'+')$)' )%%"&$"&"$*)!
,$+0'""&
&"-)*"+0'$*"&#"
"&$&
! "!
!"!
"!
46672/.)4-
'()*&+."+!+!()%"**"'&'+!,$+0'""&' +!&"-)*"+0'$*"&#"')(,$"/%"&+"'&"&$+,))''%
)+%&&*+"+,+'&%)+&''&
$*"&#"
73)48-5)(&;
)'**'),!&",&++"$
'&+ (' )' '+') &++")&+"
)8-)9)(&;
'&+"&"##$"10)0 '+'))"(&"&&
3321)16
)'**'),!)+"#"&&
This thesis was carried out at the Division of Cardiology, Heart and Lung center, University of Helsinki and Helsinki University Hospital, as well as the Research Unit of Internal Medicine, Medical Research Center, Oulu University Hospital and University of Oulu.
The Faculty of Medicine uses the Urkund system (plagiarism recognition) to examine all doctoral dissertations.
ISBN 978-951-51-6660-9 (pbk.) ISBN 978-951-51-6661-6 (PDF) Unigrafia
Helsinki 2020
!!
Sudden cardiac death (SCD) is one of the leading causes of death in the world, accounting for up to 5-20% of all deaths. The vast majority of SCDs are caused by coronary heart disease and approximately half of deaths due to coronary heart disease are SCDs. However, current clinical risk assessment tools are unable to identify a large portion of subjects at risk for SCDs, as in up to 30–
50% of SCD cases, it is the first clinical manifestation of the person’s underlying cardiac disease and another large portion of SCDs occur at persons with a known heart disease but whose risk is considered low.
The objective of this thesis was to examine the use of electrocardiographic markers in the risk prediction of SCD and other cardiac events in the general population. We used a novel method to digitize and digitally assess the paper electrocardiograms (ECGs) of 7217 Finnish adults participating in a health survey, who were followed for over 30 years.
The used paper-to-digital ECG conversion and digital measuring process demonstrated strong agreement with manual ECG assessment in the basic interval and amplitude measurements, although some inter-method differences were noted on QT interval assessment as well. The process enabled efficient digital storing, in addition to an accurate and comprehensive digital assessment of digitized paper ECGs.
Negative U-waves were present in 3.5% of the general population subjects and independently associated with an increased risk of death. Moreover, among men, negative U-waves predicted also cardiac death and hospitalization due to cardiac causes, whereas among women negative U- waves did not associate with an increased risk of cardiac events. Negative U- waves did not independently associate with an increased SCD risk in the entire study population or subgroup analyses of men or women, either.
Early repolarization (ER) was present in 11.1% of subjects aged <50 years and 12.8% of subjects aged ≥50 years. In subjects <50 years, ER was linked to an increased SCD risk, while among ≥50-year-olds ER did not associate with adverse prognosis. ER in women <50 years associated with a four folds higher SCD risk compared to the absence of ER, while in men <50 years the presence of ER did not demonstrate an increase in SCD risk.
Five of the 12 assessed ECG abnormalities independently associated with SCD risk. The cumulative number of these 5 ECG abnormalities, heart rate
>80 beats per minute, PR duration >220 ms, QRS duration >110 ms, left ventricular hypertrophy, and T-wave inversion, predicted an increasing SCD risk. The presence of ≥3 of the 5 ECG abnormalities associated with an over 10-folds SCD risk compared to the absence of ECG abnormalities. The use of the cumulative number of ECG abnormalities in risk prediction improved the discrimination of low and high SCD risk subjects.
To conclude, electrocardiographic markers provide important information about the prognosis of general population subjects. With better risk prediction, the management of general population subjects could be improved by directing diagnostic assessments and medical therapies to high-risk subjects, to ultimately improve their prognosis.
Keywords:Electrocardiography, Digitization, Sudden cardiac death, Early repolarization, Age groups, Prognosis, Risk factors
!# !=
Sydänperäinen äkkikuolema on yksi maailman yleisimmistä kuolinsyistä, kattaen 5-20% kaikista kuolemista. Sepelvaltimotauti aiheuttaa suurimman osan sydänperäisistä äkkikuolemista ja noin puolet sepelvaltimotaudin aiheuttamista kuolemista on äkillisiä. Isoa osaa sydänperäisen äkkikuoleman riskissä olevia henkilöitä ei osata kuitenkaan tällä hetkellä tunnistaa, koska jopa 30–50 %:lla sydänperäisen äkkikuoleman saaneista se on heillä taustalla olevan aiemmin tunnistamattoman sydänsairauden ensimmäinen oire.
Lisäksi monella sydänperäisen äkkikuoleman saaneista henkilöistä on tiedossa sydänsairaus, mutta heidän äkkikuolemariskiänsä on pidettyä pienenä.
Tämän väitöskirjatutkimuksen tavoitteena oli tutkia sydänfilmissä (elektrokardiogrammi, EKG) todettavien muutosten käyttöä sydänperäisen äkkikuoleman ja muiden sydäntapahtumien riskin arvioinnissa.
Tutkimuksessa käytettiin uutta EKG:iden digitointi- ja digitaalimittausmenetelmää, jonka avulla tutkimme 7217 suomalaiseen terveystutkimukseen osallistuneen aikuisen EKG:t. Tutkimuksen henkilöitä seurattiin yli 30 vuoden ajan.
Käytetyn EKG:iden digitointi- ja digitaalimittausmenetelmän tulokset olivat yhdenmukaisia käsin mitattujen keskeisten intervalli- ja amplitudimuuttujien arvojen kanssa, joskin QT intervallin mittaustuloksissa oli lievää eroa mittausmetodien välillä. Menetelmä mahdollisti paperi- EKG:iden tehokkaan digitaalisen säilömisen, sekä tarkan ja kattavan digitaalimittauksen.
Negatiivisia U-aaltoja esiintyi 3,5 % väestöstä ja ne liittyivät itsenäisesti kasvaneeseen kuoleman riskiin. Miehillä negatiiviset U-aallot liittyivät lisäksi kasvaneeseen sydänkuoleman ja sydänsairauksien vuoksi sairaalaan joutumisen riskiin, kun taas naisilla negatiiviset U-aallot eivät liittyneet kasvaneeseen sydäntapahtumien riskiin. Negatiiviset U-aallot eivät itsenäisesti liittyneet sydänperäisen äkkikuoleman riskiin koko tutkimusväestössä tai alaryhmäanalyyseissä miehillä tai naisilla.
Varhainen repolarisaatio (early repolarization, ER) todettiin 11,1 % <50 vuotiaista ja 12,8 % ≥50 vuotiaista henkilöistä. Alle 50-vuotiailla ER liittyi kasvaneeseen sydänperäisen äkkikuoleman riskiin, kun taas ≥50 vuotiailla ER ei vaikuttanut henkilöiden ennusteeseen. Erityisesti alle 50-vuotiailla naisilla ER ennusti yli nelinkertaista sydänperäisen äkkikuoleman riskiä, kun taas alle 50-vuotiailla miehillä ER ei lisännyt äkkikuolemariskiä.
Kahdestatoista tutkitusta EKG-muutoksesta 5 liittyi itsenäisesti kasvaneeseen sydänperäisen äkkikuoleman riskiin. Näiden 5 EKG- muutoksen, syke >80 lyöntiä minuutissa, PR kesto >220 ms, QRS kesto >110 ms, vasemman kammion hypertrofia ja T-aallon inversio, kumulatiivinen määrä ennusti kasvavaa sydänperäisen äkkikuoleman riskiä. Henkilöiden
äkkikuolemariski oli yli kymmenkertainen, jos heillä oli 3 tai enemmän kuin 5 tutkitusta EKG-muutoksesta, verrattuna henkilöihin, joilla ei ollut poikkeavia EKG-muutoksia. EKG-muutosten lukumäärän käyttäminen sydänperäisen äkkikuolemariskin arvioinnissa paransi matalan ja korkean äkkikuolemariskin omaavien henkilöiden erottelua toisistaan merkittävästi.
