PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Circulation. Author manuscript; available in PMC 2011 January 5.
Published in final edited form as:
PMCID: PMC3016224
NIHMSID: NIHMS258535

Sudden Cardiac Death Prediction and Prevention Report From a National Heart, Lung, and Blood Institute and Heart Rhythm Society Workshop

Despite the significant decline in coronary artery disease (CAD) mortality in the second half of the 20th century,1 sudden cardiac death (SCD) continues to claim 250 000 to 300 000 US lives annually.2 In North America and Europe the annual incidence of SCD ranges between 50 to 100 per 100 000 in the general population.36 Because of the absence of emergency medical response systems in most world regions, worldwide estimates are currently not available.7 However, even in the presence of advanced first responder systems for resuscitation of out-of-hospital cardiac arrest, the overall survival rate in a recent North American analysis was 4.6%.8 SCD can manifest as ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electric activity (PEA), or asystole. In a significant proportion of patients, SCD can present without warning or a recognized triggering mechanism. The mean age of those affected is in the mid 60s, and at least 40% of patients will suffer SCD before the age of 65.4 Consequently, enhancement of methodologies for prediction and prevention of SCD acquires a unique and critical importance for management of this significant public health issue.

Prediction and prevention of SCD is an area of active investigation, but considerable challenges persist that limit the efficacy and cost-effectiveness of available methodologies.7,9,10 It was recognized early on that optimization of SCD risk stratification will require integration of multi-disciplinary efforts at the bench and bedside, with studies in the general population.1113 This integration has yet to be effectively accomplished. There is also increasing awareness that more investigation needs to be directed toward identification of early predictors of SCD.14 Significant advancements have occurred for risk prediction in the inherited channelopathies1517 and other inherited conditions that predispose to SCD, such as hypertrophic cardiomyopathy,18 but there is much to be accomplished in this regard for the more common complex phenotypes, such as SCD, among patients with CAD. Many cardiovascular treatments (eg, lipid lowering and antihypertensive agents, antiischemic interventions, and heart failure therapies) prevent or delay the progression of the cardiovascular diseases that are the most frequent cause of SCD. However, the current workshop focused specifically on risk prediction for arrhythmic death in cardiac populations rather than on the broader topics of prediction and prevention of cardiac diseases in general.

Unfortunately, specific pharmacological therapies directed at the electrophysiological substrate and mechanisms that cause arrhythmias have proven disappointing when applied to high or moderate risk patients without prior documented clinical arrhythmias. The implantable cardioverter-defibrillator (ICD) in combination with heart failure drug therapy remains the mainstay of SCD prevention19,20 but is likely to benefit only the small population at high risk who can be identified before an SCD event.5,21

On September 29 to 30, 2009, a working group of experts was jointly convened by the National Heart, Lung and Blood Institute and the Heart Rhythm Society to address and recommend research directions and strategies in prediction and prevention of SCD, for consideration by the National Heart, Lung, and Blood Institute and the greater research community. The panel was asked to consider the 3 broad areas of bench, clinical and population sciences. After deliberation on available information as well as critical needs for SCD prediction and prevention, the group came to a consensus, identifying investigational gaps and developing research recommendations in the 6 high-priority areas discussed below. The 6 recommendations are summarized in the Table, and detailed background information on each of the 6 recommendations is provided in this document. The Workshop’s Executive Summary can be found at http://www.nhlbi.nih.gov/meetings/workshops/.

Table
Summary of Specific Recommendation for the Prediction and Prevention of SCD

Recommendation 1: Facilitate Study of Well-Phenotyped SCD and Control Populations, Including Understudied Subgroups

Background

Like most complex traits, there are aspects of the SCD phenotype that present unique challenges and these, in turn, dictate the investigative approach. Because of the sudden, unexpected, and dynamic nature of the event, the vast majority of sudden cardiac arrests occur in the community and at least 90% to 95% of these individuals do not survive despite resuscitation attempts performed in the field by emergency medical response systems.7,8 In 40% to 50% of cases, SCD is unheralded by symptoms and in 30% to 40% can be unwitnessed.5,7 It stands to reason that the ascertainment of the phenotype of individuals at risk of SCD must occur in the community, as opposed to the hospital and healthcare system. Therefore population-based approaches must be used.7 In fact, there is now clear evidence that retrospective death-certificate methods of ascertainment are inaccurate, with unacceptably low positive predictive values for determination of the SCD phenotype when compared to prospective approaches.4,22 Information on the burden of SCD is available for only selected world regions, limited largely to North America and Europe, with virtually no information available on SCD epidemiology in the vast majority of the world.7 There is also a paucity of data on the epidemiology, risk factors, prognosis, and temporal trends for SCD in nonwhite ethnic and racial groups.

