor of mortality
(Lauer et al. 1996, Lauer et al. 1998, Lauer et al. 1999). Poor heart rate recovery after
exercise is associated with an adverse prognosis (Cole et al. 1999).
2.3.8 Exercise test
The main indications for bicycle or treadmill exercise testing after myocardial infarction
in addition to risk stratification and the estimation of prognosis are the assessment of
functional capacity, adequacy of medical therapy or the evaluation of the need to use
other diagnostic or treatment options (Gibbons et al. 2002). The significance of the
exercise test has diminished, as increasing numbers of patients are referred for early
coronary angiography and revascularization treatment, and because the available
reperfusion and other effective medical therapies have decreased the mortality risk.
Ability to perform the exercise test and exercise capacity. Patients who are unable to
perform an exercise test, or have limited exercise capacity have a higher risk of adverse
events. In the TIMI II trial, 12-month mortality was higher in patients who could not
perform the exercise test (7.7%) as compared to those who could perform the test (1.8%)
(Chaitman et al. 1993). This was confirmed in the GISSI-2 database, where the 6-month
mortality rate was 7.1% in patients in whom the test could not be performed, 0.9% in
patients with a negative test result, 1.3% in patients with non-diagnostic test result and
1.9% in patients with a pathological test result (Villella et al. 1995). In the same study,
low work capacity, was an independent predictor of mortality (RR 1.8, 95% CI 1.1–3.0)
(Villella et al. 1995). In another series of 115 patients with documented myocardial
infarction and a left ventricular EF < 35%, exercise capacity remained a significant
independent predictor of death or reinfarction; the relative risk of death was 3.5 (95% CI
1.1–9.7) in the lowermost quintile ( < 4 METs) as compared to the uppermost quintile
( > 7 METs) (Pilote et al. 1989). Failure to achieve 5 METs during the test has been
associated with unfavorable prognosis in several other studies (Krone et al. 1987,
Stevenson et al. 1993a).
Exercise induced ischemia. In a large meta-analysis, exercise induced ST depression
was associated with an increased risk of cardiac death at one year (odds ratio 1.7) (Shaw
et al. 1996). In the GISSI-2 trial, symptomatic induced ischemia was an independent
predictor of 6-month mortality (RR 2.1, 95% CI 1.02–4.23) (Villella et al. 1995). In
contrast, in some studies, the prognostic significance of induced ST depression is limited
to predict future angina (Mickley et al. 1993), or recurrent ischemia (Stevenson et al.
1993a).
Ventricular arrhythmias during exercise test. In asymptomatic men the presence of
exercise induced VPBs was associated with a 2–3-fold increase in long-term cardiac
mortality (Jouven et al. 2000). In patients with suspected CAD but not previous
myocardial infarction, the occurrence of ventricular arrhythmias during treadmill testing
was associated with a 2.5-fold increase in the risk of cardiac death and nonfatal
myocardial infarction (95% CI 1.05–6.12) in univariate analysis during a 2.7-year followup (Elhendy et al. 2002).
37
Other variables. Chronotropic incompetence during exercise is linked to increased
mortality in various populations (Lauer et al. 1996, Lauer et al. 1998, Lauer et al. 1999).
Impaired exercise response in systolic pressure has been associated with increased
mortality after AMI (Arnold et al. 1993, Villella et al. 1995, Shaw et al. 1996). Low ratepressure product is also associated with an adverse prognosis (Villella et al. 1999).
2.3.9 Atrioventricular and intraventricular conduction abnormalities
In the Framingham study in both men and women with CAD, signs of Q-wave infarction
or intraventricular conduction block were risk markers for SCD (Kreger et al. 1987). The
patients who develop intraventricular conduction disturbances in conjunction with AMI
often experience greater myocardial damage, which partly explains the increased
mortality in these patients. On the other hand, an intraventricular conduction abnormality
present in the 12-lead ECG may be a sign of underlying cardiac disease (Denes 2001). In
patients with myocardial infarction, the presence of a bundle branch block identifies a
subset of patients at high risk (Col & Weinberg 1972, Priori et al. 2001).
Measuring the duration of the QRS complex from a 12-lead ECG is a simple and
inexpensive method of further assessing the risk of dying in post-AMI patients. A
prolonged QRS interval ( > 150 ms) was associated with a nonsignificant trend for a
greater benefit of ICD therapy in the MADIT II study than if the duration of the QRS
interval was ≤ 150 ms (Moss et al. 2002). In patients with anterior myocardial infarction,
the presence of a persistent intraventricular conduction abnormality, except for LAFB, is
associated with an unfavorable prognosis caused by an increased risk of SCD, partly by
the progression to complete atrioventricular block but mostly because of ventricular
tachyarrhythmias (Dhingra et al. 1978, Lie et al. 1978).
The use of thrombolytic therapy has reduced the incidence of atrioventricular
conduction disturbances. The development of atrioventricular conduction abnormalities is
more common among patients with an inferior myocardial infarction. The incidence of II
to III degree AV block in these patients was 12% in the TIMI II (Thrombolysis in
Myocardial Infarction II) trial (Berger et al. 1992). The block in these patients is usually
encountered at the level of the AV node, and thus has a better prognosis. In patients with
Mobitz type II second degree AV block or III degree AV block, survival can be improved
by permanent pacing. (Hindman et al. 1978a, Hindman et al. 1978b). The higher
incidence of SCDs among patients with bi- or trifascicular block and syncope or
intermittent III degree AV block, is not significantly reduced by permanent pacing - this
may signify that ventricular tachyarrhythmias may be the most frequent cause of SCD
(Peters et al. 1979). In a 2-year follow-up study of general pacemaker patients, the
sudden cardiac mortality was 4% during the first year, and 1.8%/year after that; severe
bradycardia, severe atrioventricular block and atrial fibrillation with low ventricular rate
before the pacemaker implantation were all associated with increased probability of SCD
when compared to other pacemaker indications, probably reflecting the extent of
myocardial damage (Zehender et al. 1992).
38
Lateral ST-depression and atrial abnormality 5–7 days after AMI were independent
predictors of cardiac death in a recent report of 1034 patients with a mean follow-up of
about 2 years (Perkiömäki et al. 2002).
2.3.10 Signal-averaged ECG
The presence of low amplitude signals in the end of the QRS complex or late potentials,
which can be assessed with signal-averaged ECG (SAECG) measurement after
amplification and filtering, reflects inhomogeneity in ventricular repolarization
(Breithardt et al. 1991). Thus, late potentials can act as a substrate for ventricular
tachyarrhythmias. It has been suggested that late potentials would better predict the
occurrence of sustained VT than that of VF or SCD (Vaitkus et al. 1991).
In the pre-thrombolytic era, late potentials were strongly predictive of adverse events
after an AMI (Simson 1981). In a meta-analysis, abnormal SAECG was associated either
with a 6-fold increase in the risk of sustained ventricular tachyarrhythmias and sudden
death when left ventricular systolic function was controlled, or an 8-fold increase
independent of Holter results (Steinberg et al. 1992). In later studies, the positive
predictive value has been low, which probably can be attributed to effective reperfusion
and revascularization. The negative predictive value remains high (McClements & Adgey
1993, El Sherif et al. 1995). In a CAST substudy, the positive predictive value was 21%
for ventricular arrhythmias with a negative predictive value of 97% (Denes et al. 1994).
SAECG is more often abnormal, if the infarct-related artery remains occluded
(Chandrasekaran et al. 1999). Recently, SAECG abnormalities among CHD patients with
decreased left ventricular EF referred for CABG did not identify a subgroup with an
increased risk of arrhythmic mortality after revascularization (Bigger, Jr. 1997). The
duration of the averaged QRS complex (QRSd) has been suggested to be the most
important predicting parameter (Malik et al. 1992, El Sherif et al. 1995).
In addition to conventional SAECG analysis, spectral analysis has been suggested.
Some controversy exists in the value of the spectral method. Bloomfield et al. found out
that abnormality in spectro-temporal analysis is only a weak predictor of arrhythmic
events after AMI (OR 1.8, p = 0.15) when compared to time-domain SAECG (OR 2.26,
p < 0.05); abnormality of both test results would result in a 3.3-fold risk of event
(Bloomfield et al. 1996). In another study, the spectral method had significantly better
positive predictive value than the conventional method of analysis for both cardiac death
and the combination of sudden arrhythmic death and arrhythmic events (Copie et al.
1996). The producibility of the spectral analysis method is worse than that of the
conventional analysis (Consensus group 1996).
39
2.3.11 Body surface mapping and magnetocardiographic mapping
In body surface mapping studies, a so-called QRST integral map can be created with the
help of a large number of unipolar electrodes. In post-MI patients, the recording of late
potentials has been refined with the help of body surface mapping (Ho et al. 1993). The
system has been also used in tachyarrhythmia mapping.
The depolarizing and repolarizing electrical currents in the myocardium create a
magnetic field, which can be measured with magnetocardiographic (MCG) equipment. In
contrast to ECG, magnetocardiography is supposed to be more sensitive to tangential
currents than radial ones, and probably it can record cardiac signals without being
affected by changes in intracardiac volume as much as conventional ECG (Siltanen
1989). In patients with a previous myocardial infarction, there is a difference between
patients with or without a propensity for ventricular tachyarrhythmia both in
magnetocardiographic late fields (Korhonen et al. 2000), the equivalent of SAECG late
potentials, as well as in magnetocardiographic intra-QRS fragmentation analysis
(Korhonen et al. 2001).
No larger studies have been published about the predictive value of either method in
post-AMI patients.
2.3.12 Natriuretic peptides and other vasoactive markers
Changes in the neuroendocrine system, and various neurohormonal markers such as
natriuretic peptides have been shown to provide prognostic information among patients
with CHF. The secretion and synthesis of A-type natriuretic peptide (ANP), N-terminal
pro-ANP (N-ANP), B-type natriuretic peptide (BNP) and N-terminal pro-BNP (N-BNP)
is increased by numerous mechanisms in patients with myocardial infarction, both in the
acute and chronic stages of the disease (Morita et al. 1993, Sumida et al. 1995, Yoshitomi
et al. 1998). The associated left ventricular dysfunction (Bonarjee et al. 1995, Uusimaa et
al. 1999), increased volume or pressure overload of the left ventricle, ischaemia, and
comorbid conditions (LVH, hypertension, diastolic dysfunction) cause neuroendocrine
activation, which can be quantified by measuring concentrations of vasoactive peptides or
neurohumoral markers. The peptide concentrations correlate with left ventricular systolic
function and with left ventricular remodeling after AMI (Motwani et al. 1993, Hall et al.
1994, Omland et al. 1996, Darbar et al. 1996, Richards et al. 1998, Yoshitomi et al. 1998,
Richards et al. 1999, Talwar et al. 2000, Crilley & Farrer 2001). The correlation of BNP
is best with left ventricular function both in the subacute phase and at 6 months after the
AMI (Uusimaa et al. 1999). ANP and NT-ANP showed a better correlation than BNP to
left ventricular dysfunction and overt heart failure in the subacute phase, but BNP
remained the best predictor of mortality (Omland et al. 1996). BNP is mainly of
ventricular origin, and it is released in proportion to intraventricular pressure or stretch
(Chen & Burnett 2000). The same factors can facilitate arrhythmogenesis by changing
electrophysiological conditions of the heart (Zabel et al. 1996). There is evidence that N-
40
BNP could be a better cardiovascular risk marker than BNP, because its concentrations
are proportionately higher in patients with cardiac impairment (Hunt et al. 1997)
Natriuretic peptides predict mortality when measured following AMI (Motwani et al.
1993, Hall et al. 1994, Omland et al. 1996, Darbar et al. 1996, Richards et al. 1998,
Yoshitomi et al. 1998, Richards et al. 1999, Talwar et al. 2000, Crilley & Farrer 2001). In
a recent study of 247 post-AMI patients, N-ANP predicted also long-term cardiac
mortality during a 10-year follow-up time (Retterstol et al. 2001). Most studies have been
performed either in the prethrombolytic era or in patients without optimal treatment,
except for the study by Richards et al., where 86% of the patients were on beta-blockers
(Richards et al. 1998). BNP seems to provide additional independent prognostic
information in the entire spectrum of acute coronary syndromes (ST-elevation and nonST-elevation infarctions as well as unstable angina pectoris) at 30 days or at 10 months,
at the cutoff point at the median of 80 pg/ml (de Lemos et al. 2001, Sabatine et al. 2002).
Comparable results for N-ANP were published by Jernberg et al: the 48-month mortality
for patients with N-ANP ≥ 550 pmol/l (upper limit of normal) had an independent 4.8fold risk (95% CI 1.5–15.0) of experiencing death (Jernberg et al. 2002). Recently, it was
shown that in patients with congestive heart failure in general and left ventricular
EF < 35%, increased concentrations of BNP but not of N-ANP predict SCD in Cox
multivariate analysis: with a cutoff point of 130 pg/ml the positive predictive value of the
test was 19%, and the negative predictive value 99% (Berger et al. 2002). In this study,
however, only a minority of the patients were on beta-blocking medication (18% in the
deceased group and 33% in the group of survivors). The ability of BNP to predict the
finding of SCD has not been confirmed in general post-MI populations.
Of other neuroendocrine factors that are elevated in heart failure, adrenomedullin has
been shown to be associated with long-term mortality after AMI (Richards et al. 1998,
Nagaya et al. 1999). There is also evidence that endothelin-1 (Omland et al. 1994,
Uusimaa et al. 1999, Berger et al. 2002) can have prognostic value at least in patients
with CHF. Measurements of renin, vasopressin and norepinephrine seem to lack
prognostic benefit (Rockman et al. 1989, Richards et al. 1998).
2.4 Invasive risk stratification after acute myocardial infarction
2.4.1 Electrophysiological testing
Electrophysiological (EP) testing is an invasive method for assessing the risk of a
malignant arrhythmia, but it cannot be used in the routine risk stratification after acute
myocardial infarction, mostly because of the limited availability and relatively high
expenses of the study. During EP testing, the ventricle is paced at various cycle lengths
with the introduction of 1–3 extrastimuli. Induction of sustained monomorphic VT is the
endpoint of this study; induction of VF or polymorphic VT is an unspecific finding. In
patients with sustained VT or cardiac arrest without a transient trigger, inducibility is
linked to an increased risk of recurrence of arrhythmias (Brugada et al. 1984, Eldar et al.
41
1987). In the reperfusion era, the incidence of inducibility of VT in post-AMI patients has
declined (Brugada et al. 1986, Kersschot et al. 1986). In a study among post-AMI
patients with an EF ≤ 40%, inducibility was a specific (99%) but insensitive (50%) risk
indicator (Zoni-Berisso et al. 1996). In various other studies programmed electrical
stimulation has often produced false negative results and thus it has not had any value in
predicting mortality or subsequent arrhythmic events (Peterson et al. 1997, Huikuri et al.
2001). In these cases, left ventricular EF has been assessed to be superior in risk
stratification to programmed electrical stimulation. Thus, in the general post-AMI
population, programmed stimulation is not recommended for assessing the risk of
arrhythmic events, because the noninducibility does not imply a lack of risk of recurrent
life-threatening arrhythmias (Crandall et al. 1993, Wellens et al. 1997, Buxton et al.
1999). However, MADIT (Moss et al. 1996) and MUSTT (Multicenter Unsustained
Tachycardia Trial) studies (Buxton et al. 1999) showed a benefit of conducting an EP
study in patients with nsVT and EF ≤ 35% or 40%, respectively. Patients with CAD, left
ventricular dysfunction and asymptomatic, unsustained ventricular tachycardia in whom
sustained ventricular tachyarrhythmias cannot be induced had a lower five-year all-cause
mortality (44% vs. 48%, p = 0.005) than similar patients with inducible tachyarrhythmias.