Yhteenvetona EKG-muutokset tuovat tietoa väestön sydäntapahtumien riskistä. Paremmalla riskiarvioinnilla voitaisiin kohdentaa diagnostisia tutkimuksia ja hoitoja korkean riskin henkilöille ja viime kädessä parantaa heidän ennustettaan.
Avainsanat:Elektrokardiografia, Digitointi, Sydänperäinen
äkkikuolema, Varhainen repolarisaatio, Ikäryhmät, Ennuste, Riskitekijät
$!
This thesis was carried out at the Division of Cardiology, Heart and Lung center, University of Helsinki and Helsinki University Hospital, as well as the Research Unit of Internal Medicine, Medical Research Center, Oulu University Hospital and University of Oulu between 2015 and 2020. I wish to thank Aarne and Aili Turunen Foundation, Aarne Koskelo Foundation, Finnish Cardiac Society, Finnish Medical Foundation, Ida Montin Foundation, Paavo Ilmari Ahvenainen Foundation, Sigrid Juselius Foundation, and the University of Helsinki for financially supporting this study.
My sincerest gratitude goes to Professor Heikki Huikuri for his supervision and guidance in the beginning of this project. His internationally acknowledged expertise in the field of cardiac electrophysiology and sudden cardiac death has been invaluable for the planning, execution, and the overall big picture of this project. His ideas have greatly improved this thesis and inspired me as a researcher as well and I am truly thankful for that.
I also wish to express my deepest gratitude to Professor Juhani Junttila, who joined as a supervisor in the middle of this project. His important suggestions and constructive feedback have remarkably helped me to improve my research and revise my manuscript drafts. I am very thankful for the supervision and advice he has provided me during this project.
I am extremely grateful to my main supervisor Docent Aapo Aro, who originally introduced me to this project and who has been an excellent supervisor and mentor to me. Although there were some geographical limitations during the first years of this research, I have always received prompt and comprehensive answers to my questions and enquiries. I deeply value his patient guidance, encouragement, and belief in my abilities throughout this project.
Since the beginning of this project, Doctor Antti Eranti has helped me through each stage of this project, from the long hours of ECG measurements in the basement of THL to data analysis and manuscript drafts. He has always provided solutions to the numerous practical problems I have encountered during this project. I would like to express my deepest appreciation to him for his support, guidance, and supervision.
I would like to thank all my co-authors in the publications for their important contributions. I am grateful to Anette for partaking in the many hours of ECG measurements. Many thanks to Kai Noponen and Professor Tapio Seppänen for the development of the ECG digitization software and collaboration. I am very thankful to Tuomas Kenttä for the development and support with EASE software, and his help with the statistical analyses with R program. I am deeply grateful to and value the collaboration with the Finnish Institute for Health and Welfare, with the feedback I have received from Doctor Markku Heliövaara and Professor Paul Knekt, and especially the
assistance with data handling and cooperation with Harri Rissanen. Many thanks should also go to Docent Tuomas Kerola, Docent Jani Tikkanen, and Docent Olli Anttonen for their important contributions to this project.
I would also like to extend my gratitude to Docent Sinikka Yli-Mäyry and Doctor Jari Tapanainen for reviewing this thesis. Furthermore, I am thankful to Docent Mika Lehto and Docent Tuomo Nieminen for evaluating the progress of this project in my thesis committee.
My colleagues in Päijät-Häme Central Hospital and former colleagues in Hyvinkää Hospital are acknowledged for their support and friendship as well.
Special thanks go to my friends Kasperi, Lauri, Marko, Samppa, and Tommi. Our fun times together has provided me the crucially needed counterbalance to the long hours put into this project. I would also like to thank Sari and Timo for their encouragement in scientific research and for introducing me to Aapo and consequently to this project in the first place. I would like to also acknowledge Helmi and Hilla, the two poodle dogs, that have frequently kept me company and brought me joy along the way of writing this thesis.
This project would not have been possible without the support of my loving family. I am deeply grateful to my parents, Petra and Jukka, for the excellent upbringing, in addition to the unconditional love, and continuous support I have received through my life. I would also like to thank Miikka and Oona for being a wonderful brother and sister to me.
Finally, I owe my warmest and most loving thanks to Roosa, for her unlimited patience, support, and care she has provided me. In the end, her companionship and support were what made this thesis possible.
16th of September, Helsinki Finland Arttu Holkeri
!!!
ABSTRACT 3 TIIVISTELMÄ 5 ACKNOWLEDGEMENTS 7 TABLE OF CONTENTS 9 ABBREVIATIONS 12 LIST OF ORIGINAL PUBLICATIONS 13 1 INTRODUCTION 14 2 REVIEW OF THE LITERATURE 16
2.1 CARDIAC ELECTROPHYSIOLOGY AND
ELECTROCARDIOGRAPHY ... 16
2.1.1 CARDIAC ACTION POTENTIAL ... 16
2.1.2 ELECTROPHYSIOLOGY OF ARRHYTHMIAS ... 18
2.1.3 PRINCIPLES OF ELECTROCARDIOGRAPHY ... 19
2.1.4 DIGITIZATION AND DIGITAL ANALYSIS OF ELECTROCARDIOGRAPHIC RECORDING ... 20
2.2 CARDIOVASCULAR DISEASE MORTALITY AND SUDDEN CARDIAC DEATH ... 22
2.2.1 EPIDEMIOLOGY ... 22
2.2.2 MECHANISMS ... 25
2.2.3 SUDDEN CARDIAC DEATH RISK FACTORS ... 27
2.3 PROGNOSTIC VALUE OF ELECTROCARDIOGRAPHIC MARKERS IN THE SUDDEN CARDIAC DEATH AND CARDIAC EVENT RISK STRATIFICATION ... 30
2.3.1 HEART RHYTHM ... 30
2.3.2 P-WAVE AND PR INTERVAL ... 32
2.3.3 QRS COMPLEX ... 33
2.3.4 EARLY REPOLARIZATION AND J-WAVE ... 35
2.3.5 ST SEGMENT ... 39
2.3.6 T-WAVE ... 39
2.3.7 QT INTERVAL ... 43
2.3.8 U-WAVE ... 44
2.3.9 OTHER ELECTROCARDIOGRAPHIC MARKERS ... 45
2.3.10 COMBINING ELECTROCARDIOGRAPHIC MARKERS TO IMPROVE RISK STRATIFICATION ... 46
2.3.11 THE CURRENT USE OF ELECTROCARDIOGRAPHIC MARKERS IN CARDIOVASCULAR RISK ASSESSMENT ... 49
3 AIMS OF THE STUDY 51 4 METHODS 52 4.1 STUDY POPULATION ... 52
4.2 ELECTROCARDIOGRAPHIC RECORDING, DIGITIZATION, AND DIGITAL MEASURING ... 53
4.3 ADDITIONAL MANUAL ELECTROCARDIOGRAPHIC MEASUREMENTS ... 54
4.4 FOLLOW-UP ... 55
4.5 VALIDATION COHORT POPULATION IN STUDY IV ... 56
4.6 EXCLUSION CRITERIA ... 56
4.7 STATISTICAL ANALYSIS ... 57
5 RESULTS 58 5.1 PAPER ELECTROCARDIOGRAM DIGITIZATION AND DIGITAL MEASUREMENT PROCESS ... 58
5.2 NEGATIVE U-WAVES: PREVALENCE, ASSOCIATION WITH COMORBIDITIES AND PROGNOSTIC SIGNIFICANCE ... 61
5.3 THE EFFECT OF AGE AND SEX ON THE PROGNOSIS ASSOCIATED WITH EARLY REPOLARIZATION ... 64
5.4 IDENTIFYING HIGH SUDDEN CARDIAC DEATH RISK GENERAL POPULATION SUBJECTS USING AN ELECTROCARDIOGRAPHIC RISK SCORE ... 68 6 DISCUSSION 74
6.1 THE PROCESS OF DIGITIZING AND DIGITALLY MEASURING PAPER ELECTROCARDIOGRAM ARCHIVE WITH CUSTOM MADE SOFTWARE ... 74 6.2 NEGATIVE U-WAVES’ SIGNIFICANCE AND LONG-TERM PROGNOSTIC VALUE IN THE GENERAL POPULATION ... 75 6.3 PROGNOSTIC SIGNIFICANCE OF EARLY
REPOLARIZATION ELECTROCARDIOGRAM PATTERN ACCORDING TO AGE AND SEX IN THE GENERAL
POPULATION ... 77 6.4 ELECTROCARDIOGRAPHIC RISK SCORE FOR
PREDICTION OF SUDDEN CARDIAC DEATH IN THE GENERAL POPULATION ... 79 6.5 STRENGTHS AND LIMITATIONS OF THE STUDIES ... 81 6.6 CLINICAL IMPLICATIONS AND FUTURE
PERSPECTIVES ... 82 7 CONCLUSIONS 86 REFERENCES 88 ORIGINAL PUBLICATIONS 114
#!