SCD is generally defined as a sudden and unexpected pulseless event, but noncardiac conditions need to be excluded before the occurrence of a primary cardiac event can be confirmed.7,23 Because of these complexities, multiple definitions have been employed. Studies assessing risk predictors of SCD have been performed in community-based cohorts24,25 and there is increasing recognition that prospective studies of SCD in the general population are also feasible.4,5,21,2628 These studies have shown that definitions can be standardized and systematic circumstantial and clinical evidence can be obtained and utilized to maximize the accuracy of identifying the SCD phenotype.

Building on the available literature4,7,24,25,29,30 and incorporating definitions that have been employed previously, this working group has developed a unified definition for SCD that can be used to ascertain the SCD phenotype in community-based cohort studies as well as investigations conducted in the general population. A case of established SCD is an unexpected death without obvious extracardiac cause, occurring with a rapid witnessed collapse, or if unwitnessed, occurring within 1 hour after the onset of symptoms. A probable SCD is an unexpected death without obvious extracardiac cause that occurred within the previous 24 hours. In any situation, the death should not occur in the setting of a prior terminal condition, such as a malignancy that is not in remission or end-stage chronic obstructive lung disease. The term “sudden cardiac arrest” should be used to describe SCD cases in which specific resuscitation records are available or the individual has survived the cardiac arrest event.

There is also strong evidence from studies in North America and Europe that there are significantly altered trends in the presenting arrhythmia observed by first responders among SCD cases.31,32 The prevalence of SCD cases presenting with VF is decreasing with a corresponding increase in the proportion of cases presenting with PEA. Given the extremes of resuscitation outcome based on presenting arrhythmia (>25% survival for VF and <2% for PEA4), it is important to improve our understanding of the determinants of these altered trends. Because population-based investigative approaches for SCD are pivotal for understanding the phenotype, there is a need for greater numbers of subjects that are available for investigation. An annual incidence of SCD in the range of 60 to 90/100 000 individuals4,5,7 necessitates the establishment of large community-based studies that ideally connect with other similar efforts, forming consortia that can share data, analyses and resources for common objectives such as refining methods to predict SCD.

Knowledge Gaps

  • For the vast majority of world regions, there is virtually no available information on epidemiology of SCD.
  • There is a critical need for large population-based studies that include women and understudied minorities in different regions of the US,
  • There is a lack of infrastructure to facilitate collaborative links between different population-based studies.
  • There is a need to improve our understanding of altered trends in the arrhythmias precipitating SCD (ie, significant changes in the prevalence of VF and PEA).

Specific Recommendations

  • Facilitate the initiation and maintenance of large population-based studies of SCD to improve understanding of SCD mechanisms across gender and all racial/ethnic groups.
  • Provide the infrastructure to connect individual population-based studies as consortia that can collaborate for a common set of objectives.
  • Perform studies that will further the understanding of presenting arrhythmias (ie, VF, PEA, asystole, and the mechanistic differences between these conditions).

Recommendation 2: Develop and Validate a SCD Risk Score Utilizing Phenotypic, Biological, and Noninvasive Markers

Background

Numerous invasive and noninvasive techniques have been developed over the years to identify patients at risk for SCD.3335 Currently, assessment of left ventricular (LV) ejection fraction is commonly used to guide primary prevention of SCD,20 but there is considerable interest in using markers that reflect arrhythmia substrates more directly, and therefore enrich the prediction of SCD events. Invasive electrophysiological testing using programmed cardiac stimulation adds considerable specificity to identification of patient populations with ischemic heart disease who are at risk for SCD.36 and who, therefore, may benefit from ICD therapy.37,38 However, there remain concerns as to whether electrophysiological testing possesses sufficient sensitivity to reliably exclude SCD risk in patients with a negative test.39