Their deaths were also less likely to be classified as tachyarrhythmic. In conclusion, the
majority of post-AMI patients, who are at risk of ventricular arrhythmias, the prognostic
value of EP testing remains uncertain (Buxton et al. 2000).
2.4.2 Cardiac catheterization
At present, routine cardiac catherization is recommended for patients without serious
comorbidity suffering from non-ST-elevation acute myocardial infarction (Braunwald et
al. 2002). The benefit of active revascularization in these patients leads to a decrease in
the combined endpoint of mortality and recidive infarctions (Stenestrand & Wallentin
2002). No reduction has been shown in the incidence of SCD. Among patients with STelevation, if primary angiography has not been performed, cardiac catherization is
recommended for patients with clinical markers of increased risk (Van de Werf et al.,
2003).
2.5 Combining test results in risk stratification after acute
myocardial infarction
It is unclear, which combination of various risk factors provides the strongest predictor of
total, cardiac or arrhythmic mortality in the era of thrombolytics, effective secondary
prevention and invasive revascularization strategies. Combining different variables that
may present diverse aspects of risk of SCD is logical, as suggested by the task force
report (Priori et al. 2001). However, many of the risk variables correlate with each other
42
or are inter-related, e.g. they are clearly dependent on the activity of the autonomic
nervous system or reflect the site or extent of myocardial damage. In most studies, the
addition of left ventricular EF into the models improves positive predictive accuracy. In
the ATRAMI trial a combination of low baroreflex sensitivity, nsVT and decreased left
ventricular EF implicated a 22-fold risk increment of SCD (La Rovere et al. 1998, La
Rovere et al. 2001). In the same study, the combination of low HRV and reduced BRS
had a positive predictive value of 15% of predicting death at the 1-year follow-up; if
decreased systolic function (left ventricular EF < 35%) was added to the model, the
positive predictive value was further increased. In the large prophylactic ICD studies
different risk predictor combinations have been used. In the MADIT study, the
combination of nsVT with reduced systolic function EF ( ≤ 35%) after the infarction and
inducible nonsuppressible ventricular tachycardia demonstrated a reduction in the total
mortality with ICDs (RR 0.46 in the defibrillator group, 95% CI 0.26–0.82) (Moss et al.
1996), and in the MUSST study, the combination of nsVT, EF ≤ 40% and inducibility
indicated CAD patients who gained benefit from an ICD (RR 0.24, 95% CI 0.13–0.45 for
cardiac arrest or arrhythmic death) (Buxton et al. 1999). The EMIAT study suggested that
post-MI patients with EF ≤ 40% and low HRV (HRVI ≤ 20 U) might benefit from
treatment with amiodarone, as the reduction in cardiac mortality was 66% (p = 0.0054) in
the amiodarone vs. placebo group (mortality 4.4% vs. 12.8%) especially if the patient had
suffered his first infarction (mortality reduction on amiodarone 49.4%, p = 0.046), the
patient was already on beta-blocking therapy (reduction 69.0%; p = 0.047), or the patient
had a high heart rate ≥ 75/min (37.9%, p = 0.054) (Malik et al. 2000). However, the
proportion of patients with pathological multiple risk parameters is low. The predictive
value of the combined test will suffer, because the majority of the adverse events will
take place in the “low-risk” group, i.e. the test pattern will be sensitive but not specific.
No combination seems to reach a positive predictive value > 40% within reasonable
levels of sensitivity. Logically, an effective combination includes both markers of damage
as well as markers of autonomic imbalance. On the other hand, if all the risk parameters
are in the desired range, then the patient will undeniably fall into the category with very
favorable prognosis; e.g., in the ATRAMI trial the 1-year mortality was only 1%, if both
SDNN and BRS were well preserved (La Rovere et al. 1998).
2.6 Prevention of mortality after acute myocardial infarction
2.6.1 Optimizing medication
The target of the optimization of medication after AMI has been both to prevent new
ischemic events and to stop the progression of CAD. In addition to this, it has been
possible to reduce the number of arrhythmic events especially with beta-blocking therapy.
This means inevitably that the treatment has to be furthermore aimed at controlling all the
risk factors associated with the progression of CAD. As the effect of multiple prognostic
risk factors is additive, special attention must be paid to ensure that the patient will
43
receive both the most effective pharmacological and non-pharmacological therapies
including controlling hyperglycemia, smoking cessation, dietary modification, weight
reduction, exercise and cardiac rehabilitation programs.
The treatment of other risk factors is important in preventing premature death after
AMI. With modification of the risk factors, the progression of CAD can be slowed down,
which should decrease the frequency of SCDs. For example, as far as hypertension is
concerned, in a meta-analysis of studies on antihypertensive treatment in diastolic
hypertension, there was a 14% reduction in coronary death or non-fatal myocardial
infarction (MacMahon et al. 1990, Collins et al. 1990a, Collins et al. 1990b, Collins &
MacMahon 1994). On the other hand, in the elderly with isolated systolic hypertension,
antihypertensive treatment reduced mortality risk by 17% and the incidence of
myocardial infarctions by 31% (Staessen et al. 1999).
Antiplatelet therapy. In a large recently published meta-analysis, acetosalicylic acid
(aspirin) therapy is associated with a 25% reduction in reaching the combined endpoint of
myocardial infarction, stroke, or cardiovascular death among patients with prior
myocardial infarction and a 30% reduction in patients suffering from AMI
(Antithrombotic Trialists' Collaboration 2002). There is also some evidence that
treatment with clopidogrel could be of benefit at least in patients who do not tolerate
acetosalisylic acid (CAPRIE Steering Committee 1996).
Beta-blockers. Beta-blocking medication is recommended for all patients after
myocardial infarction in recent guidelines. Until recently, only less than half of eligible
patients received beta-blockers after infarction, especially outside Finland and Sweden
(Smith & Channer 1995, Viskin & Barron 1996, Gottlieb et al. 1998), and their
underutilization is associated with an increase in adverse events (Soumerai et al. 1997).
There still exists too much concern about the side effects and contra-indications of betablocking drugs. In most studies the percentage of patients who tolerate this effective
therapy is above 80–90% if extra attention is paid to the adherence to the medication and
care is taken to slowly advance to the effective dose and also if the drug to is changed to
another from the beta-blocking group if side-effects are seen. In a quite recent metaanalysis of 31 trials among 54,234 post-AMI patients, beta-blocking therapy was
associated with a 23% decrease in the odds of death in long-term trials (95% CI 15%–
31%), and in a registry study, a 40% benefit was reported (Gottlieb et al. 1998). Some
benefit may be lost in drugs with intrinsic sympathomimetic activity. Most of the
evidence available is for propranolol, timolol and metoprolol. (Freemantle et al. 1999). In
13 trials that included data on SCD, the average incidence of SCD was decreased from
51% to 43% (Nuttall et al. 2000). Most recently, the benefit of beta-blocking therapy has
been proven in patients who suffer from advanced congestive heart failure, whether it is
ischemic in origin or not (MERIT-HF investigators 1999, Dargie 2001). Metoprolol
reduced mortality by 36% (from 11% to 7%) in patients with EF ≤ 40%; half of the 3991
patients had a previous myocardial infarction, and the death had been classified as sudden
cardiac in 60% of the cases. The decrease in sudden death rates in this study was 41%
(MERIT-HF investigators 1999). Bisoprolol has been shown to decrease the rate of SCDs
by 42% among patients with congestive heart failure (CIBIS-II investigators 1999), and a
carvedilol study showed a similar trend toward reduction in SCDs (Dargie 2001). It
seems that patients with reduced left ventricular function acquire the most benefit from
beta-blocking therapy, although previously, in these patients beta-blockers were often
44
regarded as contra-indicated. The effect of beta-blocking therapy remains independently
beneficial even after the introduction of new medical and invasive treatment strategies.
Patients not able to take beta-blockers could benefit from heart rate slowing calcium
channel blockers in terms of reducing the risk of a reinfarction, and slightly that of allcause mortality if the left ventricular systolic function is preserved (DAVIT II
investigators 1990).
Amiodarone. Amiodarone is a class III antiarrhythmic drug with both a sodium and
beta-blocking effect. The EMIAT study suggested the possible benefit of amiodarone
therapy after AMI: patients with reduced systolic function and low HRV showed a
reduction of 66% in cardiac mortality in the amiodarone group (mortality 4.4% on
amiodarone vs. 12.8% on placebo) especially if the infarction was the patient’s first
(mortality reduction on amiodarone 49.4%, p = 0.046), if the patient was already on betablocker therapy (reduction 69.0%; p = 0.047), or if the patient had a high heart
rate ≥ 75/min (37.9%, p = 0.054). There was a non-significant trend for all-cause
mortality in the amiodarone group (reduction of 23.2%) (Malik et al. 2000). In the
CAMIAT, amiodarone was shown to reduce the incidence of ventricular fibrillation or
arrhythmic death by 48.5% (95% CI 4.5-72.2, p = 0.016) in post-AMI patients with
frequent or repeated VPBs ( ≥ 10 per hour or ≥ 1 run of ventricular tachycardia) (Cairns
et al. 1997). The most benefit from amiodarone seems to be acquired when it is
prescribed in combination with beta-blockers (Cairns et al. 1997, Boutitie et al. 1999),
Further prospective trials should be performed to validate these results because no
survival benefit was seen in the primary studies (Huikuri et al. 2001). In the secondary
prevention of SCD regardless of the cause, amiodarone seems to be the most effective
drug (The CASCADE Investigators 1993, Antiarrhythmics versus Implantable
Defibrillators (AVID) Investigators 1997, Connolly et al. 2000, Kuck et al. 2000).
Angiotensin-converting enzyme inhibitors. ACE inhibitors have been shown to reduce
mortality in patients with low EF and/or clinical heart failure in post-AMI patients
(Pfeffer et al. 1992, The Acute Infarction Ramipril Efficacy (AIRE) Study Investigators
1993, GISSI-3 Study Group 1994), and there is evidence that they are of benefit in all
patients with CAD (Yusuf et al. 2000). Under the ACC/AHA guidelines, ACE inhibitors
are recommended for all post-MI patients (Braunwald et al. 2000). Treatment with ACE
inhibitors will result in the prevention of the progression of heart failure. There is also
some indirect evidence that treating patients with left ventricular dysfunction with ACE
inhibitors will not only reduce total and cardiac mortality but will also result in a lower
number of SCD: for example, in the TRACE study the reduction was 24% (RR 0.76; 95%
CI 0.59–0.98) (Kober et al. 1995). A meta-analysis of 15 post-MI trials with 15104
patients showed a decreased risk of all-cause mortality (RR 0.83; 95% CI 0.71–0.97), of
cardiovascular mortality (RR 0.82; 95% CI 0.69–0.97) and of SCDs (RR 0.80; 95% CI
0.70–0.92) (Domanski et al. 1999).
Lipid lowering drugs. Lipid lowering studies with HMG-CoA reductase inhibitors
(statins) which have included patients with previous myocardial infarction have shown a
substantial decrease in mortality (The Scandinavian Simvastatin Survival Study Group
1994, Sacks et al. 1996, The Long-Term Intervention with Pravastatin in Ischaemic
Disease (LIPID) Study Group 1998) but their effect in lowering the incidence of
arrhythmic events has not been proven. It is probable, however, as the incidence of nonfatal myocardial infarctions and CAD deaths will be reduced by 30–40%. Their plaque-
45
stabilizing effect could be associated with a reduction in arrhythmic events. Despite the
improvement of lipid profile by hormone replacement therapy, it does not reduce the
number of cardiac events but can actually lead to the increased likelihood of them.
Other drugs. The RALES (Randomized Aldactone Evaluation Study) showed that
adding spironolactone to the medication of patients with severely depressed left
ventricular function led to a significant reduction in heart failure and SCDs; however, the
study was not a post-MI study (Pitt et al. 1999). Some data also exist suggesting that
supplemental n-3 polyunsaturated fatty acids could lead to a reduction in all-cause and
cardiac mortality after myocardial infarction (Gruppo Italiano per lo Studio della
Sopravvivenza nell'Infarto miocardico 1999), and there could be an associated effect in
the reduction of SCD (Albert et al. 1998).
2.6.2 Revascularization therapy
Early revascularization in the setting of AMI is associated with a substantial reduction in
1-year mortality (Stenestrand & Wallentin 2002). Coronary bypass grafting improves the
prognosis among patients with chronic stable angina in high- and medium-risk patients
with a reduction in mortality (Yusuf et al. 1994). The benefit of aggressive
revascularization has recently been clearly established in non-ST-elevation myocardial
infarctions and in unstable angina. In most studies, the reduction is seen in the combined
endpoint of cardiovascular events, but it is probable that such therapy can also reduce the
number of SCDs by diminishing the number of reinfarctions.
Reperfusion therapy of AMI reduces in-hospital mortality. Primary percutaneous
coronary intervention (PCI) is considered to lead to a greater reduction in mortality than
thrombolysis, and it is possible that the effect on SCD is similar (Priori et al. 2001). If the
infarct-related artery remains occluded, the incidence of ventricular arrhythmias and SCD
is increased, probably because of electrical instability at the border zone of infarction. In
a multivariate analysis of a prospective series of 173 potentials, the occluded infarctrelated artery remained an independent predictor of arrhythmic events (Hohnloser et al.
1994), and in another study the absence of flow or impaired flow through the infarctrelated artery was associated with susceptibility to ventricular tachyarrhythmias
independently of left ventricular systolic function (Huikuri et al. 1996a) 3 months or
more after AMI. The occluded infarct-related artery is linked to the presence of late
potentials (Hohnloser et al. 1994) and reduced BRS (Mortara et al. 1996), possibly also
reflecting the larger extent of myocardial damage. Infarct-related artery patency leads to
the preservation of left ventricular function and volume (Jeremy et al. 1987, Pizzetti et al.
1996).
46
2.6.3 Implantable cardioverter-defibrillator
Improved survival has been reported in several primary and secondary prevention studies
among high-risk patients after acute myocardial infarction.
Primary prevention. In the MADIT patients with previous myocardial infarction, nsVT
and EF ≤ 35% the mortality was decreased by 54% in the implantable cardioverterdefibrillator (ICD) group (Moss et al. 1996). MUSTT included 2139 patients with CHD
or previous MI, a left ventricular EF ≤ 40% and symptom-free, non-sustained ventricular
tachycardia, in whom ICD therapy significantly reduced the risk of arrhythmic death or
cardiac arrest (RR 0.24; 95% CI 0.13–0.45: p < 0.001) during a 5-year follow-up when
compared to control patients with no antiarrhythmics or EP-guided antiarrhythmic drug
therapy. (Buxton et al. 1999). In the CABG-Patch (Coronary Artery Bypass Graft Patch)
study, the patients who were referred for CABG and had a left ventricular EF ≤ 35% and
abnormalities in SAECG, did not show a better prognosis if implanted with an ICD
(Bigger, Jr. 1997). MADIT II showed a benefit for all post-AMI patients with an
EF ≤ 30%, the reduction in mortality being 31% during a follow-up time of 20 months
(Moss et al. 2002). It must be borne in mind that in advanced cases of heart failure
bradyarrhythmia may be the underlying cause of SCD more often and these deaths can
also be prevented by ICD because of the built-in pacemaker function in all these devices.