bpm beats per minute
CI confidence interval
DAD delayed afterdepolarization CHD coronary heart disease
EAD early afterdepolarization ECG electrocardiogram
ER early repolarization
ETT ECG Trace Tool
fQRS fragmented QRS complex
HR hazard ratio
ICD implantable cardioverter defibrillator IDI integrated discrimination improvement IVCD intraventricular conduction delay JTc JT interval corrected for heart rate κ Cohen's kappa coefficient
LBBB left bundle branch block LVEF left ventricular ejection fraction LVH left ventricular hypertrophy
MI myocardial infarction
Non-SCD non-sudden cardiac death NRI net reclassification improvement PAC premature atrial contraction PEA pulseless electrical activity PTF P terminal force
PVC premature ventricular contraction RBBB right bundle branch block
SCA sudden cardiac arrest SCD sudden cardiac death
SD standard deviation
VF ventricular fibrillation VT ventricular tachycardia QTc QT interval corrected for heart rate
!"!
This thesis is based on the following publications:
I Holkeri A, Eranti A, Kenttä TV, Noponen K, Haukilahti MAE, Seppänen T, Junttila MJ, Kerola T, Rissanen H, Heliövaara M, Knekt P, Aro AL, Huikuri HV. “Experiences in Digitizing and Digitally Measuring a Paper- Based ECG Archive.” Journal of Electrocardiology 2018;51(1):74-81.
II Holkeri A, Eranti A, Haukilahti MA, Kerola T, Kenttä TV, Noponen K, Seppänen T, Rissanen H, Heliövaara M, Knekt P, Junttila MJ, Huikuri HV, Aro AL. “Prevalence and Prognostic Significance of Negative U- waves in a 12-lead Electrocardiogram in the General Population.” The American Journal of Cardiology 2019;123(2):267-273.
III Holkeri A, Eranti A, Haukilahti MAE, Kerola T, Kenttä TV, Tikkanen JT, Rissanen H, Heliövaara M, Knekt P, Junttila MJ, Aro AL, Huikuri HV.
”Impact of age and sex on the long-term prognosis associated with early repolarization in the general population.” Heart Rhythm 2020;17(4):621-628.
IV Holkeri A, Eranti A, Haukilahti MAE, Kerola T, Kenttä TV, Tikkanen JT, Anttonen O, Noponen K, Seppänen T, Rissanen H, Heliövaara M, Knekt P, Junttila MJ, Huikuri HV, Aro AL. ”Predicting sudden cardiac death in a general population using an electrocardiographic risk score.” Heart 2020;106(6):427-433.
The publications are referred to in the text by their roman numerals.
!"!
According to the most accepted definition, sudden cardiac death (SCD) is a non-traumatic unexpected circulatory arrest of cardiac etiology within an hour of symptom onset (Al-Khatib et al., 2018; Chugh et al., 2008; Priori et al., 2015). SCD constitutes as a major public health problem, accounting for up to 25–50% of all cardiovascular deaths and 5–20% of all deaths (Al-Khatib et al., 2018; Chugh et al., 2008; Hayashi et al., 2015; Priori et al., 2015). It usually occurs in people with an underlying cardiac condition, which is in 70–80% of the cases coronary heart disease (Junttila et al., 2016; Myerburg and Junttila, 2012; Tseng et al., 2018). Accordingly, medical therapy is recommended in many patient populations with cardiac disease to reduce the risk of SCD (Al- Khatib et al., 2018; Priori et al., 2015). Moreover, implantable cardioverter defibrillator (ICD) is recommended for a subset of cardiac patients in markedly high SCD risk (Al-Khatib et al., 2018; Priori et al., 2015). However, the major obstacle in SCD prevention is that in a large portion of cases, approximately 30–50% of SCD victims, the cardiac arrest is the first symptom of the underlying undiagnosed cardiac condition (Hayashi et al., 2015;
Myerburg, 2001). Furthermore, many of the SCD victims have diagnosed heart disease, but prior to the event, their SCD risk is estimated to be low with current risk prediction tools (Myerburg, 2001). Therefore, the need for better SCD risk assessment methods is evident, to identify the subjects with a high individual SCD risk from the large populations with low risk on average.
Electrocardiogram (ECG) is one of the most commonly used tests in the assessment of cardiovascular conditions. Although its major role in clinical practice is as a diagnostic procedure in acute events and more chronic conditions, its use as a prognostic tool has been actively examined in recent years and decades. Consequently, numerous ECG abnormalities that associate with an increased risk of SCD and cardiac events have been identified (Eranti et al., 2016; Wellens et al., 2014). Still, the prognostic significance of some ECG abnormalities in the general population has remained unknown. Furthermore, in most cases, the clinical utility of a single ECG risk marker in general population subjects has remained somewhat limited due to only a minor increase in the absolute risk of a cardiac event associated with individual markers. On the other hand, the presence of an ECG abnormality could act as a signal of an underlying undiagnosed cardiac disease and the subject’s prognosis could be improved with timely diagnosis and adequate treatment.
Furthermore, the integration of several ECG or other risk markers into risk models could improve their usefulness and clinical impact (Wellens et al., 2014).
One difficulty in the investigation of the long-term prognostic importance of ECG markers has been, that a great number of the archived ECGs of older study populations exist only in paper format. This limits the storing and
sharing of these databases. Furthermore, some of the novel, more complicated ECG parameters require sophisticated algorithms and consequently computer software for assessment (Kenttä et al., 2018; Waks et al., 2016). Although several digitization and digital measuring processes have been proposed, with unique advantages and disadvantages, no single one is currently in universal use (Waits and Soliman, 2016).
U-wave was first described as a component of the normal cardiac cycle in the ECG over 100 years ago (Einthoven, 1912). It has received much less interest than the other waveforms of the ECG and, for example, the exact mechanism underlying the U-wave is still not fully understood (Eyer, 2015;
Rautaharju, 2015). U-waves have normally positive amplitudes, with negative U-waves considered abnormal. Consequently, negative U-waves have been linked to cardiac diseases (Kishida et al., 1982; Watanabe, 1967) and cardiac morbidity and mortality in special cardiac populations (Bellet et al., 1957;
Kishida et al., 1987). However, the prognostic significance of negative U-waves in the general population is unknown.
Early repolarization (ER) has been linked to an increased risk of SCD, in addition to cardiac death and death from any cause in the general population (Cheng et al., 2016). Furthermore, several ER patterns have been shown to associate with more adverse prognosis (Adler et al., 2013; Roten et al., 2016;
Tikkanen et al., 2011). Whether the prognosis associated with ER differs in patient subgroups has been also examined. ER seems to associate with cardiac mortality more strongly among middle-aged and young adults, compared to the elderly (Hisamatsu et al., 2013; Sinner et al., 2010). Yet, whether age affects the risk of SCD associated with ER is not well established. Furthermore, controversy remains whether sex affects the prognosis of ER (Olson et al., 2011; O’Neal et al., 2016; Rollin et al., 2012; Sinner et al., 2010).
Several combinations of ECG risk parameters have been shown to predict a high SCD risk (Aro et al., 2017c; Reinier et al., 2015; Waks et al., 2016).
However, the used ECG markers in these studies have been of varying complexity. Moreover, some of the risk models were developed using specific case-control populations with the majority of the controls having known cardiac conditions. Thus, no ECG risk model is yet in clinical use for SCD prediction in the general population.
The present thesis aimed to investigate the feasibility of a novel paper ECG digitizing and digital measuring process, examine the prevalence and prognostic significance of negative U-waves in the general population, study the effect of age and sex on the prognostic significance of ER, and develop a cumulative ECG risk score and assess its ability to identify high SCD risk subjects from the general population.
#$!!!"
%4(-%')/)'6423,;5-2/2+;%1()/)'642'%4(-2+4%3,;
%4(-%'%'6-21326)16-%/
The electrical activity of the heart depends on the spontaneous excitation of specific pacemaker cells and the propagation of these excitatory stimuli through the cardiac muscle cells, cardiomyocytes. Due to the unequal distribution of electrically charged ions across the membranes of cardiomyocytes and the presence of ion channels in the membrane, there is a membrane potential between the inside and outside of the cardiomyocyte. The opening and closing of these ion channels can generate an action potential, a rapid change in membrane potential caused by transient depolarization followed by repolarization, which can propagate and depolarize adjacent myocytes (Figure 1).
The cardiac action potential consists of 5 phases. In the resting phase (phase 4), the membrane potential is constant, approximately from -50 mV to -90 mV. This potential is maintained by the outward flow of K+ ions through the inwardly rectifying K+ channels (IK1) and the Na+/K+-ATPase, which pumps three Na+ ions out of the cell and two K+ ions into the cell and maintains the high intracellular K+ concentration (Rubart and Zipes, 2014).
When a spontaneous excitation or propagation of an excitatory stimuli increases the cardiomyocyte cell membrane potential sufficiently, it causes a rapid depolarization (phase 0) by opening voltage-dependent Na+ ion channels (INa) followed by the influx of Na+ ions. In addition, the inward flux of Ca2+ ion through L-type voltage-gated Ca2+ channels (ICa.L) plays a part in the rapid depolarization, especially in the pacemaker cells (Rubart and Zipes, 2014).
Following the rapid depolarization, the inactivation of the INa channels, followed by the activation of transient outward K+ currents (Ito1) and Ca2+- activated outward chloride currents (Ito2) makes the membrane potential slightly more negative, causing the early rapid repolarization (phase 1).
Furthermore, Na+/Ca2+ exchanger operating in reverse mode causes an outward movement Na+ ions, also contributing to the phase 1 (Rubart and Zipes, 2014).
A plateau (phase 2) follows the phase 1, in which the membrane potential remains almost constant for several hundred milliseconds due to summation of inward and outward currents. The outward current is maintained by outward K+ movement and inward Cl- movement, and the inward current is carried by the inward movement of Ca2+ ions. The inward movement of Ca2+
causes further Ca2+ release from the sarcoplasmic reticulum and this increased
Ca2+ concentration is finally responsible for the contraction of the cardiomyocyte. In addition, Na+/Ca2+ exchanger operating in forward mode causes inward movement of Na+ ions in exchange for Ca2+ ions (Rubart and Zipes, 2014).
The plateau ends with final rapid repolarization (phase 3), caused by inactivation of ICa.L and further activation of repolarizing K+ currents IKs and IKr and the inwardly rectifying K+ current IK1. The net effect repolarizes the membrane potential back to the negative resting potential, phase 4. Ion pumps then restore the ion concentrations to pre-action potential balance and pump intracellular Ca2+ out, ending the myocyte contraction (Rubart and Zipes, 2014).
In some parts of the heart, such as the sinoatrial node, portions of the atrioventricular node, and His-Purkinje fibers, the resting membrane potential does not remain constant in phase 4, but gradually starts to depolarize (phase 4 diastolic depolarization). This happens due to inward Na+ and K+ flow through the hyperpolarization-activated channels (If). Another possible cause for the diastolic depolarization could be a release of Ca2+ ions from sarcoplasmic reticulum that further activates Na+/Ca2+ exchanger, and thus depolarizes the membrane potential (Joung et al., 2009). If this spontaneous depolarization reaches the threshold potential, it initiates an action potential, that can then propagate to adjacent myocytes. Usually, the depolarization discharge rate of the sinoatrial node is the fastest of the automatic pacemaker sites and dictates the cardiac rhythm (Rubart and Zipes, 2014).
-+74) Cardiac action potential in the myocyte and the major ion currents responsible for it. IK1 = inward rectifier potassium, INa+/K+ -ATPase = sodium potassium ATPase pump, INa = voltage-dependent sodium, ICa.L = L-Type calcium, Ito1 = transient outward potassium, Ito2 = calcium activated chloride, INaCaex = sodium calcium exchanger, IKr = rapid delayed rectifier potassium, IKs = slow delayed rectifier potassium.
/)'6423,;5-2/2+;2*%44,;6,0-%5
Cardiac arrhythmias are defined as abnormalities in the heart rate and rhythm that are not physiologically justified. In many cases, arrhythmias are caused by disorders of the electrical conduction system of the heart. They can be broadly divided to be caused by impulse formation disorders, impulse conduction disorders, or combinations of both. Disorders of impulse formation include increased or reduced automaticity and triggered activity.
However, the exact electrophysiological mechanism responsible for many arrhythmias remains uncertain.
Automaticity can occur at an inadequate rate in the normal pacemaker, the sinoatrial node, or occur in ectopic subsidiary pacemakers sites (Rubart and Zipes, 2014). Reduced automaticity results in bradycardia and increased automaticity in tachycardia. Ectopic pacemaker sites are normally overdriven by suppression from a more rapid discharge rate of the sinoatrial node. If the sinoatrial discharge rate slows or a conduction block occurs between the sinoatrial node and the ectopic pacemaker site, the ectopic site can start to discharge at its typical rate. The ectopic pacemaker site can also discharge if enhanced automaticity occurs. Furthermore, abnormal automaticity can occur also in atrial and ventricular myocardial tissue or Purkinje fibers (Antzelevitch and Burashnikov, 2011; Rubart and Zipes, 2014).
Triggered activity occurs when an initial depolarization wave of an action potential triggers a following new depolarization in the myocardium, called an afterdepolarization. These afterdepolarizations can be divided into early afterdepolarizations (EADs), occurring before the initial action potential has fully repolarized in phase 2 or phase 3 of the action potential, and delayed afterdepolarizations (DADs), occurring after the repolarization in phase 4 of the action potential. The EADs in phase 2 are usually associated with prolongation of the action potential duration and are caused either by reactivation of ICa.L or by activation of the Na+/Ca2+ exchange current due to spontaneous calcium release from the sarcoplasmic reticulum (Tse, 2016). The EADs in phase 3 of the action potential are associated with shortening of the action potential durations and are caused by activation of the Na+/Ca2+
exchange current due to elevated intracellular calcium concentration (Tse, 2016). DADs are caused by high intracellular calcium concentration that activates the transient inward current, mostly through the Na+/Ca2+
exchanger, that depolarizes the membrane (Tse, 2016). Regardless of the mechanism, a sufficiently large membrane potential change caused by EADs or DADs can result in triggered activity, giving rise to extrasystole, which can cause further tachyarrhythmias. EADs are thought to play a role in the pathophysiology of polymorphic VT torsades de pointes associated with long QT, while DADs have been thought to be involved in catecholaminergic polymorphic ventricular tachycardia (Tse, 2016). In general, ventricular arrhythmias leading to cardiac arrest are presumed to be commonly caused by an interaction between an underlying heart disease (substrate) and a dynamic trigger that gives rise to the lethal arrhythmia (Montagnana et al., 2008).
Conduction disorders can cause bradyarrhythmias if the propagating impulse is blocked and tachyarrhythmias if the conduction block or delay results in re-entry. Re-entry occurs if the propagating impulse returns to its point of origin that has recovered from the depolarization and is able to re- excites the same path again. Re-entry can be classified as anatomical or functional. In anatomic re-entry, two anatomically separate pathways have differing electrophysiological properties, causing the initial depolarization wave to be blocked in the pathway with a longer refractory period. The depolarization conducts through the pathway with the shorter refractory period and is able to excite the other pathway downstream of the blocked site.