Among patients with reduced left ventricular EF, non-sustained VT and inducible
sustained VT according to the primary intervention studies, ICD therapy will improve the
prognosis, mostly due to a decrease in arrhythmic risk. The ongoing BEST plus ICD
(Beta-blocker Strategy plus Implantable Cardioverter) includes patients with a recent
AMI (5–21 days) and EF ≤ 35% and either increased ectopy, decreased HRV or positive
SAECG. The patients are on intensive metoprolol therapy with or without an ICD
(Raviele et al. 1999).
Secondary prevention. In post-AMI patients resuscitated from SCD with inducible
polymorphic ventricular tachycardia or ventricular fibrillation or with an anatomical
arrhythmic substrate, revascularization can be insufficient in preventing future malignant
arrhythmias. The only evidence-based therapy is the implantation of an ICD (Huikuri et
al. 2001). In inducible patients, post-operative retesting is essential, as only 50% of cases
will be suppressed by cardiac surgery alone (O'Rourke 1992). Despite a negative postoperative test, however, the patient can still belong to a group with a considerably high
risk of SCD (Daoud et al. 1995). In large secondary prevention studies after resuscitation
(Antiarrhythmics versus Implantable Defibrillators (AVID) Investigators 1997), the
CASH (Cardiac Arrest Study Hamburg) (Kuck et al. 2000) and the CIDS (Canadian
Implantable Defibrillator Study) (Connolly et al. 2000), more than half of the patients had
suffered from a prior MI, and about 80% of the patients had CHD. Therefore,
generalization is probably acceptable for most post-AMI patients with similar risk
profiles. AVID (Antiarrhythmics Versus Implantable Defibrillators) study showed lower
mortality in ICD patients during a 3-year-follow-up (RR 0.69, 95% CI 0.48–0.90,
p = 0.009) when compared to the control group which was predominantly on amiodarone
therapy. However, in a subgroup of patients with a higher EF (35–40%), the patients did
not benefit from ICD therapy over medical therapy (Domanski et al. 1999). In CASH and
CIDS studies there was a trend towards improved survival for patients treated with an
47
ICD. CASH and AVID included patients with preserved left ventricular EF in addition to
patients with a low EF. In a meta-analysis of these studies, ICD therapy was significantly
associated with a better prognosis (RR for mortality 0.73; 95% CI 0.59–0.89) during the
time of 6 years.
In a recent task force report a class I recommendation for ICD therapy was made for
post-MI patients with a reduced left ventricular EF ( < 40%), clinical nsVT and sustained
ventricular arrhythmia in EP study (B level of evidence) based on MADIT and MUSTT
studies (Task Force of the European Society of Cardiology and the North American
Society of Pacing and Electrophysiology 1996). In the MADIT II trial, the implantation
of an ICD improved survival among patients with a left ventricular EF ≤ 30%, with a
31% reduction in the risk of death (95% CI 0.07–0.49, p = 0.016) (Moss et al. 2002).
Accordingly, a class IIa recommendation for ICD implantation among such patients was
recently made by the European Society of Cardiology (Priori et al. 2003).
2.6.4 Catheter ablation and surgical treatment of ventricular
tachyarrhythmias
In drug-refractory forms of ventricular tachycardia in post-MI patients, the catheter
ablation has been curative in 60–90% of cases. The recurrence rate is up to 40%, and the
rate of acute complications is 2%–5% (Stevenson et al. 1993b, Stevenson et al. 1998,
Stevenson & Soejima 2002) Catheter ablation is recommended for sustained and
hemodynamically tolerated VT, and the survival effect of this therapy is probably not
substantial (Priori et al. 2001).
Surgical ablation of VT and the excision of possible left ventricular aneurysm can be
considered especially for post-AMI patients who are referred for coronary artery bypass
grafting, or for patients in drug refractory categories with an acceptable risk of surgery.
The surgical therapy probably does not have a significant effect on survival (van Hemel
et al. 1989, van Hemel et al. 1996).
3 Aims of study
The aim of the present study was to find out how mortality could be predicted by noninvasive means of risk stratification in post-AMI patients who were discharged alive from
the hospital and treated optimally according to contemporary guidelines. This study also
aimed to study the epidemiological pattern of all-cause, cardiac, and arrhythmic mortality
in this patient group. The specific goals of the papers were as follows:
1. To measure the effect of sampling frequency in HRV analysis on the results of
traditional and new dynamic HRV analysis methods in both post-AMI patients and
healthy subjects.
2. To assess the prognostic value of dynamic fractal-like scaling HRV analysis in
consecutive post-AMI patients.
3. To study TWA as a marker of mortality during a predischarge exercise test after AMI.
4. To assess the prognostic power of natriuretic peptides in post-AMI patients.
5. To evaluate the proportion and time dependence of SCD and arrhythmia events in a
consecutive post-AMI patient series with optimized medical therapy, and to define
the power of risk predictors of SCD and arrhythmia events.
4 Subjects and methods
4.1 Subjects
The subjects in study I included ten patients with recent AMI treated in Oulu University
Hospital and ten healthy subjects. The patients in studies II–V were sampled in a
consecutive series of AMI patients treated in Oulu University hospital during 1996–2000.
This patient population formed the core of the single-center prospective study, multiple
risk factor analysis trial (MRFAT). Study II also included post-AMI patients from
consecutive series in the hospitals of Gentofte and Glostrup in Copenhagen, Denmark as
a part of the Nordic implantable cardioverter-defibrillator pilot study (Thomsen et al.
1999). The clinical characteristics of the post-AMI study populations (I–V) are seen in
Table 1.
The patients were recruited to participate during the first seven days after the AMI.
The diagnosis of AMI was confirmed with two of the following three parameters: (1)
chest pain or dyspnea lasting for at least 30 min, (2) elevation of myocardial enzymes up
to > 2x the upper limit of the reference value, which could not be attributed to any other
condition, and (3) ischemic ECG changes on admission or any later change in the ECG
caused by AMI. The exclusion criteria were advanced age ( > 75 years), unstable angina
at recruitment, dementia, alcoholism, drug abuse, or any other condition that could impair
capacity for informed consent, as well as non-sinus rhythm in ECG. The patients who
underwent coronary bypass surgery before discharge (I–IV) or were not discharged alive
from the hospital were not included in the analysis. Study V also included patients who
went to CABG.
50
Table 1. Clinical characteristics of the post-AMI study populations (I–V).
Gender
males/females
Age
History
Hypertension
Diabetes
Previous infarction
CHF
Smoking (current or
previous)
Infarction
Anterior wall
Non-Q-wave
Thrombolysis or
primary PTCA
Ejection fraction
NYHA class
I–II/III–IV
(at discharge)
Therapy
Amiodarone
Beta blockers
Calcium antagonist
ACE inhibitor or
ATII receptor
antagonist
ASA or warfarin
Statin
Study I
(n = 10)
Study II
(n = 697)
Study III
(n = 379)
Study IV
(n = 521)
Study V
(n = 675)
8/2
(80%/20%)
62.9 ± 10.0
521/176
(75%/25%)
61.2 ± 10.1
272/107
(72%/28%)
62.4 ± 10.1
403/118
(77%/23%)
61.1 ± 10.0
489/161
(75%/25%)
61.8 ± 9.7
300 (44%)
135 (20%)
139 (20%)
*
*
196 (52%)
85 (22%)
80 (21%)
45 (12%)
243 (65%)
249 (48%)
108 (21%)
105 (20%)
51 (10%)
359 (69%)
333 (49%)
154 (24%)
134 (21%)
77 (12%)
443 (69%)
5 (50%)
301 (45%)
297 (44%)
183 (48%)
160 (42%)
243 (47%)
230 (44%)
303 (47%)
295 (44%)
43 ± 10
329 (50%)
45.9 ± 9.9
172 (45%)
45.0 ± 9.6
245 (47%)
45.6 ± 9.3
300 (46%)
45.1 ± 9.3
534/67
(89%/11%)
455/62 (88%/12%)
321/53
(76%/14%)
553/93
(86%/14%)
8 (1%)
559 (84%)
*
272 (41%)
6 (2%)
367 (97%)
22 (6%)
172 (46%)
8 (2%)
504 (97%)
29 (6%)
227 (44%)
12 (2%)
628 (95%)
50 (8%)
239 (39%)
*
*
357 (95%)
125 (33%)
495 (95%)
235 (45%)
618 (95%)
239 (37%)
3 (30%)
* Data not available from the Danish patients.
Except for the patients treated in the hospitals of Gentofte and Glostrup (II), the treatment
of the patients was uniform (I–V). To optimize the treatment, beta-blocking drugs were
prescribed to all patients and ACE-inhibitors were prescribed to patients with a left
ventricular ejection fraction below 40% whenever no contra-indications for such
medications existed. When possible, the dose of beta-blocking therapy was adjusted to
achieve a resting heart rate between 50–60 bpm. Special emphasis was paid on long-term
compliance with the beta-blocking medication, which was not discontinued without an
absolute contraindication or disabling side effects. Other cardiac medication was
prescribed and optimized by the primary physicians of the patients. Patients were referred
for revascularization according to contemporary guidelines (Ryan et al. 1996).
All the patients were required to give informed consent, and the ethical committee of
the institution approved the study.
51
4.2 Methods
4.2.1 Follow-up and endpoints
The patients went through a clinical examination, and a complete history was taken (I–
V). The patients or their families were contacted via telephone at 6, 24, 36, 48 and 60
months after the AMI, and the patients had a clinical visit at 12 months. If the patient
could not be contacted, the survival status was checked during the follow-up from the
official register
In cases of death, the reasons for death were verified from the hospital and autopsy
records and from either relatives or the primary physicians who had witnessed the death.
(II–V). The mode of death was classified as cardiac or noncardiac, with cardiac mortality
being the primary endpoint. The cardiac deaths were grouped into sudden and nonsudden deaths. Two independent endpoint committees defined the mode of death, one at
the University of Oulu and the other at the University of Miami (IV–V).
Cardiac death was defined as sudden, if it was (1) a witnessed death occurring within
60 minutes from the onset of new symptoms unless a cause other than cardiac was
obvious, (2) unwitnessed death ( < 24 hours) in the absence of pre-existing progressive
circulatory failure or other causes of death, or (3) death during attempted resuscitation
(Kim et al. 1993, MERIT-HF investigators 1999) (IV–V). In addition to the clinical
definition of SCD, documented ventricular tachyarrhythmia events were separately
defined as events with a high probability of prevention by an ICD. These events were
defined as (1) successful resuscitation from sustained VT or VF, or (2) probable
arrhythmic death. Probable arrhythmic death was defined as an SCD with verified ECG
recordings of VT or VF at the time of resuscitation, or a witnessed, instantaneous death
without evidence of a non-arrhythmic cause from an autopsy (V).
4.2.2 Left ventricular ejection fraction
All patients were examined with echocardiography using Hewlett-Packard 77020A or
SONOS 5500, or Toshiba SSA-380A system 2 to 7 days after the AMI. The left
ventricular systolic function was measured with 2-D echocardiography using Wall
Motion Index (WMI) analysis. The left ventricle was divided into 16 segments, each of
which was given a score for its motion (−1 for dyskinesia, 0 for akinesia, 1 for
hypokinesia, 2 for normokinesia and 3 for hyperkinesia) (Berning & Steensgaard-Hansen
1990). Wall motion index (WMI) is the mean score of all the segments, and a rough
estimate of the left ventricular ejection fraction (EF) can be calculated by multiplying
WMI with 30 (Kober et al. 1997).
The predefined cutoff point of EF < 40% (corresponding WMI ≤ 1.3) was used in
studies III and V. In studies II and IV, the cutoff point was chosen as the value that
provided the best predictive accuracy in Cox univariate survival analysis.
52
4.2.3 Heart rate variability
4.2.3.1 Recording of heart rate variability (I–III, V)
A 24-hour ECG recording was carried out with an Oxford Medilog system (Oxford
Medilog 4500, Oxford Medical Ltd., England) between the days 5 and 14 after the
infarction in post-AMI patients. The sampling frequency of this system is 128 Hz,
corresponding to a timing accuracy of 8 ms. A 24-hour RR interval recording was carried
out simultaneously with the 24-hour ECG recording using a portable RR interval recorder
(Polar Electro Co Ltd., Kempele, Finland), which is a real-time microprocessor QRS
detector system. It uses a sampling frequency of 500 Hz and a subsequent interpolation to
2000 Hz, resulting in a final sampling frequency of 1000 Hz, which corresponds to a
timing accuracy of 1 ms (Ruha et al. 1997, Loimaala et al. 1999).
The data were saved in a computer for editing and further analysis of HRV by a
special software package (Hearts, Heart Signal Co., Kempele, Finland). All the RR
interval tachograms went through a detailed manual editing process (Huikuri et al. 1993,
Huikuri et al. 1994, Mäkikallio et al. 1999b) (I,II,III,V). For 24-hour analysis, recordings
with less than 18 hours of data or less than 85% of qualified sinus beats were excluded
(II, III, V).
In study I, a 4-hour segment with more than 95% of qualified beats was chosen for the
time domain analysis of HRV. For spectral and nonlinear parameters, three consecutive
samples of 4000 beats during this 4-hour period were analyzed. The simultaneous, timesynchronized tachograms were inspected, and noise and ectopic beats were edited
manually to eliminate the differences between the tachograms from the different sources.
4.2.3.2 Analysis of heart rate variability
The analysis was performed according to the recommendation of the Task Force (Task
Force of the European Society of Cardiology and the North American Society of Pacing
and Electrophysiology 1996). All the analyses were performed with Hearts software
(Hearts, Heart Signal co., Kempele, Finland).
In study I, the analyses were performed separately 1) in subjects with low and high
HRV (division by median in SDANN) and 2) in subjects with low and high heart rates
(division by median in average RR interval). To study the effect of sampling accuracy on
the comparison of the patient groups divided by median in SDANN, the comparison
between the low and high HRV groups was made separately with each sampling system.
The results acquired from the two systems were then compared to each other. (I)
Time- and frequency domain analyses. The standard deviation of consecutive RR
intervals (SDNN) from the entire recording period was chosen as a time domain index of
heart rate variability (HRV) (I, II, III, V) (Task Force of the European Society of
Cardiology and the North American Society of Pacing and Electrophysiology 1996). A
value < 70 ms was defined as abnormal (III, V) (Kleiger et al. 1987, Task Force of the
53
European Society of Cardiology and the North American Society of Pacing and
Electrophysiology 1996, La Rovere et al. 1998), or the cutoff point was chosen to be the
value which resulted in the best positive predictive accuracy in the Cox univariate
analysis (II). In study I, the standard deviation of the average RR intervals calculated over
5-minute segments (SDANN), as another marker of long-term HRV, and the square root
of the mean squared differences of successive RR intervals (RMSSD), a short-term HRV
marker in time domain analysis, were also determined. The mean RR interval was
calculated as a simple time domain parameter (II).
The power spectrum of HRV was measured using Fast Fourier transform (FFT)
analysis (I–III) and autoregressive analysis (AR) (I) in 4 frequency bands: < 0.0033 Hz
(ultra low frequency, ULF) (II, III), 0.0033–0.04 Hz (very low frequency, VLF (II, III)
(the boundaries for VLF were 0.005–0.04 in study I), 0.04–0.15 Hz (low frequency, LF)
and 0.15–0.40 Hz (high frequency, HF). Ultra low frequency and very low frequency
components were computed over the entire recording interval (II, III). LF and HF (and
VLF components in study I) components were computed from segments of 512 RR
intervals. All components were quantified in absolute values of power. The LF/HF ratio
was calculated as the ratio of LF power in ms2 to HF power in ms2. In study I, size 20
was used for the model order of autoregressive analysis, and a linear detrend was applied
to the RR interval data segments of 512 beats to make the data more stationary (I).