The depolarization then conducts in reverse direction through the pathway with the longer refractory period to the site of origin, exciting once again the starting point of the pathway with the shorter refractory period, causing the depolarization to conduct the same circular path again. Functional re-entry lacks anatomically defined pathways and in contrast, is caused by local electrophysiological heterogeneities in the myocardium. These tissue heterogeneities can be fixed due to chronic heart conditions or dynamic due to acute conditions. Compared to an anatomical re-entry, the core of a functional re-entry circuit is usually smaller and more unstable. Furthermore, the size and location of the functional re-entry may vary due to the absence of an anatomical component (Rubart and Zipes, 2014). A more sophisticated model of the functional re-entry has been also proposed, the spiral wave, with a core or a “rotor” in its center sending drifting spiral action potential waves outwards (Pandit and Jalife, 2013).
4-1'-3/)52*)/)'642'%4(-2+4%3,;
The electrocardiogram (ECG) is a recording of the electrical activity of the heart, produced by the movement of action potentials through the cardiac myocytes. A typical ECG is recorded with four electrodes on the extremities and six electrodes in standard locations on the chest, to construct 12 pairs of electrodes, called leads. These leads record the fluctuations in the heart’s voltage from different angles over time.
An ECG consists of waveforms that reflect the depolarizations and repolarizations of the cardiac muscle (Figure 2). Normally, an action potential originates spontaneously in the pacemaker cardiomyocyte cells in the sinoatrial node, located in the wall of the right atrium. The action potential propagates to adjacent cardiomyocytes and depolarizes the right and left atrium, reflected by the P-wave in the ECG. The action potential propagates subsequently to the atrioventricular node, the only electrical pathway from atria to ventricles in a normal heart, followed by the bundle of His. Thereafter, the action potential propagates to left and right bundle branches, with the main left bundle bifurcating into a left anterior fascicle and a left posterior fascicle. From the bundle branches, the action potential propagates to Purkinje fibers and finally depolarizes cardiomyocytes in the ventricles. This
ventricular depolarization is reflected by the QRS complex in the ECG. Finally, the ventricles repolarize, generating the T-wave in the ECG. The U-wave, the final waveform before a new cardiac cycle, can also be many times seen, which is hypothesized to be caused by mechanoelectrical forces (Eyer, 2015).
Although ECG was introduced over a century ago, due to its non- invasiveness, wide availability, and cost-effectiveness, it continues to be one of the most commonly performed cardiac tests in modern clinical practice (Kligfield et al., 2007). ECG still plays a central role in diagnosing arrhythmias, conduction disturbances, and ischemia, in addition to being used in patient monitoring and as a prognostic tool (Kligfield et al., 2007; Narayanan and Chugh, 2015).
-+74) A schematic illustration of an ECG.
-+-6-<%6-21%1((-+-6%/%1%/;5-52*)/)'642'%4(-2+4%3,-' 4)'24(-1+
Since the discovery of the ECG, the main ECG recording format was paper for a long time. The first analog-to-digital ECG signal conversion technique was introduced in the 1950s, followed by computerized ECG analysis methods (Hongo and Goldschlager, 2006). With advances in technology, the predominant ECG format has shifted from analog to digital and today ECGs are recorded mainly in digital format (Kligfield et al., 2007).
The most important advantages of digital ECG format are efficient storing, transmission, and retrieval capabilities, in addition to automated digital analysis and interpretation possibilities (Kligfield et al., 2007). Digital ECG format also enables more complex analyses of ECG waveforms and advanced computational techniques (Lyon et al., 2018; Tse and Yan, 2017). In clinical practice, the goals of automated digital analysis include decreased ECG analysis time, reduced cost, and assisting physicians at interpreting ECG recordings (Schläpfer and Wellens, 2017). Accordingly, computer-assisted ECG analysis has been demonstrated to reduce analysis time by 24–28%
(Hongo and Goldschlager, 2006). Furthermore, the computer produced ECG analyses are relatively accurate in general and some computer ECG interpretation programs are comparable to interpretations by cardiologists (Hongo and Goldschlager, 2006; Schläpfer and Wellens, 2017).
However, automated digital analyses have several limitations. Despite significant efforts, there are still no international standards for computerized ECGs, limiting the generalizability of the techniques (Hongo and Goldschlager, 2006). Furthermore, there is a concern about the inter-method difference between computer ECG analyses and manual ECG interpretations.
For example, computer analyzed global ECG intervals are frequently greater than manual measurements from single leads, especially in QT interval measurements (Schläpfer and Wellens, 2017). Moreover, significant differences up to 13 ms in QRS duration (De Pooter et al., 2017; Vancura et al., 2017) and 18 ms in QT interval (Poon et al., 2005) measurements have been demonstrated between automated ECG measurements from different manufacturers. As an example, QRS duration plays a central role in the assessment of cardiac resynchronization therapy candidates, and these inter- manufacturer differences can lead up to a 15% rate of misclassified candidates based on QRS duration (De Pooter et al., 2017). In addition, computerized ECG interpretations are prone to mistakes in rhythm diagnosis, especially with tracings with pacemaker rhythm (Poon et al., 2005). Automated ECG interpretations have also insufficient accuracy to detect ST-segment elevation myocardial infarction (MI), with up to 42% false-negative rate (O’Connor et al., 2015). Moreover, automated ECG interpretations cannot currently include patient history and clinical context to the interpretation, which could assist in the determination of the correct diagnosis (Smulyan, 2019). As a consequence of these limitations, systematic over-reading of digital ECG analyses by a physician has remained necessary in clinical practice.
Nevertheless, a large number of archived ECGs remain in only paper format. An effort has been made to convert these paper ECGs to digital format to enable efficient storing and more comprehensive retrospective digital ECG analysis. However, no single method or even an output format is currently in universal use (Waits and Soliman, 2016). In general, the manual-to-digital ECG conversion process includes ECG scanning, skew correction, gridline removal, generation of continuous digital ECG signal, and possible additional steps such as tracing algorithms and filtering (Waits and Soliman, 2016).
Different conversion techniques have utilized these methods with varying levels of automatization. However, challenges still remain in the areas of time efficiency, signal quality, signal distortions, and with poor quality ECGs and ECGs with excessive signal noise (Stockbridge, 2005; Waits and Soliman, 2016).
%4(-28%5'7/%4(-5)%5)0246%/-6;%1(57(()1 '%4(-%'()%6,
3-()0-2/2+;
Cardiovascular diseases are a major cause of death in the world, causing nearly one-third of all deaths, with over 17 million deaths due to cardiovascular diseases worldwide annually (Wong, 2014). Of these, approximately 7–8 million deaths annually, the majority of cardiovascular deaths, are caused by coronary heart disease (CHD) (Roth et al., 2015; Wong, 2014). The next most common causes of cardiovascular deaths are ischemic stroke with 3 million annual deaths, hemorrhagic stroke with likewise 3 million annual deaths, and hypertensive heart disease with 1 million annual deaths worldwide (Roth et al., 2015). There are significant regional differences in cardiovascular mortality trends, with the cardiovascular death burden currently shifting from high-income Western countries to low and middle-income countries in Asian and Middle-Eastern regions (Finegold et al., 2013; Roth et al., 2015; Wong, 2014).
Multiple risk factors for cardiovascular diseases have been identified.
These range from fixed and unmodifiable risk factors such as sex, family history, and age, to modifiable lifestyle risk factors (Wong, 2014). The prevalence of CHD increases markedly with age, from <1% in persons aged 20–39 years to 20–30% in persons ≥80 years (Benjamin et al., 2018). CHD is more prevalent in males than females of all ages (Benjamin et al., 2018).
Numerous modifiable lifestyle risk factors for CHD have been discovered and the presence of multiple risk factors has been shown to indicate a higher risk for CHD events compared to the presence of just a single risk factor. The generally accepted most important modifiable lifestyle risk factors for CHD include hypertension, cigarette smoking, diabetes, obesity, physical inactivity, unhealthy diet, and elevated cholesterol levels (Arnett et al., 2019; Wong, 2014). Furthermore, numerous other risk factors for CHD have been discovered, e.g. chronic kidney disease, chronic inflammatory disease, persistently elevated inflammatory markers, and periodontal disease (Helfand et al., 2009; Lin et al., 2018)
Although the absolute number of cardiovascular deaths globally have increased over 40% between 1990 and 2013, this increase has been mainly caused by population aging and population growth (Roth et al., 2015). In contrast, at the same time, the cardiovascular deaths attributed to epidemiological changes have decreased by approximately 40% (Roth et al., 2015). This trend highlights the importance of health policies, in addition to
preventive medical treatments and invasive procedures in cardiovascular disease prevention (O’Flaherty et al., 2013).
Finland had one of the highest cardiovascular mortality rates in the world in the 1960s, with the CHD mortality rate being particularly high in the eastern parts of Finland (Jousilahti et al., 2016; Thom and Epstein, 1994). However, the cardiovascular mortality rates have been declining in Finland since the 1970s, mainly due to improved primary and secondary prevention (Pyörälä et al., 1985; Salomaa et al., 1996; Vartiainen et al., 1994). Consequently, at the beginning of the 21st century, Finland’s age-adjusted CHD mortality rates were roughly similar to other high-income countries (Finegold et al., 2013). In 2016, 11 000 persons died due to CHD in Finland, resulting in an incidence of 197 per 100 000 person-years (World Health Organization, 2018).
Sudden cardiac arrest (SCA) is defined as non-traumatic unexpected circulatory arrest occurring within one hour of onset of acute symptoms in a person with or without a known cardiac disease, with no obvious extracardiac cause, that will lead to sudden cardiac death (SCD) if the circulation will not restore, with our without attempt (Priori et al., 2015). SCD is a major cause of mortality in the world, accounting for approximately 25–50% of cardiovascular deaths (Al-Khatib et al., 2018; Priori et al., 2015), and 5–20%
of all deaths (Chugh et al., 2008; Hayashi et al., 2015).
Due to the varying definitions, data sources, case ascertainment, reporting and data extrapolation, the exact epidemiological data on SCA and SCD remains limited. Although the use of uniform SCD definitions has been recommended, in one study as much as 40% of deaths that met World Health Organization criteria for SCD were defined non-cardiac deaths after a postmortem study (Tseng et al., 2018). These limitations being considered, the annual incidence of SCD in the United States was estimated to be from 180 000 to >450 000 in a relatively recent systematic review (Kong et al., 2011).
Correspondingly, in prospective studies using standardized definitions, the SCD incidences have ranged 40–100 per 100 000 person-years globally (Hayashi et al., 2015). In Finland, no precise data of SCD incidence have been recently published. In a study based on SCD victims in the Province of Oulu, Northern Finland, the annual SCD incidence was estimated to be 56.9 per 100 000 persons (Hookana et al., 2011).
Similar to with CHD, the incidence of SCD increases with age. The annual incidence of SCD is around 100 per 100 000 persons in subjects in their fifties, whereas the annual incidence increases to over 1000 per 100 000 persons in subjects ≥80 years (Niemeijer et al., 2015). Among individuals aged <35 years, SCD is rare, with an annual rate of 1–3 per 100 000 persons in recent studies (Hayashi et al., 2015). Concerning sex and race, men are at higher risk than women (Iwami et al., 2003; Niemeijer et al., 2015), and black persons seem to
be at higher risk than nonblack persons (Okin et al., 2012). During the recent decades, the SCD incidence seems to be decreasing, possibly resulting from better primary prevention, diagnosis, and treatment of heart diseases (Niemeijer et al., 2015; Shen et al., 2017).
The vast majority of SCA victims have an underlying cardiac condition. The underlying cardiac disease may be previously diagnosed, or it may have remained undetected until the SCA. Accordingly, the presence of clinically recognized cardiac disease increases the risk of SCA to sevenfold compared to subjects without heart disease diagnoses (Rea et al., 2004).
Among adults, CHD is the most common cardiac condition underlying SCD. CHD is generally considered to be the underlying cause in up to 80% of SCDs (Myerburg and Junttila, 2012), although in recent autopsy studies, the proportion seems to be lower and decreasing (Junttila et al., 2016; Tseng et al., 2018). SCD accounts for approximately 50% of CHD deaths, and the proportion of CHD deaths that are sudden versus non-sudden has remained constant (Myerburg and Junttila, 2012). In up to 30–50% of SCDs, it is the person’s first clinical manifestation of the underlying cardiac disease (Hayashi et al., 2015; Myerburg, 2001). Furthermore, a large portion of SCDs caused by CHD occur to subjects with diagnosed CHD but who are estimated to have a low SCD risk based on current risk stratification methods, i.e. do not have characteristics associated with high SCD risk (Myerburg, 2001). CHD as an underlying cause for SCD is more common in men than in women (Chugh et al., 2009b; Haukilahti et al., 2019).
Other significant underlying causes for SCD besides CHD are heart failure, valvular heart disease, coronary artery anomalies, myocarditis, primary ion channelopathies, and cardiomyopathies, such as dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy (Hayashi et al., 2015). Among young adults and children, these other causes are more prevalent and respectively the proportion of SCD caused by CHD is smaller (Ackerman et al., 2016; Bagnall et al., 2016). In approximately 5% of SCA victims, no significant cardiac abnormality is found (Hayashi et al., 2015).
However, more than of half subjects who experience an SCD do not have previously diagnosed cardiac disease with SCD being the first clinical manifestation of the underlying condition, or have an established CHD but estimated to have a low risk for SCD (Hayashi et al., 2015; Myerburg, 2001).
Many of the subjects without diagnosed cardiac disease nevertheless have underlying undetected cardiac condition, for example, silent MI has been detected in over 40% of SCD victims without a clinical history of CHD (Vähätalo et al., 2019). However, because this subgroup of individuals without apparent heart disease accounts for the majority of the population, the absolute risk of SCD for these individuals is estimated to be very low using the
current risk stratification tools. This is the root cause of the challenge of SCD prevention in the general population. With the identification of better risk markers, the general population individuals with a high SCD risk could be identified from the rest of the population and ultimately their prognosis could be improved.
)',%1-505
The primary electrical mechanisms accounting for SCAs are ventricular arrhythmias (ventricular fibrillation [VF] and ventricular tachycardia [VT]) that are too fast or chaotic to produce cardiac output, pulseless electrical activity (PEA) with seemingly adequate organized electrical rhythm of the heart without a sufficient cardiac output to produce perfusion for some reason, and asystole with complete absence of electrical and mechanical activity in the heart. The mechanism and electrophysiological events vary depending on the cause of the SCA accordingly. However, due to the abrupt nature of the SCA and the fact that the majority of the SCAs occur out-of-hospital without monitoring, there is limited knowledge about the exact electrophysiological cascade occurring in the heart preceding and during the SCA. In recent reports of out-of-hospital SCAs, the first recorded rhythm has been VT or VF in 20–
55%, asystole in 30–45%, and PEA in 15–30% of cases (Cobb et al., 2002;
Kauppila et al., 2018; Mader et al., 2012; Nichol et al., 2008).
The presumed common mechanism behind SCA caused by ventricular arrhythmias is an underlying electrical instability making the heart vulnerable to the fatal arrhythmia, followed by an acute dynamic trigger that precipitates the arrhythmia, and, finally, an electrophysiological event that initiates either VT that then degenerates to VF, or directly VF (Huikuri et al., 2001;
Montagnana et al., 2008). Subsequently, without restoration of the circulation, the VF will eventually degenerate to asystole. The electrophysiological event that initiates fatal arrhythmia can be, for example, a premature ventricular contraction (PVC) occurring on the repolarization phase of the preceding beat (the “R-on-T” phenomenon) (Bayés de Luna et al., 1989). In most of the SCA cases due to VT or VF, the sustained fatal ventricular arrhythmia is preceded by an increase in premature ventricular complexes or non-sustained VT runs (Bayés de Luna et al., 1989).