Natural logarithmic conversion was used to report the results of study II.
Geometric and non-linear analyses. To perform two-dimensional vector analysis, a
Poincaré plot was created (I, III). The following parameters of quantitative analysis were
assessed (Huikuri et al. 1996b): SD1, which shows the instantaneous beat-to-beat
variability of the data and SD2, a marker of continuous, long-term RR intervals; and the
ratio SD1/SD2.
For short- and intermediate-term scaling properties of HRV, the detrended fluctuation
analysis (DFA) technique was used (I, II). This method detects the presence or absence of
fractal-like scaling properties and it has been validated for time series data. The
correlation properties were measured both for short-term ( ≤ 11 beats, α1) and
intermediate-term ( > 11 beats, α2) fluctuations of RR interval data. (Peng et al. 1995,
Iyengar et al. 1996, Mäkikallio et al. 1997). The analysis was performed in 2 steps: in the
first phase, only the artifacts were edited out of the recording (analysis of “unedited
data”), the second analysis was performed on the data including only sinus beats (“edited
data”) (II). In the DFA technique, the integrated time series is first divided into sections.
On a log-log scale, the lengths of these sections are plotted against the average
fluctuation of the least squares lines about the locally best-fit line. The slope of the line
thus produced determines the scaling exponent α, which is an index of self-similarity of
the time series. For totally uncorrelated data, α = 0.5; if 0.5 < α ≤ 1, long-range powerlaw correlations exist; α = 1 corresponds to 1/f noise; and α = 1.5 indicates Brownian
noise (Peng et al. 1995, Hausdorff et al. 1995, Iyengar et al. 1996, Hausdorff et al. 1996).
For long-term fluctuations, the scaling exponent β was computed to assess the powerlaw relationship of HRV (II). The frequency range used for the analysis was 10−4 to 10−2
Hz (Bigger et al. 1996). The point power spectrum was logarithmically smoothed in the
frequency domain, and the power was integrated into bins spaced 0.0167 log (Hz) apart.
A robust line-fitting algorithm of log (power) on log (frequency) was then applied to the
power spectrum in the selected frequency range, and the slope of this line was calculated.
54
Approximate entropy (ApEn), the index of complexity of the RR interval data, was
calculated for each 4000-beat period in study I. Out of the series of RR intervals, a vector
time series was created. The length of the vector was preset by the variable m, which
determined the number of adjacent RR intervals determining the start and endpoints of
the vector. If m = 2, then the vector was formed between two adjacent RR interval values.
Conditional probability was calculated for each vector in the time series to determine the
percentage of instances in which vectors that come close (within a preset tolerance r) to
the template vector have their subsequent point within the same tolerance r of the
subsequent point following the template vector (Fleisher et al. 1993, Pincus &
Goldberger 1994). The conditional probabilities were aggregated into approximate
entropy. The fixed input variables were m = 2 and r = 20% of the standard deviation of
the data sets. These values are based on previous findings with good statistical validity
(Pincus 1991, Pincus & Goldberger 1994, Mäkikallio et al. 1996).
4.2.4 Baroreflex sensitivity (III, V)
Baroreflex sensitivity (BRS) was measured between days 5 and 21 using a phenylephrine
method. Blood pressure was measured continuously with a Finapres finger cuff, and a
100–200 µg bolus of phenylephrine was injected into a large cubital or forearm vein
(Eckberg et al. 1971, Airaksinen et al. 1998). In BRS analysis, RR intervals were plotted
as a function of systolic arterial pressure, and the linear correlation between the two was
calculated (Smyth et al. 1969, Eckberg et al. 1971, Vanoli & Adamson 1994). The
requirement of the phenylephrine-induced rise in systolic arterial pressure was ≥ 15
mmHg, with a correlation coefficient r > 0.7 and a p-value < 0.05. Three measurements
were performed on each patient, and the mean of the rate-pressure response was
calculated. A value < 3.0 ms/mmHg was defined as abnormal (La Rovere et al. 1998, La
Rovere et al. 2001).
4.2.5 ECG measurements (QRS duration, QT-interval and QT
dispersion) (III, V)
QRS duration was measured between days 2 and 20 on a standard 12-lead ECG at the
speed of 50 mm/ms, and a QRS duration of ≥ 120 ms was considered abnormal (V). QT
intervals were measured from one cycle (III) or two cycles (II, V) on the same printout.
Bazett’s formula was applied to correct the QT intervals (Bazett 1920). The tangent-slope
method was applied to each ECG printout manually (Perkiömäki et al. 1995). QT
dispersion was measured without a correction for heart rate (Sporton et al. 1997). A QT
dispersion of ≥ 90 ms was considered abnormal (III, V) (Bogun et al. 1996) or the cutoff
point which maximized the most significant hazard ratio in the Cox univariate analysis
(II).
55
4.2.6 Exercise test and T-wave alternans analysis (III)
A maximal bicycle exercise test with a starting stage of 25 W and increments of 25 W
every 3 minutes was performed 5 to 21 days after the AMI. TWA was measured with
special equipment using the spectral analysis method (CH2000, Cambridge Heart, Inc.,
Cambridge, Mass., USA). Special high-resolution electrodes, which record ECG signals
from multiple segments of each electrode, were used. The skin was carefully prepared
before the test, in order to reduce the impedance in each lead. ST changes, chest pain and
blood pressure during the test were recorded. TWA was classified as positive, if the
amplitude of TWA was ≥ 1.9 µV, the ratio of the alternans power to the standard deviation
of the noise was ≥ 3, and the duration of the alternans ≥ 1 minute with heart rate ≤ 110
bpm. The TWA test was negative, if the heart rate was ≥ 105 bpm for at least one minute
and there was no sustained alternans present at heart rates ≤ 110 bpm without
phenomenona which could obscure the presence of TWA. The recording was
indeterminate, if the presence or absence of TWA could not be confirmed, and
incomplete, if the heart rate remained < 105 bpm without the positivity criteria. (Table 2)
(Hohnloser et al. 1997).
Table 2. Definitions of TWA exercise test results (Hohnloser et al. 1997, Kavesh et al.
1998).
TWA exercise test result
Definition
Positive
Alternans amplitude ≥ 1.9 µV and
alternans ratio ≥ 3 and
duration ≥ 1 min. with heart rate ≤ 110 bpm.
No sustained alternans at heart rate ≤ 110 bpm and
heart rate ≥ 105 bpm for ≥ 1 min.
Heart rate ≥ 105 bpm for ≥ 1 min and
presence or absence of TWA cannot be confirmed.
Heart rate < 105 bpm and positivity criteria not met or
inability to perform the stress test.
Negative
Indeterminate
Incomplete result
4.2.7 Signal-averaged ECG (II, III, V)
The SAECG was recorded between days 5 and 14 using the LP Plus system (Fidelity
Medical Ltd., Haifa, Israel). The patient was supine during the recording, and ≥ 300 beats
were obtained and averaged in orthogonal Frank leads (X, Y, and Z). The presence of late
potentials was established if two of the following three criteria were met: the filtered
QRS duration (QRSd) was more than 114 ms, the root mean square voltage of the
terminal 40 ms of the filtered QRS complex was less than 20 µV or the duration of the
terminal portion was more than 38 ms (Breithardt et al. 1991). Furthermore, to find out
the predictive value of a QRS duration ≥ 114 ms, it was analyzed both alone and in
combination with other SAECG parameters (Malik et al. 1992, El Sherif et al. 1995).
56
4.2.8 Ambulatory ECG recording (III, V)
A 24-hour ECG recording was carried out with an Oxford Medilog system (Oxford
Medilog 4500, Oxford Medical Ltd., England) between days 5 and 14. Ventricular
tachyarrhythmias were recorded, and the number of ventricular premature beats (VPBs)
was counted. Nonsustained ventricular tachycardia was defined as at least 3 consecutive
beats with a wide QRS complex at the rate of 100/min or more (Buxton & Duc 2002)
(V), or at the rate of 150/min or more (III).
4.2.9 Natriuretic peptides (IV)
The blood sample for natriuretic peptides was taken between days 3 and 20 after the
infarction through an intravenous cannula into EDTA vacuum tubes. The samples were
placed on ice, centrifuged at 4ºC for 10 minutes, and the plasma was stored at –70ºC. NTpro ANP was assayed directly from unextracted plasma, and ANP and BNP were
extracted from the plasma using SepPak C18 cartridges before the radioimmunoassay.
(Vuolteenaho et al. 1985, Vuolteenaho et al. 1992).
4.2.10 Statistical methods
The data were analyzed using the SPSS software (SPSS 8.0–10.0.1, SPSS Inc., Chicago,
Illinois). In study I, the means of the difference of the results provided by each recording
system and a 95% confidence interval of the means were calculated. To estimate the error
between the two systems, the mean difference of the values was calculated and divided
by the mean of the results obtained from the system with the 1-ms timing accuracy. The
Mann-Whitney U-test was used to compare the results between groups with high and low
HRV.
Univariate comparisons of the baseline characteristics between the subjects who had
died and the survivors were performed with the chi-square test for categorical variables
(II–V) and with the two-sample t-test for continuous variables (II, III). For continuous
variables in studies IV and V, the baseline characteristics between the groups were
performed with one-way analysis of variance with Bonferroni post-hoc analysis, or
Tamhane’s test depending on the Levene statistic for homogeneity-of-variance.
The best cutoff point for each predictive variable was determined by searching for the
value that maximized the statistically most significant hazard ratio obtained from the Cox
regression analysis (II, IV). Optionally, a previously predefined cutoff point validated by
previous studies was used (III, V). In study IV, the lowest (or the highest) quartile in the
pathological range was also chosen as a cutoff point for the survival analyses, in order to
limit the size of the subgroup involved, to improve the predictive accuracy of the test
applied, and to facilitate the assessment of the performance of each risk predictor in
57
subgroups of comparable sizes. The sensitivity, specificity, positive, negative and overall
predictive value of each test parameter were calculated. In addition, receiver-operating
characteristic (ROC) curves showing sensitivity as a function of the complement of
specificity were generated to compare the predictive power of various prognostic
variables. (II, IV).
Relative risk (RR) and 95% confidence intervals (CI) were calculated for each
categorical variable as a predictor of all-cause and cardiac mortality in the Cox regression
model. To estimate the independent power of the variables in predicting mortality, age,
diagnosis of diabetes, and NYHA class were included in the Cox proportional hazards
regression analyses (IV-V). In study II, all the risk variables with a univariate association
with mortality were included in the model after adjusting for clinical variables and WMI.
In study III, the stratification was done first by age and sex, and then all the univariate
clinical predictors were included in the model. Kaplan-Meier estimates of the distribution
of times from baseline to the endpoints were computed, and log-rank analysis was
performed to compare the survival curves between the groups (II–V). Linear regression
analysis was performed to detect correlations between different parameters. A value of
p < 0.05 was considered significant (I–V).
5 Results
5.1 Effect of sampling frequency on the analysis of heart rate
variability (I)
No significant differences were seen in the measurement of the following time domain
HRV parameters: mean RR interval, SDNN, or SDANN, regardless of whether the heart
rate sampling was performed with a sampling frequency of 125 Hz (8-ms timing
accuracy) or 1000 Hz (1-ms timing accuracy). A 4.0% difference was seen in the analysis
of RMSSD. In subjects with low HRV, the difference was proportionately more
pronounced (7.1%) than in subjects with high HRV (3.1%).
In spectral analysis, there were no significant differences in the VLF or LF power
results. The HF power was significantly lower when measured with a 1-ms timing
accuracy compared to the conventional system (error 4.0%). The difference was more
notable in the group with low HRV (8.0%) than in the high HRV group (2.8%). The
LF/HF ratio calculated from the data acquired by using a superior timing accuracy was
clearly higher than the ratio derived from the data acquired by using the conventional
system (error 13.9%). The lower the HRV, the larger the proportionate difference between
the results in the LF/HF ratio; the error was up to 22.6% in subjects with low HRV. The
heart rate had a similar effect: in subjects with a high heart rate, the error was 17.5% but
in subjects with low heart rates, it was 5.4%. The method of analysis (AR or FFT) did not
affect these results in any significant way.
In the Poincaré analysis, a significant difference was seen in the values of SD1, a
measure of short-term HRV. The values acquired by using a high sampling frequency
were significantly lower (error 4.8%). The difference was greater in subjects with low
HRV. No prominent differences were detected in the values of SD2. As a result, the ratio
of SD1 to SD2 was slightly but significantly lower when measured with a 1-ms timing
accuracy, in patients with high HRV, this difference was not significant. When the HRV
was lower, or when the mean heart rate was higher, the difference was more considerable.
The evaluation of new dynamic parameters revealed marked differences depending on
the sampling frequency of the system used. The measurement of ApEn with a 1-ms
59
timing accuracy provided significantly lower values than the measurement with an 8-ms
timing accuracy (error 11.1%). Again, the error was proportionately more pronounced
when the HRV was low (error 17.6%), and lower but still significant, when the HRV was
high (3.8%). On the other hand, the error was stronger in subjects with high heart rates
(15.0%) than in those with lower heart rates (7.9%).
In DFA analysis, significant differences were recorded in the short-term scaling
exponent value (α1). The values obtained with a 1-ms timing accuracy were higher (1.30
vs. 1.25; difference 3.1%). In two groups divided according to HRV, the differences
persisted and were greater in the group with low HRV (1.20 vs. 1.14; difference 5.0%)
than with high HRV (1.39 vs. 1.37; error 1.4%). The differences were comparable when
the study population was split according to heart rate. There were no significant
differences in the values of the long-term scaling exponent (α2).
When the performance of the systems in studying the differences between subject
groups with low and high HRV were compared to each other, no discrepancy was seen in
the results derived from time or frequency domain analysis. The change in SD1/SD2 ratio
was estimated to be more prominent between the groups when the conventional method
was used. In the values of α1 and α2, there was a small difference between the results. The
system with the 8-ms timing accuracy detected a clear false difference in ApEn levels
when compared to the results acquired by using the 1-ms system.
5.2 Medication after acute myocardial infarction
The optimization of beta-blocking therapy was successful: in studies III–V, consisting of
post-AMI patients treated at Oulu University Hospital, the percentage of patients taking
beta-blocking agents was high in all substudies (97%). In study II, which also included
patients from two Danish hospitals, the total number of patients receiving beta-blockers
was smaller (84%). In addition to beta-blockers, a minority of patients was prescribed
amiodarone (2%), either alone or in combination with beta-blocking therapy. ACE
inhibitors, or ATII receptor agonists when the patient did not tolerate ACE inhibitors,
were given to 46% of patients.
5.3 Mortality after acute myocardial infarction
During a mean follow-up of 43 ± 15 months (range 30–60 months), 101 patients died
(15.0%) out of 675 patients who were discharged alive and included in the long-term
follow-up sample (V). The number of cardiac deaths was 59 (8.7%), and 42 (6.2%) were
non-cardiac deaths. Among the cardiac deaths, 37 (5.5%) were non-sudden, and 22
(3.3%, or 37% of cardiac deaths) were classified as SCDs (Figure 1). Arrhythmia events
occurred in 17 patients (2.5%). In study II, the mortality was 7.0% (49/697) during the
follow-up of 18 ± 7 months. In study III, the mortality was 6.9% (26/378) during a
follow-up time of 14 ± 8 months, and, in study IV, at 43 ± 13 months, total mortality was
60
11.5% (60/521), cardiac mortality 6.3% (33/521) and sudden cardiac mortality 3.1%
(16/521).