In the recent decades, the proportion of VT or VF as the initial rhythm of SCA has been declining and, as a result, the proportion of subjects with non- shockable rhythms, PEA or asystole, as the initial rhythm has been increasing (Agarwal et al., 2009; Cobb et al., 2002; Herlitz et al., 2000; Myerburg et al., 2013). This decline in the proportion of VTs and VFs in SCAs has been hypothesized to result from overall reduced CHD mortality due to improved medical therapy, including increased usage of beta-blockers (Cobb et al., 2002; Narayan et al., 2019). Furthermore, although asystole is the first rhythm observed in many unmonitored subjects without exact time of onset, the original rhythm causing the cardiac arrest may have been VT or VF, that have
lost all electrical activity due to increasing hypoxia, acidosis and myocardial tissue death (Narayan et al., 2019). In contrast to VT and VF, only approximately 50% of PEAs may be classified as primary cardiac events, with the rest being caused for example by profound hypovolemia, trauma or obstruction of the circulation (Myerburg et al., 2013). Although non-ischemic cardiac conditions are associated with non-shockable primary rhythm, up to 30% of resuscitated out-of-hospital SCAs due to PEA or asystole have ST- segment elevations in the ECG following the resuscitation and undergo immediate coronary intervention (Dumas et al., 2010; Kauppila et al., 2018).
The overall out-of-hospital survival rate for cardiac arrest is only 10%, with subjects with VF or VT as the initial rhythm having a better survival rate compared to subjects with PEA or asystole as the initial rhythm (Al-Khatib et al., 2018; Myerburg et al., 2013). However, there seems to be an improving trend in the survival rate in recent decades (Daya et al., 2015). Women have lower overall out-of-hospital cardiac arrest survival rates compared to men, likely explained by a lower rate of shockable initial rhythms (Blom et al., 2019).
In many cases, typical warning symptoms, such as angina pectoris and dyspnea, immediately precedes the SCA (Marijon et al., 2016; Müller et al., 2006). Only a minority, approximately one-fifth, report these warning symptoms to emergency medical services, although this reporting is associated with an increased survival rate (Marijon et al., 2016). Furthermore, only in a minority of SCAs bystanders perform resuscitations attempts, although this is also associated with a better survival rate (Müller et al., 2006).
Generally accepted dynamic triggers of SCD include pH and electrolyte imbalances, inflammation, and ion channel abnormalities (Montagnana et al., 2008). Various drugs, including antiarrhythmic drugs, have been also associated with cardiac rhythm disturbances and can trigger SCD (Montagnana et al., 2008).
SCDs occur more commonly in the morning hours (Arntz et al., 2000;
Cohen et al., 1997), on Mondays (Arntz et al., 2000; Peckova et al., 1999), and during winter (Arntz et al., 2000; Peckova et al., 1999), suggesting that circadian and endogenous rhythm, in addition to external factors play a role in vulnerability to SCD. Recent major life events have been also shown to associate with SCA (Wicks et al., 2012).
Although regular exercise reduces the long-term risk of SCD, vigorous exercise increases the short-term SCD risk during or shortly after the exercise (Narayanan et al., 2017). Approximately 5% of all SCDs occur during sports activities, with male dominance of over 90% (Narayanan et al., 2017).
Furthermore, although attention in the media has been drawn to young competitive athletes, more than 90% of sports-related SCDs occur in the context of recreational sports (Marijon et al., 2011).
The SCD can occur generally in 3 different settings in subjects with CHD: due to an acute MI or after it, due to a coronary ischemia without acute MI, or due to secondary changes in myocardium, such as fibrosis or scar, caused by prior MI or ischemia (Hayashi et al., 2015). The mechanism of SCD also seems to differ regarding the predisposing setting in CHD patients.
Acute MI seems to be a common trigger for SCA in CHD patients. Acute coronary thrombus or active coronary lesion is present in 20–70% of SCD victim autopsies and approximately 50% of out-of-hospital SCA victims with VF as the initial rhythm have evidence of acute MI (Al-Khatib et al., 2018;
Hayashi et al., 2015). On the other hand, 5–10% of subjects with acute MI will have VF or sustained VT before hospital arrival and another 5% after hospital admission (Al-Khatib et al., 2018). In acute ischemia, with or without MI, the deficient coronary perfusion, and the possible following reperfusion, causes variations in regional myocardial cell membrane electrophysiology, initiating the malignant arrhythmia through triggered activity or reentry (Myerburg and Junttila, 2012). For example, the heterogeneities in electrophysiological properties of infarction and peri-infarction regions may serve as substrates for the reentry (Myerburg and Junttila, 2012). Moreover, increased oxidative stress in addition to abnormalities in myocardial cell metabolism and ionic homeostasis are common in ischemia and play a role in arrhythmogenesis (Yang et al., 2015).
The substrates for SCD in chronic CHD patients develop over time. Chronic ischemia or MI can result in a scar formation and remodeling of the myocardium. Subsequently, ventricular arrhythmias can originate from re- entry circuits in the regions with scar tissue. Furthermore, myocardial remodeling can result in heart failure due to ventricular dilatation, neurohumoral abnormalities, vasculopathy, and fibrosis formation (Bunch et al., 2007). The gradual myocardium remodeling and fibrosis formation lead to slow but progressive deterioration, with possible abrupt cardiac arrest as the final manifestation. An ischemic event or other dynamic sudden changes may act as the final trigger for the SCD, although in many cases, no acute precipitating trigger can be identified. The mechanism of SCD is tachyarrhythmia in many of these patients, although some are caused by acute mechanical failure due to PEA or asystole (Packer, 2019).
7(()1'%4(-%'()%6,4-5.*%'6245
Prior SCA survivors are at the highest risk for new SCA. They have a 1-year VT or VF event rate of near 20%, and current guidelines recommend secondary preventive ICD implantation to these subjects in the absence of reversible SCA causes (Al-Khatib et al., 2018; Narayan et al., 2019; Priori et al., 2015).
Reversible and correctable causes of SCAs are a heterogeneous group, including acute ischemia, electrolyte and metabolic disturbances, and initiation of new antiarrhythmic drugs. Approximately half of SCAs are due to reversible causes (Ladejobi et al., 2018).
Subjects with diagnosed severe heart disease and features increasing the arrhythmia risk are at the second-highest risk of SCA and SCD. In clinical practice, this patient group is currently mainly identified by reduced LVEF.
Consequently, current guidelines recommend ICD therapy in primary SCD prevention in general only for subjects with reduced LVEF due to ischemic or non-ischemic heart disease (Al-Khatib et al., 2018; Priori et al., 2015).
However, this subgroup of subjects accounts for only a minor part of the absolute number of SCD events, as only 20–30% of SCA and SCD victims have markedly reduced LVEF and meet the criterion for ICD implantation (Gorgels et al., 2003; Stecker et al., 2006).
As CHD accounts for majority of the SCD cases in the general population, consequently classical risk factors for CHD, such as hypertension, smoking, diabetes, obesity, and hypercholesterolemia, are also associated with an increased risk of SCD (Albert et al., 2003; Jouven et al., 1999; Wannamethee et al., 1995). Furthermore, there is evidence that improvement in these factors can also reduce the risk of SCD in general population subjects (Rahimi et al., 2012; Sandhu et al., 2012).
A family history of SCD also increases the risk of SCD (Dekker et al., 2006;
Jouven et al., 1999). Moreover, genome-wide association studies have also identified genomic loci that affect the SCD risk in the general population (Marsman et al., 2014). Although these findings have limitations due to lack of replication efforts and inconsistency across studies, these methods can hopefully reveal the genetic basis of SCD in new patient populations and ultimately improve SCD risk stratification (Bezzina et al., 2015; Marsman et al., 2014).
Several lifestyle features have been associated with a lower SCD risk, such as physical activity (Wannamethee et al., 1995; Whang et al., 2006), eating fish regularly (Albert et al., 1998), having higher magnesium intake (Chiuve et al., 2011), and Mediterranean-style diet (Bertoia et al., 2014). The association between alcohol intake and SCD risk seems to be U-shaped, with moderate intake of up to 1 drink a day associated with reduced SCD risk (Albert et al., 1999; Chiuve et al., 2010), whereas heavy drinking is associated with an increased sudden death risk (Dyer et al., 1977; Wannamethee and Shaper, 1992). Interestingly, almost 40% of SCD victims in Northern Finland with underlying CHD have been shown to have a positive blood alcohol test in autopsy (Perkiömäki et al., 2016).
Left ventricular hypertrophy (LVH) has been linked to an increased SCD risk in several studies (Haider et al., 1998; Konety et al., 2016; Laukkanen et
al., 2014b). Furthermore, echocardiographic markers, such as reduced LVEF, mitral annular calcification, and diastolic dysfunction markers, have been shown to associate with an increased risk of SCD in the general population (Konety et al., 2016).