Fig. 1. Mortality during follow-up time (V).
Figure 2 demonstrates the cumulative event rates for total mortality (Fig. 2a), and nonSCDs and SCDs (Fig. 2b) during the follow-up (V). The majority of non-sudden cardiac
deaths (68%) occurred during the first 18 months. The incidence and temporal
distribution of SCDs (14%) was significantly lower during the early time period
(p < 0.001). Similarly, only 3 out of the 17 arrhythmia events took place during the first
18 months after the AMI.
Fig. 2. a) Total mortality during follow-up time (V). b) The incidence of non-sudden and
sudden cardiac deaths during follow-up time (V).
61
5.4 Clinical parameters
Clinical data of survivors and the deceased patients according to the mode of death are
shown in Table 3. Several conventional clinical risk markers were associated with an
unfavorable prognosis during follow-up (II, III). For example, advanced age, history of
diabetes or previous heart failure, non-Q-wave or anterior infarction, lack of thrombolysis
or primary PTCA at presentation, and poor NYHA functional class (II, IV) were
univariate predictors of both all-cause and cardiac mortality (III). A high proportion of the
patients dying of a cardiac cause — either sudden or non-sudden — had diabetes. The
only significant difference between the patients who died non-suddenly or suddenly due
to a cardiac cause was in the New York Heart Association (NYHA) functional class, with
Class I being more common among those who died suddenly (55%) than among those
who experienced a non-SCD (24%) (p < 0.05). Patients who had an arrhythmia event
were younger and also more commonly in NYHA class I (76%) (p < 0.001 compared to
non-SCD).
5.5 Heart rate variability as a risk predictor (II, III, V)
HRV analysis was possible in 87%–93% of the patients (II, III, V). The most common
reason for a missing heart rate variability result was a technically unsuccessful recording
(with artifacts, ectopy or episodes of atrial fibrillation) (III, V).
5.5.1 Time and frequency domain parameters (II, III, V)
Concurrent with previous studies, most of the conventional HRV parameters used were
able to predict mortality in the univariate analysis, but after adjustment for the clinical
variables and left ventricular function, their predictive value was lower than that reported
in previous studies (II, V). The prevalence of nondiagnostic HRV results was
significantly greater in patients who died during the follow-up than the survivors (III). In
addition, SDNN was significantly lower in patients who died when compared to
survivors (III), and it was also lower in patients who experienced non-sudden cardiac
death (V), but there was no statistically significant difference between survivors and
either SCD victims or patients experiencing an arrhythmia event (V). Low SDNN failed
to predict all-cause or cardiac mortality with the cutoff point of 70 ms (III), as well as the
risk of suffering from an SCD (V). In study II, where the cutoff point was optimized, an
SDNN < 65 ms and a mean RR interval < 815 ms were associated with increased allcause mortality in the univariate analysis but not in the multivariate analysis.
The spectral parameters ULF, VLF, LF and LF/HF ratio were lower in patients who
died during the follow-up period when compared to the survivors (II). No difference was
seen in the HF component between these two patient groups. All the spectral components
62
with the exception of HF were predictors of all-cause mortality in both uni- and
multivariate Cox analysis when the best-defined cutoff points were used (II). The LF/HF
ratio < 1.45 was the strongest predictor of subsequent mortality of the spectral HRV
parameters studied (II).
Table 3. Differences in the clinical parameters grouped by cardiac mortality or
arrhythmic events (V)
Age (years)
Sex (f/m)
Smokers
Prior MI
Prior CHF
Diabetes
Prior CABG
Anterior MI
Q-wave MI
Thrombolysis
PTCA or CABG
before discharge
Atrial fibrillation
at discharge
CKMB maximum (U/l)
Killip class 3 or 4
NYHA-class 2 or 3
at discharge
Medication at discharge:
Aspirin or warfarin
Beta-blockers
Statins
ACE inhibitors
or ATII blockers
Ca-channel blockers
Digoxin
Diuretic
Alive
(n = 574)
Non-sudden
cardiac death
(n = 37)
Sudden cardiac
death (n = 22)
Arrhythmia event
(n = 17)
61 ± 10
437 / 137
182 (32%)
107 (19%)
48 (8%)
116 (20%)
27 (5%)
250 (44%)
300 (51%)
285 (50%)
71 ± 7***
26 / 11
4 (11%)*
17 (46%)***
17 (46%)***
17 (46%)***
7 (19%)**
25 (68%)**
9 (24%)**
6 (16%)***
67 ± 8**
13 / 9*
8 (36%)*
6 (27%)
7 (32%)***
13 (59%)***
4 (18%)**
16 (72%)**
10 (45%)*
5 (23%)**
63 ± 8††
13 / 4
8 (47%)††
4 (24%)
5 (29%)*
8 (47%)***
4 (24%)
12 (70%)*
10 (59%)
6 (35%)
145 (25%)
12 (2%)
4 (11%)
5 (14%)***
1 (5%)
1 (5%)
1 (6%)
3 (18%)**
174 ± 188
47 (8%)
112 ± 127
16 (43%)***
177 ± 195
6 (27%)***
235 ± 210
4 (25%)
149 (26%)
28 (77%)***
10 (45%)**†
4 (25%)†††
549 (96%)
555 (97%)
221 (39%)
210 (37%)
33 (89%)
35 (95%)
4 (11%)
22 (59%)*
19 (100%)
22 (100%)
7 (32%)
13 (59%)*
17 (100%)
16 (94%)
7 (41%)
11 (65%)*
40 (7%)
17 (3%)
102 (18%)
7 (19%)
15 (41%)***
29 (78%)***
1 (5%)
1 (5%)†
13 (59%)***
2 (12%)
2 (12%)
8 (47%)†
*p < 0.05, **p < 0.01 and ***p < 0.001 compared to survivors. †p < 0.05, ††p < 0.01 and †††p < 0.001 compared
to patients who experienced a non-sudden cardiac death during the follow-up.
63
5.5.2 Dynamic parameters (II)
The reduced short-term scaling exponent α1 was the most powerful univariate and
multivariate predictor of all HRV parameters associated with increased all-cause
mortality. Its predictive value was higher when measured from unedited HRV data than
from fully edited data. Among the different HRV measures, a reduced α1 ( < 0.65) had the
highest positive predictive accuracy for all-cause mortality, 23%. Reduced values of α1
significantly predicted mortality in groups with either preserved or reduced left
ventricular function (WMI ≥ 1.5, or < 1.5, respectively). (Figure 3). The power-law
scaling exponent β (slope) was clearly more negative in patients who died during the
follow-up than in the survivors. A reduced β < −1.55, or a steep 1/f power-law slope,
predicted all-cause mortality in both uni- and multivariate Cox analysis. The KaplanMeier survival curves for various HRV parameters, both traditional and dynamic, are seen
in Figure 4.
Fig. 3. Kaplan-Meier survival curves for the reduced short-term scaling exponent α1 in
patients with preserved or reduced left ventricular function.
64
Fig. 4. Kaplan-Meier survival curves for various HRV parameters, all-cause mortality.
5.6 T-wave alternans as a risk predictor (III)
Of the 379 consecutive patients enrolled in study III, a clinical exercise test was
performed on 323 (85%) patients at 8.1 ± 2.4 days after the AMI. Fifty-six patients were
unable to do the exercise test, and the reasons were poor overall health (n = 21; 38%),
problems with the locomotor system (n = 9; 16%), recent or past disabling stroke (n = 5;
9%); obliterating arteriosclerosis of the limbs (n = 4; 7%); pulmonary disease (n = 4;
7%); heart failure at the time of the planned exercise test (n = 3; 5%), and various other
medical causes in the remaining 10 patients. Out of the remaining 323 patients, TWA
analysis could be reliably performed on 200 patients (62%). The number of positive
tracings was 56/323 (17%), while 144 tracings were negative (45%). The test was
indeterminate in 46 cases (14%). The reasons for the indeterminate tests (i.e. complete
but not classifiable as positive or negative) were mainly noise (27 cases), “bad beats”
(mainly ectopy, 9 cases) or the presence of nonsustained T-wave alternans (21 cases). In
as many as 133 patients, the test was incomplete (35%), i.e. the patient was not able to
reach the required heart rate of 105 bpm for one minute or more (n = 77) or to perform
the exercise test (n = 56).
65
None of the patients with a positive TWA result died during follow-up. On the other
hand, however, only one patient with a negative TWA result died, confirming the high
negative predictive value of the TWA test. The prevalence of incomplete TWA test
results, classified as a combination of inability to perform an exercise test, or to reach the
heart rate of 105 for one minute or more, was clearly higher for the patients who died
during the follow-up. The Kaplan-Meier survival curves are shown in Figure 5. Of all the
risk predicting parameters tested in study III, an incomplete TWA test result was the most
powerful univariate and multivariate predictor of all-cause and cardiac mortality. Both the
inability to perform the exercise test and the inability to reach the target heart rate of 105
were related to high risk of dying. Mortality was very low (0.5%) in the patient groups in
whom the TWA analysis could be reliably performed: only 1 of the 200 patients died
during follow-up.
Fig. 5. Kaplan-Meier survival curves for all-cause mortality, stratified by the result of TWA
test.
66
5.7 Natriuretic peptides as risk predictors (IV)
Levels of all natriuretic peptides were higher in patients who suffered a cardiac death
during the follow-up than in the survivors. The patients who died suddenly also had
significantly higher concentrations of N-ANP and BNP than the survivors. BNP was not
clearly higher in patients who experienced a non-sudden cardiac death, in comparison to
survivors. However, the levels of N-ANP and BNP were also increased in patients with
non-cardiac deaths.
High levels of ANP, N-terminal pro-ANP and BNP predicted both cardiac and sudden
arrhythmic mortality during the follow-up period. After adjusting for clinical variables,
high levels of vasoactive peptides remained independent predictors of cardiac mortality.
The levels of all the measured peptides had an inverse significant correlation (p < 0.001)
to the left ventricular systolic function, but they still provided independent prognostic
information after adding WMI into the Cox regression model. For BNP, the RR for
cardiac death was 2.97 (95% CI 1.29–6.83, p = 0.010) and that of SCD 3.86 (95% CI
1.22–12.23, p = 0.022). The predictive value of natriuretic peptides for both cardiac and
arrhythmic mortality was likewise preserved if creatinine concentration or WMI was
added to the Cox multivariate analysis. Non-sudden cardiac death was predicted by
elevated ANP and N-ANP at both cutoff point levels. Elevated BNP was not a significant
predictor of non-sudden cardiac death. Elevated BNP predicted the occurrence of SCD,
whereas elevated ANP and N-ANP were not as powerful predictors of sudden than nonsudden cardiac death. The Kaplan-Meier survival curve for BNP at the best cutoff point is
shown in Figure 6. The difference between the patient groups is seen throughout the
follow-up time.
Fig. 6. Kaplan-Meier survival curves for BNP, cardiac mortality (left) and sudden cardiac
mortality (right).
67
5.8 Other risk predictors
5.8.1 Left ventricular function (II–V)
The measurement of left ventricular function by WMI analysis was possible in 98.5% of
the patients. Left ventricular function was clearly and significantly reduced in patients
who died during the follow-up, and with a cutoff value of 1.3 (EF~40%), reduced systolic
function was independently associated with increased all-cause mortality, but not with
cardiac mortality, in study III. In study II, a WMI < 1.5 (EF~45%) was an independent
predictor of all-cause mortality. Reduced left ventricular systolic function was seen more
often in patients who experienced cardiac death, and it predicted similarly both sudden
and non-sudden cardiac death (IV, V). Not surprisingly, the optimized cutoff point of
WMI at 0.9 (or EF < 30%) was somewhat stronger predictor of both sudden and nonsudden death than the cutoff point at the lowest quartile (WMI ≤ 1.3 or EF < 40%) (IV,
Figure 5). In patients who died of non-cardiac causes, the WMI did not differ from that of
the survivors. A lower WMI was also a predictor of an arrhythmic event.
5.8.2 Baroreflex sensitivity (II, V)
BRS could be analyzed in up to 80% of patients (II, V). BRS was lower in patients who
died during the follow up or who experienced a non-sudden cardiac death. The numbers
did not differ, if the death was sudden or the patient had an arrhythmic event. Impaired
BRS with the cutoff point < 3.0 ms/mmHg did not predict cardiac or non-arrhythmic
death (V) nor all-cause mortality (II). The number of nondiagnostic BRS tests was clearly
higher in the patients who died than in the survivors. A nondiagnostic result or the
presence of a contra-indication to the test predicted both all-cause and cardiac mortality
in Cox univariate analysis (II).
5.8.3 QTc interval (III) and QT dispersion (III, V)
QT interval and dispersion could be measured in up to 96% of patients. Both the
corrected QT interval and QT dispersion were significantly longer in patients who died
by the end of follow-up than in those who were alive (III). A long QTc interval with a
cutoff point of 440 ms predicted all-cause mortality in univariate analysis, but not in
multivariate analysis, and it did not predict cardiac mortality during the follow-up time
(III). QT dispersion was insignificantly wider in patients who experienced a sudden
cardiac death or an arrhythmia event; the difference was significant in patients who
experienced a non-sudden cardiac death (V). On the other hand, increased QT
dispersion > 90 ms was an independent marker of both overall and cardiac mortality (III).
68
The number of nondiagnostic QT measurements was proportionately higher in patients
who died during the follow-up time, but this difference was not large enough to be of
statistical significance.
5.8.4 Signal-averaged ECG (III, V)
SAECG analysis could be reliably performed in up to 90% of the patients. The
percentage of subjects with positive SAECG did not differ between the survivors or the
patient group who died during follow-up (III). However, the duration of the QRS
complex (QRSd) analyzed during the SAECG measurement was significantly lower in
the survivors than in the deceased. Similarly, the percentage of patients in whom the
QRSd was in the suggested pathological range ( > 114 ms) was clearly higher in the
patients who died, and the patients belonging to this group had an increased risk of both
all-cause and cardiac death in the Cox multivariate analysis (III). The QRS duration was
longer in patients who experienced non-sudden cardiac death, and, conversely, the
duration of the late potential was longer in patients who experienced SCD when
compared to survivors (V). Abnormal SAECG predicted SCD in the multivariate
analysis, and it was also more often abnormal also in patients experiencing arrhythmic
events, but this difference was not significant in the multivariate analysis. (V)
5.8.5 Ambulatory ECG (III, V)
In study III, the patients who died during the follow-up, showed more often ventricular
ectopy in their 24-hour ECG or episodes of nonsustained ventricular tachycardia than the
survivors, but this difference was insignificant. No episodes of sustained ventricular
tachycardia were detected in any of the patients during the recording. In study V, where
the mode of death was studied in detail, increased ectopy was significantly more common
in patients experiencing non-sudden cardiac death than in the survivors. The presence of
nonsustained VT was significantly more common in patients with non-sudden or sudden
cardiac deaths, and in patients with arrhythmia events than in patients without events. The
presence of non-sustained VT was able to predict both SCD and an arrhythmia event, but
increased ectopy was not.
5.8.5 Chronotropic insufficiency (III)
During the exercise test, an inability to reach the target heart rate of 105 beats/min
required for TWA analysis was a strong independent predictor of both all-cause and
cardiac death.
69
5.9 Univariate predictors of all-cause, cardiac and sudden cardiac
mortality (II–V)
The mean values for arrhythmia risk variables are shown in Table 4 (II–V). Most risk
variables differed between the survivors and the patients who died during the follow-up,
except for baroreflex sensitivity and abnormal SAECG. All arrhythmia risk variables
tested in study V differed between the survivors and those who died of a non-sudden
cardiac death. In most risk variables tested, the patients experiencing an SCD did not
differ from those who remained alive, but an nsVT was more common in their Holter
recordings and the left ventricular systolic function was worse than in survivors. A similar
finding was seen in patients experiencing an arrhythmic event (V). Notably, there were
no significant differences in the arrhythmia risk variables between the patients who died
non-suddenly or suddenly (Table 4).
70
Table 4. Differences in the risk predictors grouped by mortality or arrhythmic events (II–
V). The populations in the studies are of different sizes, and the follow-up times vary.
Alive
Ejection fraction
46 ± 9
24-hour ECG
nsVT
24 (4%)
> 10 VPBs/h
72 (13%)
HRV parameters
SDNN (ms)
98 ± 32
Mean RR interval (II)
901 ± 144
ULF (ln) (ms2)
8.6 ± 0.7
VLF (ln) (ms2)
6.5 ± 0.9
LF (ln) (ms2)
5.5 ± 1.2
LF/HF ratio
2.7 ± 2.1
α1
1.02 ± 0.30
α1 (edited)
1.26 ± 0.23
β < -1.55
−1.27 ± 0.56
BRS (ms/ mmHg)
9.3 ± 8.1
ECG markers
QRS-duration (ms)
89 ± 16
QTc maximum
453 ± 54
QT-dispersion (ms)
74 ± 39
SAECG
QRS-duration (ms)
98 ± 18
Rms last 40 ms
47 ± 33
Duration < 40 µV/ms
29 ± 10
TWA analysis (III)
Positive TWA
16%
Negative TWA
41%
Indeterminate TWA
13%
Incomplete TWA test
31%
Natriuretic peptides (IV)
ANP (pmol/l)
55 ± 84
N-ANP (pmol/l)
594 ± 360
BNP (pmol/l)
26 ± 28
Non-sudden
cardiac
death
Sudden
cardiac
death
Arrhythmia
event
41 ± 10***
37 ± 11***
41 ± 11*
39 ± 12**
9 (13%)**
25 (36%)***
5 (14%)*
12 (32%)**
4 (18%)*
5 (23%)
4 (24%)*
3 (18%)
81 ± 25***
859 ± 123*
8.4 ± 0.7*
5.8 ± 1.0**
4.6 ± 1.2***
1.9 ± 2.1*
0.76 ± 0.33***
1.10 ± 0.32***
−1.44 ± 0.24***
6.6 ± 8.5
74 ± 21***
5.3 ± 6.0*
82 ± 29
8.2 ± 12.3
93 ± 42
8.7 ± 13.8
95 ± 19**
491 ± 57***
95 ± 47*
103 ± 22***
93 ± 19
95 ± 22
91 ± 33**
80 ± 48
87 ± 48
104 ± 26
52 ± 39
30 ± 17
116 ± 25***
44 ± 33
29 ± 15
103 ± 22
49 ± 44
36 ± 20**
102 ± 23
43 ± 33
33 ± 14
0%*
4%***
4%
92%***
-
-
-
93 ± 99**
969 ± 582***
55 ± 68***
145 ± 141***
1041 ± 430**
47 ± 34
83 ± 72
893 ± 669*
54 ± 76*
-
All-cause death
-
-
-
*p < 0.05, **p < 0.01 and ***p < 0.001 compared to survivors. - not analyzed.
The hazard ratios in the univariate analysis of each categorized arrhythmia risk variable
with pre-defined or optimized cutoff values, are shown in Table 5 (II–V). By univariate
analysis, SCD and arrhythmia events were predicted by a reduced EF, an episode of nsVT
and an abnormal SAECG. After including clinical variables as covariates, the same
variables still predicted the SCD. The sensitivity, specificity and predictive accuracy of a
71
reduced EF, an episode of nsVT and an abnormal SAECG as predictors of SCD and nonSCD are shown in Table 6 (V).
The prevalence of incomplete TWA test results as well as non-diagnostic BRS and
HRV results was higher among the patients who died (III). The specificity, sensitivity,
and positive and negative predictive accuracies of various risk predictors in predicting
sudden cardiac mortality are listed in Table 6. The positive predictive accuracy of all
these variables was lower in predicting SCD (between 5 and 11%) than non-SCD
(between 12 and 18%). Receiver-operating characteristic curves of various risk predictors
are seen in Figure 7.
Fig. 7. HRV parameters as predictors of all-cause mortality (II); right panel: BNP and left
ventricular systolic function as predictors of sudden cardiac mortality (IV).
72
Table 5. Predictors of all-cause, cardiac and sudden cardiac mortality in univariate
analysis. The populations in the studies are of different sizes, and the follow-up times vary
(II–V).
Clinical parameters
Age (V)
Age > 70 years (V)
Diabetes (V)
CHF (V)
Previous myocardial infarction (V)
Anterior infarction (V)
Non-Q-wave infarction (V)
No thrombolysis/PTCA on arrival
(V)
NYHA III–IV at discharge (V)
Test result
Incomplete TWA test (III)
heart rate < 105
no exercise test
BRS < 3.0 ms/mmHg (V)
Nondiagnostic result
EF < 40% (III, V) or EF < 45% (II)
nsVT (V)
VBPs > 10/h (V)
SAECG positive (V)
QRSd > 114 ms (III)
QRS duration ≥ 120 ms
QTc maximum > 440 ms (III)
QT dispersion > 90 ms (V)
Mean RR interval < 815 ms (II)
SDNN < 65 (II) or SDNN < 70 (V)
ULF (ln) < 8.45 (II)
VLF (ln) < 5.30 (II)
LF (ln) < 3.85 (II)
LF/HF ratio < 1.45 (II)
α 1 < 0.65 (II)
α 1 (edited) < 1.00 (II)
β < −1.55 (II)
ANP > 37.6 pmol/l (IV)
N-ANP > 973 pmol/l (IV)
BNP > 23.0 pmol/l (IV)
All-cause mortality
Cardiac mortality
Sudden cardiac
mortality
1.1 (1.1–1.2)***
5.0 (3.1–7.9)***
2.8 (1.8–4.4)***
4.6 (2.9–7.4)***
1.9 (1.2–3.1)**
1.7 (1.1–2.6)*
2.7 (1.7–4.3)***
4.3 (2.4–7.9)***
1.1 (1.1–1.2)***
5.6 (3.2–9.5)***
3.7 (2.2–6.1)***
5.9 (3.8–9.8)***
2.5 (1.5–4.2)**
2.5 (1.4–4.2)**
2.2 (1.3–3.7)**
4.4 (2.2–8.7)***
1.1 (1.0–1.1)*
3.0 (1.3–6.9)*
5.3 (2.3–12.5)***
4.3 (1.7–10.5)**
1.5 (0.6–3.7) ns
2.3 (1.0–5.5) ns
1.4 (0.6–3.3) ns
3.3 (1.2–8.8)*
4.2 (2.6–6.8)***
4.3 (2.5–7.3)***
4.3 (1.8–10.2)***
24.7 (5.8–104.7)***
15.2 (3.5–66.4)***
-
10.1 (2.0–50.2)**
13.3 (5.8–103.8)***
1.7 (0.8–3.5) ns
4.5 (2.1–9.8)***
3.7 (2.0–6.8)*** (II)
2.5 (1.3–5.1)**
3.0 (1.8–4.9)***
2.6 (1.4–5.1)**
2.7(2.3–14.0)***
2.4 (1.4–4.3)**
3.1 (1.1–8.4)*
5.6 (2.2–14.5)***
2.1 (1.2–3.8)**
2.4 (1.3–4.3)** (II)
2.4 (1.3–4.2)***
3.7 (2.0–6.9)***
3.5 (1.9–6.9)***
3.8 (2.1–6.7)***
5.1 (2.9–8.9)***
4.1 (2.3–7.2)***
3.1 (1.7–5.7)***
3.4 (1.9–5.9)***
4.4 (2.6–7.3)***
2.9 (1.7–4.9)***
8.4 (1.6–43.1)*
8.4 (3.2–22.2)***
1.1 (0.5–2.8) ns
4.0 (1.6–10.2)**
2.5 (1.3–4.7)** (II)
3.4 (1.7–7.0)**
2.5 (1.4–4.4)**
5.3 (2.9–9.7)***
5.7 (2.0–16.1)**
ns
3.9 (1.4–10.7)*
5.2 (2.3–12.1)***
4.7 (2.4–9.4)***
4.7 (2.1–10.4)***
1.2 (0.3–4.2) ns
-
2.7 (1.2–6.2)* (V)
4.2 (1.4–12.5)*
1.7 (0.6–4.7) ns
5.4 (2.1–14.3)**
2.4 (0.8–7.0) ns
1.0 (0.4–2.8) ns
1.8 (0.6–4.7) ns (V)
4.1 (1.3–12.8)*
3.4 (1.2–9.3)*
4.4 (1.4–13.8)*
*p < 0.05, **p < 0.01 and ***p < 0.001 compared to survivors, ns = non-significant, - not analyzed.
73
Table 6. Specificity, sensitivity, and negative and positive predictive accuracies of various
risk predicting parameters, sudden cardiac death (IV, V).
Parameter
ANP ≥ 37.6 pmol/l (IV)
N-ANP ≥ 973 pmol/l (IV)
BNP ≥ 23.0 pmol/l (IV)
EF < 40% (V)
NsVT (V)
SAECG abnormal (V)
Specificity
Sensitivity
Positive
predictive
value
Negative
predictive value
64
88
64
45
19
32
75
38
73
78
96
94
7
10
6
6
11
12
99
98
99
88
86
88
5.10 Multivariate predictors of all-cause, cardiac and sudden cardiac
mortality
After adjusting for clinical variables, functional impairment and an inability to reach the
target heart rate of 105 beats/min during exercise, or an incomplete TWA test, were
powerful predictors of all-cause and cardiac death in the multivariate analyses among the
several known risk variables assessed in the consecutive series of patients with AMI (III).
Increased QT dispersion, QRSd measured with SAECG and nondiagnostic BRS tests
remained independent predictors of both overall and cardiac death after adjusting for
clinical variables (III). Other risk indicators, such as EF, SDNN and QT interval, did not
provide much additional predictive information in this substudy (III). Most of the spectral
and dynamic HRV parameters predicted all-cause mortality in the multivariate analysis,
the strongest predictor being a reduced α1 below 0.65. LF/HF ratio was the most powerful
risk predictor in the spectral HRV analysis (II). All measured natriuretic peptides
predicted all-cause, cardiac and sudden cardiac mortality, after stratification for clinical
parameters. (IV). A reduced EF, the presence of non-sustained ventricular tachycardia and
an abnormal SAECG were associated with increased risk of sudden cardiac mortality in
the multivariate analysis (V).
5.11 Combination of risk predictors
The predictive power of the short-term scaling exponent α1 (II) and BNP (IV) were
analyzed in combination with WMI. Reduced values of α1 significantly predicted allcause mortality in groups both with preserved and reduced left ventricular systolic
function. The combination of low α1 and reduced WMI is associated with clearly
increased all-cause mortality, in contrast to the cases with preserved values of both
parameters, in whom the mortality during the follow-up was very low, and thus the
74
negative predictive accuracy very good (II) (Figure 3). In study IV, similarly, the
combination of BNP with WMI improved the predictive power in terms of both cardiac
and sudden cardiac death (Figure 8).
Table 7. Predictors of all-cause, cardiac and sudden cardiac mortality in the multivariate
analysis.
Risk predictor
EF < 40%
NsVT
Abnormal SAECG
QRSd > 114 ms (III)
Incomplete TWA test (III)
heart rate < 105
no exercise test
Nondiagnostic BRS result (III)
QTc maximum > 440 ms (III)
QT dispersion > 90 ms (III)
ULF (ln) < 8.45 ms2 (II)
VLF (ln) < 5.30 ms2 (II)
LF (ln) < 3.85 ms2 (II)
LF/HF ratio < 1.45 (II)
α 1 < 0.65 (II)
α 1 (edited) < 1.00 (II)
β < −1.55 (II)
ANP ≥ 37.6 pmol/l (IV)
N-ANP ≥ 973 pmol/l (IV)
BNP ≥ 23.0 pmol/l (IV)
All-cause
mortality
Cardiac mortality
Sudden cardiac
mortality
–
–
–
5.7 (2.3–14.0)***
24.7 (5.8–104.7)***
10.1 (2.0–50.2)**
13.3 (5.8–103.8)***
4.5 (2.1–9.8)***
3.1 (1.1–8.4)*
5.6 (2.2–14.5)***
2.1 (1.1–4.0)*
2.5 (1.2–5.1)*
3.5 (1.9–6.9)*
3.5 (1.8–6.9)***
3.9 (2.0–7.5)***
2. 9 (1.5–5.6)**
2.2 (1.1–4.4)*
2.3 (1.3–4.0)**
2.6 (1.5–4.5)**
1.8 (1.0–3.2)*
–
–
–
5.7 (2.0–16.1)***
15.2 (3.5–66.4)***
8.4 (1.6–43.1)*
8.4 (3.2–22.2)***
4.0 (1.6–10.2)**
ns
3.9 (1.4–10.7)**
–
–
–
–
–
–
–
3.5 (1.5–8.3)**
3.5 (1.7–7.0)**
3.6 (1.6–8.1)**
2.1 (1.0–5.0)*
2.9 (1.2–12.0)*
4.5 (1.7–12.0)*
–
–
–
–
–
–
–
–
–
–
–
–
–
–
3.7 (1.2–11.4)*
3.0 (1.1–8.3)*
3.9 (1.2–12.3)*
*p < 0.05, **p < 0.01 and ***p < 0.001 compared to survivors, – not analyzed.
75
Fig. 8. Kaplan-Meier curves for the combination of left ventricular systolic function (WMI)
and BNP; cardiac and sudden cardiac mortality. *BNP < 23 pmol/l and EF < 40%, or
BNP ≥ 23 pmol/l and EF ≥ 40%.
6 Discussion
The main findings of the study were as follows: arrhythmic mortality was low and its
proportion and temporal distribution were different from those previously reported in this
post-AMI patient population with optimized beta-blocking treatment. Previously
accepted powerful markers of mortality lose some of their predictive power in this
setting, partly because of the low number of events during follow-up. This was seen in
markers of decreased HRV and BRS, but also with novel parameters such as TWA. The
predictive power of fractal-like dynamic HRV parameters surpasses that of conventional
HRV analysis. The increased concentrations of natriuretic peptides, and especially that of
BNP, are promising in both arrhythmic and non-arrhythmic risk stratification after AMI.
In this study, most risk predictors studied were better markers of non-arrhythmic cardiac
mortality than SCD, probably because they closely reflect the extent of myocardial
damage after the AMI.
6.1 Effect of sampling frequency on heart rate variability analysis (I)
It has been suggested that the R wave measurement error using conventional systems can
make spectral HRV analysis inaccurate (Hilton et al. 1997) and that too low a sampling
rate may result in an error affecting the higher-frequency components (Merri et al. 1990,
Bianchi et al. 1993). In the present study, the sampling frequency of the recording system
did not significantly affect long-term or low frequency components of HRV. Obviously,
sampling frequency is more important in the accurate detection of beat-to-beat
fluctuations in RR intervals that are smaller in amplitude. Most prognostic studies on
post-AMI patient populations, however, are based on SDNN estimation, and hence, it is
unlikely that the results of these studies would have been changed if the sampling
frequency had been higher. Conventional Holter systems have an effect on parameters
estimating short-term, high frequency variation (RMSSD, NN50, pNN50, and spectral
HF component). This was confirmed in the present study, and this error also has a
significant effect on the LF/HF ratio. In addition, marked errors were observed in the
analysis of newer dynamic measures of HRV, especially in approximate entropy (ApEN).