Moreover, some studies have also suggested that chronic kidney disease (Deo et al., 2010), obstructive sleep apnea (Gami et al., 2013), epilepsy (Bardai et al., 2015), elevated C-reactive protein levels (Albert et al., 2002), and psychiatric disorders (Albert. et al., 2005; Koponen et al., 2008; Shi et al., 2017) associate with an increased SCD risk.
Although numerous SCD factors for general population subjects have been identified, it is unlikely that any single risk factor will in isolation have significant utility in clinical use. Consequently, more focus has been recently put to estimating general population subjects’ SCD risk using a combination of multiple previously discovered risk factors. For example, a combination of 12 independent risk factors was demonstrated to provide accurate SCD risk prediction in two large general population cohorts without cardiovascular diseases (Deo et al., 2016).
Prior MI increases the SCD risk in CHD patients, with the risk being highest after the MI and then gradually decreasing over time (Hayashi et al., 2015).
Ventricular arrhythmias occurring within 48 hours after the infarction have been assumed not to predict long term risk of SCA, although they seem to predict in-hospital, 30-day and 1-year mortality (Al-Khatib et al., 2003; Askari et al., 2009; Mont et al., 1996). However, early ICD implantation after an MI does not improve survival and is not recommended in the current guidelines (Elayi et al., 2017). The risk of SCA remains more markedly elevated for a few months after MI, then decreasing to a more stable chronic risk (Elayi et al., 2017). In the modern era, the overall annual SCD incidence in MI survivors is low, only around 1%, due to improved revascularization and medical therapy possibilities (Adabag et al., 2008; Mäkikallio et al., 2006)
The strongest risk factor for SCD in patients with prior MI is reduced left ventricular ejection fraction (LVEF) (Deyell et al., 2015). Approximately 10%
of subjects with LVEF <30% will suffer SCD in the following 2 years after MI (Greenberg et al., 2004; Solomon et al., 2005). The overall myocardial scar burden can be somewhat estimated with LVEF, with reduced LVEF accounting for more scar tissue (Deyell et al., 2015). Furthermore, reduced LVEF can cause autonomic abnormalities and electrophysiological arrhythmia vulnerability, which can also play a role in the increased SCD risk (Deyell et al., 2015). Currently, ICD therapy is recommended as primary prevention in subjects with prior MI and reduced LVEF (Al-Khatib et al., 2018; Priori et al., 2015). However, LVEF has major limitations as a risk marker. Female SCD victims are less likely to have severely reduced LVEF compared to male SCD
victims, and therefore fewer women may be eligible for ICD based on current guidelines (Chugh et al., 2009b). Furthermore, some subgroups of CHD patients with reduced LVEF do not benefit from ICD (Goldenberg et al., 2008).
Moreover, LVEF is not specific enough alone to predict the mode of death to be SCD from non-sudden cardiac death (non-SCD) (Buxton, 2005). This has led to the search for other factors that could identify subjects at the highest SCD risk among subjects with reduced LVEF (Buxton et al., 2007; Goldenberg et al., 2008; Narayan et al., 2019).
In addition to LVEF, several other risk markers for SCD in CHD patients have been identified (Buxton, 2009). Many of the risk factors for CHD are associated with an increased SCD risk in the general population but may not associate with SCD risk independently anymore among patients with established CHD (Kannel et al., 1987). However, smoking, physical inactivity, and diabetes have been independently associated with an increased SCD risk in CHD patients (Deo et al., 2011; Goldenberg et al., 2003; Junttila et al., 2018, 2010). Various electrocardiographic risk markers have been identified (Wellens et al., 2014). Inducible VT in an electrophysiological study has been associated with SCD and it could possibly help to identify subjects at high arrhythmic SCA risk in some patient populations (Al-Khatib et al., 2018;
Buxton, 2009; Priori et al., 2015). LVH associates with SCD risk in CHD patients (Aro et al., 2017b; Reinier et al., 2011; Turakhia et al., 2008).
Furthermore, concentric remodeling of the left ventricle without hypertrophy seems to also associate with an increased SCD risk (Aro et al., 2017b).
Myocardial fibrosis and scars are arrhythmogenic and identification of these tissue characteristics with the cardiac magnetic resonance imaging with late gadolinium enhancement has been shown to improve SCD risk stratification in both CHD subjects with reduced and normal LVEF (Wu, 2017).
Abnormalities in autonomic nervous system parameters, such as heart rate turbulence, T-wave alternans, heart rate variability, baroreflex sensitivity, and periodic repolarization dynamics have been also linked with an increased SCD risk (Bauer et al., 2019; Farrell et al., 1992; Fukuda et al., 2015; Kiviniemi et al., 2007).
42+1256-'8%/7)2*)/)'642'%4(-2+4%3,-'0%4.)45-1 6,)57(()1'%4(-%'()%6,%1('%4(-%')8)164-5.
564%6-*-'%6-21
)%464,;6,0
Heart rate is probably the simplest parameter assessable from the ECG.
Numerous nonmodifiable and modifiable factors affect the heart rate in a complex way, with the autonomic nervous system playing a major role (Valentini and Parati, 2009). A high resting heart rate has been shown in multiple studies to be a predictor of increased cardiac and all-cause mortality
(Fox et al., 2007). An elevated resting heart rate has been also linked to sudden death in male general population subjects, with the risk increasing linearly with the level of resting heart rate (Jouven et al., 2001; Kannel et al., 1987). An elevated resting heart rate has been also linked to an increased SCD risk in CHD patients, and this association is independent of reduced LVEF and heart rate modulating drugs (Teodorescu et al., 2013).
Atrial fibrillation, the most common sustained arrhythmia, has been associated with SCD in several studies, although whether this association is independent of other confounding factors is uncertain (Chen et al., 2013;
Reinier et al., 2014). Possible mechanisms for the association include electrical remodeling of the myocardium, myocardial ischemia, shortening of the action potential duration, ventricular refractory period shortening, irregular ventricular excitation, and short-long-short sequences (Rattanawong et al., 2018). In a recent meta-analysis, atrial fibrillation was associated with SCD in the general population, in subgroups of CHD patients, heart failure patients, and other subgroups with cardiac conditions (Rattanawong et al., 2018).
Premature atrial complexes (PACs) and PVCs are common ECG findings.
At least one PAC during 24-hour rhythm monitoring is observed in 99% of general population subjects (Conen et al., 2012). PACs have been considered a benign phenomenon, but more recently a large number of PACs have been associated with mortality, atrial fibrillation, stroke and cardiac death (Himmelreich et al., 2019; Lin et al., 2015). Whether PACs associate with SCD has remained uncertain, as studies have had conflicting findings (Cheriyath et al., 2011; Lin et al., 2015). PVCs are also common in general population subjects, occurring in 40-75% of subjects in 24–48h rhythm monitoring (Ng, 2006) and up to 6% of subjects during 2-minute ECG recording (Ataklte et al., 2013). They are especially prevalent in subjects with cardiac diseases, with 60–
80% of CHD patients having premature ventricular complexes in 24-hour rhythm monitoring after MI (Bastiaenen et al., 2012). In CHD patients, PVCs have been associated with cardiac mortality and SCD (Bastiaenen et al., 2012).
The risk seems to increase with increasing PVC frequency and PVCs with multiple morphologies (Bastiaenen et al., 2012). In the general population, studies have had conflicting results of the prognosis associated with PVCs (Bastiaenen et al., 2012). A meta-analysis examining the prognosis associated with PVCs in subjects with normal hearts showed an increase in risk for cardiovascular endpoints (Lee et al., 2012). In another meta-analysis that used some of the same studies used in the meta-analysis by Lee and colleagues, PVCs in the general population subjects were associated with an increased SCD and cardiac death risk (Ataklte et al., 2013). A possible pathophysiological mechanism for this association could be that premature ventricular complexes act as triggers for malignant cardiac arrhythmias. However, the suggested association between PVCs and an increased risk for adverse events in general population subjects with presumed normal hearts could also result from, including subjects with undetected underlying cardiac conditions to the