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The mean error detected in a previous study was about 2% in the different time- and
frequency domain parameters, along with nonlinear symbolic RR interval dynamics,
when the sampling frequency of 128 Hz was compared with 2000 Hz (Voss et al. 1996a,
Voss et al. 1996c). Errors of up to 8% in RMSSD and HF power were observed in the
present study. It should be pointed out, however, that errors < 10% may not constitute a
significant difference in the traditional analysis from the point of view of this type of
experimental technique. The differences were much larger in ApEN in our study, but the
results of other dynamic parameters in the high HRV group resembled previous results
(Voss et al. 1996a, Voss et al. 1996c).
The errors in HRV parameters due to differences in sampling frequency increase as
HRV is diminished or heart rate is increased, probably because of the close inverse
correlation between heart rate and HRV. Such patient groups are often the target of HRV
analysis. The population in the above-mentioned study was healthy (Voss et al. 1996a)
and, consequently, their HRV was obviously higher than that of the post-AMI population.
In conventional Holter recordings, significant intersubject differences may appear to
exist even when not actually present. Holter recordings should be used with caution in the
analysis of short-term time domain components, the LF/HF ratio and new dynamic HRV
parameters, especially in patient populations with low total HRV. The optimal sampling
frequencies have been proposed to range from 200 Hz to 500 Hz or even higher (Pinna et
al. 1994, Voss et al. 1996c). The measurement of the new dynamic parameters should
preferably be done with a higher sampling frequency than the conventional 128 Hz.
6.2 Optimization of medication
The optimization of beta-blocking therapy was successful, as the great majority of the
patients were on beta-blockers at discharge. The high rate of beta-blocking therapy
probably was effective in keeping the proportion of arrhythmic events low in this patient
population. The adherence to the beta-blocking therapy was well maintained throughout
the study, and the various patient groups did not differ in terms of beta-blocking therapy,
when the mode of death was studied.
The proportion of the patients on beta-blocking medication has varied between 10%
and 70% in previous observational studies and randomized trials (the Multicenter PostInfarction Research Group 1983, Maggioni et al. 1993, McClements & Adgey 1993, El
Sherif et al. 1995, Glancy et al. 1995, Perkiömäki et al. 1995, Moss et al. 1996,
Hartikainen et al. 1996, Michaels & Goldschlager 2000, La Rovere et al. 2001, Bailey et
al. 2001). Data from several studies have convincingly shown that beta-blocking
medication reduces specifically the incidence of SCDs in high risk post-AMI populations
(Beta-Blocker Heart Attack Trial Group (BHAT) 1982, Packer et al. 1996, Gottlieb et al.
1998, MERIT-HF investigators 1999, CIBIS-II investigators 1999). The current findings
suggest that beta-blockers provide an increased benefit for a specific manifestation of risk
in the post-AMI patients, namely the early occurrence of fatal arrhythmia events.
However, this protective effect does not appear to be maintained for more than 18 months
after the AMI. Similarly, a recent randomized trial including patients with a depressed
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ejection fraction and a remote AMI (Moss et al. 2002) showed that in the presence of
effective therapy of heart failure including beta-blockers the survival benefit of the ICD
therapy began relatively late compared to previous trials without optimized medical
therapy (Moss et al. 1996, Buxton et al. 1999).
The proportion of patients receiving ACE inhibitors or AT II blockers was 46%–47%
(II–V). The percentage of patients using this medication was somewhat higher in the
group experiencing a cardiac death or an arrhythmic event, reflecting the deterioration of
left ventricular systolic function in this patient group. In light of the more recent large
multicenter studies, the great majority of patients convalescing from AMI should be
prescribed ACE inhibitors (Yusuf et al. 2000).
In the presence of a high percentage of beta-blocker and ACE inhibitor treatment after
AMI, risk stratification by means of several tests is impaired. Thus, in clinical practice
perhaps more emphasis should be placed on optimizing medical treatment rather than on
risk stratification.
6.3 Proportion and temporal distribution of sudden cardiac death
after acute myocardial infarction
The temporal distribution of SCDs was altered in this study as compared to previous
studies, with the majority of the SCDs occurring after the first 18 months after the AMI
(V). In addition to the changed temporal distribution of SCD events, the proportion of
SCDs was smaller, i.e. less than 40%, than in previous studies (Myerburg et al. 1992,
Myerburg et al. 1993, Myerburg et al. 1997, Zipes & Wellens 1998, Michaels &
Goldschlager 2000, Huikuri et al. 2001), supporting the theory that the epidemiological
pattern of SCD risk is modified by improved adherence to beta-blocking therapy.
6.4 Heart rate variability as a risk predictor
6.4.1 Time and frequency domain parameters as predictors of mortality
(II,III,V)
Numerous previous studies assessing the power of HRV indexes, such as SDNN and
various spectral components of HRV, have shown that these parameters predict mortality
when measured in the convalescent phase of AMI (Kleiger et al. 1987, Bigger et al. 1992,
Hartikainen et al. 1996, Fei et al. 1996, Zuanetti et al. 1996, Lombardi et al. 1996, La
Rovere et al. 1998). In study II, most of the conventional HRV parameters were able to
predict mortality in the univariate analysis, but after adjustment for clinical variables and
left ventricular function their predictive power was lower than that previously reported,
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except for the LF/HF ratio. The most commonly used time domain parameter, SDNN,
failed to predict SCD or arrhythmic events. However, it seemed to be a marker of allcause or non-sudden cardiac mortality. It is probable that the HRV markers reflect poor
overall health more than the actual arrhythmic risk.
Some salient differences between the present and previous observational studies exist.
The first original studies showing the association of reduced HRV with mortality rates are
from the prethrombolytic era (Kleiger et al. 1987, Bigger et al. 1992). Since the early
1980s, both the medical and invasive treatments of post-AMI patients have significantly
changed, as beta-blockers, angiotensin-converting enzyme inhibitors, and
revascularization, for example, are used now more frequently, resulting in much lower
mortality figures. More recently, other studies showing the performance of traditional
HRV parameters, such as the Autonomic Tone and Reflexes After Myocardial Infarction
(ATRAMI) study may suffer from selection bias, as they did not include consecutive
post-AMI patient populations (La Rovere et al. 1998).
6.4.2 Fractal analysis of heart rate variability and mortality (II)
The reduction of the short-term fractal scaling exponent α1 was initially observed in
patients with congestive heart failure (Peng et al. 1995). Subsequently, the reduced
scaling exponent was found to provide prognostic information among patients with
depressed left ventricular function (Mäkikallio et al. 1999b, Huikuri et al. 2000,
Mäkikallio et al. 2001, Perkiömäki et al. 2001). The present study shows that this
property can be extended also into post-AMI patient groups with preserved left
ventricular systolic function. One of the main advantages of using detrended fluctuation
analysis is the easiness of the editing process when compared to traditional parameters,
because editing of ectopic beats is not needed.
The physiologic background between abnormal fractal correlation properties and
mortality risk has not been fully established. There is evidence that sympathetic
activation can cause impairment of α1. It has been shown that intravenous infusion of
norepinephrine leads to the reduction of short-term fractal scaling exponent values, and it
seems that high intrinsic levels of norepinephrine reflecting sympathoexcitation are
related to random RR interval dynamics in patients with heart failure (Tulppo et al.
2001b). This suggests that an increase in the randomness of short-term heart rate behavior
can be a specific marker of neurohumoral and sympathetic activation, and thus be
associated with an increased risk of adverse cardiovascular events.
The value of fractal analysis in predicting arrhythmic mortality was not investigated in
this study, because there is an on-going study in a larger combined patient group where
its predictive power is to be assessed and compared to HRT.
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6.5 T-wave alternans
Previous studies reported that sustained TWA predicts the occurrence of life-threatening
arrhythmic events (Rosenbaum et al. 1994, Hohnloser et al. 1998, Armoundas et al.
1998a, Armoundas et al. 1998b, Ikeda et al. 2000, Ikeda et al. 2002). Most studies have
mainly included randomly selected patient populations with various clinical
characteristics (Rosenbaum et al. 1994, Estes, III et al. 1997, Hohnloser et al. 1998,
Armoundas et al. 1998a, Armoundas et al. 1998b). In our study, sustained TWA during
the predischarge exercise test is not associated with an increased risk of mortality among
survivors of AMI in whom medical therapy is optimized before discharge. The previous
studies included mostly high-risk populations with a recent episode and a high recurrence
rate of ventricular tachyarrhythmias. The risk of sudden death due to ventricular
tachyarrhythmias in the present material is much lower among survivors of AMI (V)
when the beta-blocker and ACE inhibitor therapy is optimized according to current
guidelines. This will lead to an increase in the rate of false-positive results in TWA tests.
According to Bayes’ theorem, the TWA test will provide poor positive accuracy in such a
low risk population. Raising the cutoff point for positive TWA tests could decrease the
number of false-positive results. The optimal cutoff point in this type of post-AMI
population remains to be defined. In a recent post-AMI study of 850 patients, the
association of sustained TWA was seen with arrhythmic endpoints, but these results
cannot be generalized, as the tests were done later after the infarction (mean 2.7 months),
the percentage of patients taking beta-blockers was low (13%) and patients who died of
nonarrhythmic causes were excluded from the analysis (Ikeda et al. 2002). It has been
shown that beta blocking therapy reduces the amplitude of TWA (Klingenheben et al.
2001).
A great number of patients did not reach the target heart rate of 105 beats/min during
the exercise, and it is possible that some of them might have developed sustained TWA if
the heart rate had been increased by some other method. It can be discussed whether the
TWA test should be done off beta-blockers. This might give a better predictive value for
the TWA test, but it would make it less feasible in a clinical setting of post-AMI patients
before discharge from the hospital. It is hardly clinically possible, or even acceptable, to
withdraw beta-blocker therapy at this phase in post-AMI patients.
The problem of not reaching the target heart rate can be overcome by performing the
TWA test by atrial or ventricular pacing, which will render the test invasive and thus not
suitable for routine post-AMI risk stratification. Even so, in some patients, atrial pacing
will result to the development of Wenckebach AV-block, which can ameliorate the TWA
analysis. The concordance between atrial pacing and exercise testing has been confirmed
(Hohnloser et al. 1997), but the concordance between ventricular pacing and exercise
testing has not been confirmed.
In the study by Ikeda et al (Ikeda et al. 2000), the exercise test was performed 20 days
after AMI, whereas we measured TWA about 8 days after AMI. There are some
preliminary data that suggest that the TWA test should be performed more than 1 to 2
weeks after AMI to improve its prognostic power (Ikeda et al. 2000, Bloomfield et al.
2002, Ikeda et al. 2002). It is possible that the process of infarct scar formation and the
development of the arrhythmic substrate is a temporal time-dependent phenomenon,
81
which may lead to an increased number of false-positive test results in the early phase
after the AMI. Later on, after formation of a permanent scar, the substrate may become
more stable and any test assessing the presence or absence of arrhythmic substrate may
provide more powerful prognostic information at this phase. Results of signal-averaged
ECG studies support this idea (El Sherif et al. 1989, El Sherif et al. 1995). Thus, TWA is
not suitable as a part of predischarge risk stratification.
In our series, TWA could be analyzed reliably in less than two thirds of the patients,
because many of the patients were not able to reach the target heart rate of 105 beats/min
(III). It is obvious that an ability to measure TWA during an exercise test is a prerequisite
that excludes a notable proportion of high-risk patients. To emphasize this, the group that
could not perform the test was combined with the group unable to reach the target heart
rate.
The adoption of so-called B criteria has been recommended recently (Bloomfield et al.
2002). By using the B criteria, a number of incomplete results could be reclassified as
negative. This in fact would increase the number of false-negative results in this material,
and therefore impair the value of TWA in predicting mortality. This would be due to a
higher mortality in the “new” negative patients than that in the patients with complete
negative tests.
6.6 Natriuretic peptides
Natriuretic peptides have been shown to be strong predictors of cardiac and all-cause
mortality after AMI (Svanegaard et al. 1992, Hall et al. 1994, Omland et al. 1996,
Arakawa et al. 1996, Darbar et al. 1996, Richards et al. 1998, Retterstol et al. 2001, de
Lemos et al. 2001, Crilley & Farrer 2001, Jernberg et al. 2002), and repetitive
measurements of natriuretic peptides may improve their predictive value (Stanek et al.
2001). The power of natriuretic peptides to predict arrhythmic events has not been
thoroughly studied earlier in post-AMI populations. In patients with chronic heart failure,
BNP was an independent predictor of sudden cardiac death (Berger et al. 2002). In study
IV, all the measured peptides were predictive of arrhythmic mortality. BNP was better
than ANP or N-ANP in predicting sudden arrhythmic mortality after AMI, which is in
concert with previous reports comparing BNP to N-ANP and ANP as a risk indicator for
all-cause and cardiac mortality (Omland et al. 1996, Arakawa et al. 1996, Darbar et al.
1996, Richards et al. 1999, Berger et al. 2002). There may be several reasons why BNP
provides more specific information on the risk of SCD than the other peptides or even the
measurement of left ventricular systolic function. ANP and N-ANP are more closely
related to atrial volume loading, whereas BNP secretion from the ventricles is increased
in the failing heart (Yasue et al. 1994, Luchner et al. 1998) and is released by the
ventricles in response to increased pressure, stretch and hypertrophy (Chen & Burnett
2000). Ventricular stretch, hypertrophy, and fibrosis can have a significant influence on
cardiac electrophysiological properties via mechano-electrical feedback (Reiter et al.
1988, Hansen et al. 1990, Kowey et al. 1991, Zabel et al. 1996) After myocardial
infarction, for example, acute volume load, and compensatory ventricular hypertrophy of
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the noninfarcted region, have been shown to increase QT dispersion (Zaidi et al. 1996),
which in turn might facilitate reentry and thus development of ventricular
tachyarrhythmias. Therefore, BNP may be an indirect marker of the mechanical factors
predisposing the patient to the onset and perpetuation of life-threatening arrhythmias.
The point of time of making the peptide measurement has an effect on the predictive
accuracy of vasoactive peptides, since the secretion of natriuretic peptides varies
according to the changes in hemodynamics in the immediate post-AMI period (Motwani
et al. 1993, Uusimaa et al. 1999). BNP has been shown to be more stable than ANP with
a longer half-life (Mukoyama et al. 1991). After an acute myocardial infarction, the
reliability of BNP in indicating left ventricular dysfunction is not as dependent on the
time point of measurement as that of ANP or N-ANP (Motwani et al. 1993, Uusimaa et
al. 1999).
One of the main differences between study IV and the previous reports studying the
prognostic value of natriuretic peptides is in the usage of medication. The percentage of
patients receiving beta-blockers has been as low as 11–65%, except in the study by
Richards et al., where it reached 86% (Richards et al. 1998). Only 18% of the deceased
were on beta-blockers in the above mentioned study by Berger et al compared to 33% in
the surviving group (Berger et al. 2002). It is well known that beta-blocking therapy has a
significant influence on both the autonomic risk markers of sudden arrhythmic mortality,
as well as the incidence of SCD (Sandrone et al. 1994, Mortara et al. 2000).
The measurement of vasoactive peptides may help recognize patients at increased risk
after AMI. Berger et al. suggested the use of BNP measurements for determining which
patients might benefit from ICD implantation (Berger et al. 2002). In study IV, the
positive predictive value of the measurements was so low that this implication cannot be
generalized to a conventional post-AMI population. It may, however, work in a
subpopulation already at an increased risk of sudden arrhythmic death.
6.7 Other predictors of mortality
6.7.1 Clinical parameters as predictors of all-cause and cardiac
mortality (II, III)
Advanced age, the presence of diabetes or congestive heart failure all predicted
independently both all-cause and cardiac mortality (II, III), as reported previously
(Peterson et al. 1997). The diagnosis of previous myocardial infarction predicted an
increased risk of mortality (II) in univariate analysis. Of in-hospital clinical features, the
location of the infarct in the anterior wall was associated with increased all-cause and
cardiac mortality when compared to other location, in concordance with prior studies
(Ryan et al. 1999). Non-Q-infarctions were linked with increased mortality in univariate
analysis but not after adjusting for other clinical features. If the patient did not receive
reperfusion therapy, either thrombolysis or primary PTCA, both the risks of all-cause and
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cardiac mortality risk were higher, which is in concert with prior studies (GISSI 1987,
Wilcox et al. 1990). This finding can be associated partly with the worse prognosis of a
non-ST-elevation infarction, as well as with the high risk patients who had either a
contra-indication to thrombolytic therapy or who were not offered reperfusion treatment
at presentation because of concomitant diseases.
6.7.2 Functional impairment and chronotropic incompetence (II, III)
Poor functional capacity with NYHA class III–IV at discharge from the hospital was
associated with both all-cause mortality and cardiac mortality in the univariate analysis,
and with all-cause mortality in the multivariate analysis. In study III, inability to perform
the exercise test or to reach the target heart rate of 105 beats/min during the exercise test
was the strongest predictor of all-cause and cardiac deaths. Previous studies in various
populations have shown that chronotropic incompetence during exercise is a predictor of
mortality (Lauer et al. 1996, Lauer et al. 1998, Lauer et al. 1999, Cole et al. 1999), but
there has been little information on the prognostic value of heart rate responses to
exercise after an AMI. Of course, the variable dosage of beta-blockers has an influence
on heart rate responses during the exercise, despite adjusting the dose according to resting
heart rate. Despite this, the prognostic significance of poor chronotropic reserve is
preserved in the post-AMI population receiving beta-blockers (III). A simple exercise test
seems promising in identifying high-risk patients even in post-AMI patients with
adequate treatment.
6.7.3 Other test results as predictors of mortality
In numerous previous studies, it has been shown that reduced left ventricular systolic
function, impaired BRS, increased QT dispersion, presence of VT in 24-h ECG
recordings and the presence of late potentials in SAECG are able to predict all-cause and
sudden cardiac mortality when measured in the convalescent phase after AMI (Simson
1981, the Multicenter Post-Infarction Research Group 1983, Bigger et al. 1984, Steinberg
et al. 1992, Zareba et al. 1994, Glancy et al. 1995, Mortara et al. 1997, La Rovere et al.
1998, Hohnloser et al. 1999). Impaired left ventricular ejection fraction was associated
with increased all-cause mortality in the multivariate analysis (II, III), and in studies IV
and V, it was associated with SCD and non-sudden cardiac death as well as arrhythmic
events, in concert with prior studies. In our study, pathological BRS did not predict
mortality or arrhythmic events (III, V). However, the mean value of BRS was lower in
the patients who died during the study and also in the patients who experienced nonsudden cardiac death. A prolonged QT interval predicted all-cause mortality in study III
in the univariate analysis but not in the multivariate analysis. Increased QT dispersion
predicted independently all-cause and cardiac mortality (II, III), but was not associated
with sudden cardiac deaths (V). In study V, the presence of nsVT in a 24-hour ECG
84
recording predicted SCD and arrhythmic events. Pathological SAECG predicted SCD
independently (V); in studies II and III, a prolonged duration of QRS complex but not
pathological SAECG was associated with an increased risk of both all-cause and cardiac
mortality in the multivariate analysis. This probably reflects the fact that the duration of
the averaged QRS complex could be the most important risk predicting parameter in
SAECG analysis (Malik et al. 1992, El Sherif et al. 1995).
6.7.4 Combination of tests
Combination of test results that reflect various aspects of risk profile has been suggested
as a method to improve risk stratification. However, in a general post-AMI population
with optimized medication in the reperfusion era, the prevalence of a single risk marker is
rather low and when combined, the prevalence of multiple risk markers is even lower. For
example, in a recent consecutive study, the presence of nsVT together with depressed left
ventricular EF was only 2.4% (Hohnloser et al. 1999).
Analysis of left ventricular ejection fraction after AMI is widely available, and it has
been clearly associated with prognosis. Therefore it is tempting to combine other risk
indicators with ejection fraction. Combining the measurement of ejection fraction with
HRV analysis (II) or the assessment of natriuretic peptides (IV) improved the risk
predicting power of the short-term scaling exponent α1 or BNP, respectively.
6.8 Statistical considerations
6.8.1 Accuracy of diagnostic tests
It is obvious that the developing therapy for post-AMI patients has changed the validity
of several risk markers, which in previous studies proved to be effective with a high
positive predictive value. Because mortality is decreasing, it is becoming more and more
difficult to find any marker with a high predictive value and the rate of false-positive
results, on the other hand, will increase. Any test used in a low-risk population will
provide poor positive accuracy according to Bayes’ theorem (Hughes 1993, Brophy &
Joseph 1995). It seems that in the presence of a high level of beta-blocking and ACE
inhibitor treatment, most of the risk stratification by means of several tests loses some of
its value.
The value of a diagnostic test is reflected by its sensitivity, specificity and both
positive and negative predictive accuracies. Sensitivity and specificity reflect the validity
of the test, and they do not depend on the incidence of mortality in the population. The
predictive accuracies, on the other hand, reflect the performance of the test in the selected
population, and depend on the incidence of mortality. Thus, when dealing with
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populations with low mortality rates, even “good” tests will lose their diagnostic value.
The value of some tests will improve, when changing from the general post-MI
population to more specific groups.
Choosing the cutoff point is a major problem in prognostic studies. Using a predefined
cutoff point is more acceptable to facilitate the comparison to previous reports. Reporting
receiver-operating characteristic (ROC) curves can, however, help in overcoming this
problem.
Cox multivariate analysis is a widely used approach in risk stratification studies. Some
problems are clearly inherent: the number of parameters included in a multivariate model
should be low enough depending on the number of patients or events. The combination of
various risk parameters leads to still another problem, when they are correlated to each
other. The inter-related risk predictors will compete with each other in the multivariate
analysis.
6.8.2 Incomplete/nondiagnostic tests
It was clearly seen, that the percentage of nondiagnostic test results was higher in patients
who died during the follow-up than those who survived. (III). As far as the exercise test
or TWA analysis during exercise is concerned, it is very clear that a patient who is not
able to perform the test is at a higher risk than a patient able to go through the test. Most
often this is explained by poor overall health and functional status, frequently linked to
various co-morbid conditions. These are in turn either associated with both all-cause and
cardiac mortality or they are risk factors for other diseases which will be finally fatal.
Most ECG-based risk assessment methods are sensitive to phenomena that are
frequently seen in patients with a high risk of mortality. Nearly all of these methods,
except for 24-hour ECG recordings, are invariably invalidated by bouts of atrial
fibrillation, flutter and the presence ectopic beats. The prevalence of all such arrhythmias
is higher in patients who have advanced cardiac disease than in patients with normal
hearts. The reported insensitivity of DFA measurement to ectopics makes this method
more promising as a risk stratifier than the traditional ones.
BRS analysis is difficult in patients in whom baroreflex sensitivity is very low or
potentially zero. By definition, when BRS is diminished, the correlation coefficient will
get lower. Thus some of these patients are sometimes classified into the group in whom
BRS cannot be measured despite repeated attempts. Another patient group in whom BRS
is difficult measure noninvasively includes patients with advanced ASO. The severity of
ASO is often linked to that of coronary disease and thus to cardiac mortality. It was
clearly seen that the inability to measure BRS was a marker of increased all-cause and
cardiac mortality in post-AMI patients (III).
The measurement of QT interval can be inaccurate as some leads must be excluded
because the end of the T-wave cannot be measured. In some of these patients, the T-wave
in the excluded lead can be flat. Especially in these cases, the actual duration of the Twave may be prolonged leading to a lengthened QT interval, or a prolonged QT
dispersion.
86
6.8.3 Limitations and biases of observational study designs
Many previous studies on AMI patients have mainly been done from selective patient
materials instead of including consecutive post-AMI patients. In several studies, the
number of patients that have evaluated the prognostic accuracy of various noninvasive
markers has been small. The studies have been retrospective, and they have not been
adjusted sufficiently for baseline risk. The follow-up results of excluded patients usually
are not reported, which prevents generalization of the results to all survivors of AMI.
Therefore, based on these results, it is very difficult to state which risk predictor would be
clearly superior. The patient material in studies II, III, and V included a consecutive,
predefined population, and the patients in study IV were a subgroup of this patient
material. The Danish patients in study II belonged likewise to a consecutive patient
series. Several other risk predicting studies have included randomly selected patient
populations with various clinical characteristics.
Another problem in previous studies, and also present in this one, is that some highrisk patients are excluded from the study because of the strict inclusion criteria. However,
including moribund patients in the study is hardly worthwhile, or even ethical, as their
adverse prognosis is already well established.
A relatively small number of sudden deaths (22 cases) may prevent the generalization
of the results to other post-AMI populations. However, this is one of the largest
prospective observational studies assessing the prediction of sudden death in a
consecutive post-AMI population that includes several pre-defined arrhythmia risk
markers in the study design. In the largest prospective observational study assessing the
predictive power of autonomic markers, 30 patients reached the combined endpoint of
sudden death and non-fatal ventricular tachycardia (La Rovere et al. 2001). Even in
studies with a moderate-sized patient population, i.e. hundreds of patients, the sample
size may be too small for generalization of the results. In this work, however, the patient
materials in studies III, IV and V belonged to a single-center trial with uniform and
optimized treatment.
In observational studies, there is often inaccuracy in defining the exact mode of death,
particularly the occurrence of arrhythmic death. Sudden deaths occurring within 1 hour of
the onset of symptoms commonly have been attributed to arrhythmia, but many other
pathophysiologic conditions that evolve rapidly may lead to sudden death. On the other
hand, many of the deaths defined as nonarrhythmic may be due to tachyarrhythmias
(Moss et al. 1996). Marked discordance has been found between different observers in
the classification of deaths. (Ziesche et al. 1995). Therefore, it has been suggested that
total mortality is probably the only reliable endpoint in trials on heart failure or
myocardial infarction (Ziesche et al. 1995, Priori et al. 2001).
In most previous observational studies assessing arrhythmic mortality, SCD together
with non-fatal ventricular tachyarrhythmia has been used as a combined endpoint
(Maggioni et al. 1993, McClements & Adgey 1993, Glancy et al. 1995, Hartikainen et al.
1996, Hohnloser et al. 1999, La Rovere et al. 2001). This endpoint is partly biased,
because many other rapidly evolving conditions than arrhythmias can also lead to sudden
death. In fact, recent studies on patients with ICD indicate that many of the deaths
defined as sudden were not due to cardiac arrhythmia (Pratt et al. 1996, Grubman et al.
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1998). Therefore, in study V, the cases of SCD, which were defined by clinical criteria,
were also separately analyzed from arrhythmia events, which were defined by the
documentation of life-threatening arrhythmia and/or autopsy findings. Despite the
relatively low number of SCDs and arrhythmia events, the arrhythmia risk markers would
have been expected to have a better predictive accuracy in order to be considered useful
for clinical purposes, e.g. for evaluation of the candidacy of an ICD. The observational
nature of the study design may not allow definite conclusions about the clinical utility of
the arrhythmia risk variables. Therefore, the predictive power of the arrhythmia risk
markers should ideally be confirmed in larger randomized trials with special attention to
the adherence to evidence-based medical therapy, including beta-blockers.
6.9 Prediction of susceptibility to life-threatening ventricular
arrhythmias after myocardial infarction
Although several clinical and arrhythmia risk markers were associated with an increased
risk of cardiac mortality in this study, many of them did not identify a risk for a specific
type of cardiac death, either sudden or non-sudden except the presence of impaired LV
function, the presence of nsVT in 24-hour ECG recording and an abnormal SAECG (IV–
V). Similarities in the risk profile between the patients who died suddenly or nonsuddenly imply that there are common factors predisposing the patient to both sudden
and non-sudden cardiac death, probably with the severity of CAD and left ventricular
dysfunction being the most important.
The only previously accepted risk variables that were able to predict SCD and
arrhythmia events were impaired left ventricular function, nsVT, and abnormal SAECG,
but the positive predictive accuracy of these variables was relatively low (V). However,
as a novel risk predictor, an increased concentration of natriuretic peptides (IV) was
linked to the incidence of SCD. Patients who experienced arrhythmia events had a less
severe functional NYHA class, compared to those dying of a non-SCD despite the equal
left ventricular dysfunction. This is in agreement with a recent trial showing that a higher
proportion of the patients with less functional impairment will die suddenly, while the
majority of the patients with advanced functional impairment will experience a non-SCD.
(MERIT-HF investigators 1999).
In contrast to present observations, previous studies have suggested that the markers of
autonomic nervous function (Hartikainen et al. 1996, La Rovere et al. 2001),
measurements from standard 12-lead ECG (Glancy et al. 1995, Perkiömäki et al. 1995,
Michaels & Goldschlager 2000, Bailey et al. 2001), or TWA analysis (Rosenbaum et al.
1994) could specifically predict the risk of SCD or arrhythmia events, alone or in
combination with other variables. The new dynamic HRV analysis methods, such as α1,
are valuable in predicting cardiac and all-cause mortality. The value of α1 in predicting
arrhythmic mortality will be studied in a larger patient group. The value of TWA during
the predischarge test as a predictor of mortality is negligible. In a post-AMI population
with optimized therapy, particularly the beta-blocking medication, many of the
88
conventional indices of autonomic regulation seem to lose some predictive power when
compared to the previous landmark studies.
7 Conclusions
1. There exists a significant difference between HRV analysis results derived from a 24hour ECG recording system with a low sampling frequency when compared to a
system with a higher sampling frequency, especially in the analysis of some spectral
and newer dynamic parameters. This is more pronounced in subjects with low HRV,
such as post-AMI patients. HRV data should preferably be collected using a high
sampling frequency.
2. Detrended fluctuation analysis of HRV provides prognostic information beyond
clinical variables and left ventricular function in post-AMI patients. The predictive
power of short-term scaling exponent α1 is higher than that of traditional time- and
frequency-domain indexes of HRV in risk stratification.
3. The presence of sustained TWA is not a marker of increased mortality during the
predischarge exercise test in post-AMI patients who are on β-blocker therapy. The
inability to perform an exercise test or to reach the target heart rate of 105 beats/min
needed for TWA analysis is an independent marker of all-cause mortality.
4. High levels of natriuretic peptides are associated with both sudden and non-sudden
cardiac mortality after the infarction in patients after AMI. B-type natriuretic peptide
provides more specific information on the risk of subsequent sudden than nonsudden cardiac death.
5. The epidemiological pattern of SCD in a post-AMI population with optimized
therapy is different compared to that reported in previous studies. SCDs or
arrhythmia events occurred mainly more than 18 months after the AMI. The common
arrhythmia markers provide only limited predictive power on the risk of future SCD
or arrhythmic events in post-AMI patients with optimized beta-blocking therapy.
Many risk variables previously considered to provide information on the risk of SCD
predict better the occurrence of non-SCD.
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