In contrast to invasive electrophysiological testing, noninvasive tests for predicting SCD are clearly more attractive in a clinical strategy for widespread screening. Numerous markers derived mainly from surface ECG have been correlated with SCD, cardiac, and total mortality over the past 3 decades. These can be classified as (1) indices of abnormal autonomic modulation of cardiovascular function such as heart rate variability,40 heart rate turbulence,41 heart rate recovery from exercise,42 and baroreflex sensitivity43; (2) indices of abnormal impulse conduction such as signal averaged ECG44 and QRS fractionation45; and (3) indices of abnormal repolarization such as microvolt T wave alternans,46 QT interval dynamicity,47,48 and various measures of T wave morphology and dispersion. Most of the autonomic markers have been correlated with total rather than arrhythmic mortality. Although extensive comparative data are not available, when examined in the same population with other risk markers T wave alternans appear to predict SCD-related events with greatest negative predictive value4951, suggesting that a patient with systolic dysfunction and a negative T wave alternans test may be at comparatively low risk for events. However, other recently published data from 2 large clinical trials of the prophylactic ICD indicate that the use of T wave alternans is likely to be limited by low predictive ability, higher number of indeterminate tests, and concern about incremental value over known risk factors.52,53 Taken together, the available experience suggests that multiple risk markers used in combination may provide a more robust prediction of events, which is not surprising when one considers the complexity and diversity of electro-anatomic substrates that underlie SCD. To date, no randomized clinical trials have been conducted that demonstrate benefit of non-invasive risk stratification in reducing SCD events. That being said, there are extensive observational data suggesting that various ECG risk markers used alone or in combination can be useful in identifying subsets of patients who are more or less likely to benefit from ICD therapy to prevent SCD. It is important to emphasize that few studies have attempted to account for dynamic time-varying changes in SCD risk but rather tend to measure a risk marker at only 1 point in time to predict SCD risks indefinitely. Although premature ventricular beat frequency measured by ambulatory Holter monitoring has been associated with enhanced risk for SCD,54 ectopic beats are so highly variable from day to day that it cannot be used as a reliable method for tracking SCD risk. Clearly, any viable strategy for predicting and preventing SCD will require tools for serial assessment of risk markers over time.

The aforementioned risk stratification and prevention efforts have been directed toward high risk subsets of patients with LV systolic dysfunction.55 However, the overwhelming majority of SCDs occurs in the general population,4,30,56 and approximately 55% of men and at least 68% of women have no clinically recognized heart disease prior to SCD5,24,28,30 A community-based study has recently drawn attention to the phenomenon of gender-specific risk factors.57 Women have a significantly lower prevalence of phenotypic traits that increase SCD risk, with half the likelihood of severe LV dysfunction (odds ratio 0.51, 95% confidence interval 0.31 to 0.84) and a 3-fold lower prevalence of established CAD (odds ratio 0.34, 95% confidence interval 0.20 to 0.60) compared to men. Although CAD continues to be observed in the majority of SCDs at autopsy,58 many individuals are not diagnosed with CAD prior to death.28,58 Even for those in whom CAD is recognized, there is only 1 major established clinical risk predictor: severe LV systolic dysfunction defined as a substantial decrease in the LV ejection fraction.7 Therefore, patients with LV ejection fraction of less than 30% to 35% are deemed to be high risk and qualify as candidates for primary prevention using the ICD.9 Recent studies confirm that ejection fraction alone is unlikely to be sufficient for effective SCD risk prediction, because it lacks both sensitivity and specificity. In the community, less than a third of all SCD cases have severely decreased LV ejection fraction that would have qualified them as candidates for an ICD.21 Conversely, even among patients who do qualify for ICD implantation based on the ejection fraction criterion, only a small minority (2% to 5% per year), will suffer a ventricular arrhythmia resulting in SCD19,20,59 Furthermore, for most patients, the ejection fraction is a risk factor that is identified relatively late in the natural history of this particular high-risk phenotype14 and is of no utility for those in whom SCD is the initial manifestation of cardiovascular disease. To maximize effectiveness of prevention, risk factors need to be identified and utilized early in the natural history of specific high risk conditions.14

In the past decade, other clinical risk markers have been identified, but none of these are currently used for risk stratification.25,28,6063 These include LV hypertrophy,62 QTc prolongation,28,63 diabetes mellitus,25,6063 and elevated resting heart rate.64 Serum biomarkers have also been identified that are associated with risk of SCD in cohorts and community-based studies.6568 In a significant proportion of patients, SCD is likely to be triggered by plaque rupture and acute myocardial infarction. There are significant ongoing efforts to identify biomarkers as well as imaging techniques that pinpoint key events related to inflammation and vulnerable plaque pathways.6971 The challenge is in identifying the specific patient who will suffer SCD with plaque rupture and acute myocardial infarction.

Upon the publication of 4 studies that provide strong evidence for independent genetic contributions to risk of SCD,25,26,72,73 the identification of variants that confer genetic susceptibility has become an area of active investigation. Candidate-gene based association studies have identified some candidates for SCD risk,7483 and genome-wide association studies (GWAS) are ongoing. The latter can be divided into 2 categories. The first are GWAS that have identified determinants of intermediate-risk traits for SCD such as the QT interval.80,84,85 These have been followed by evaluation of specific significant variants in populations with SCD.86,87 In this fashion, variants in NOS1AP have been identified as modest predictors of risk (odds ratios ≈1.3). The second GWAS approach investigates SCD risk directly in the general population using a case-control approach. These latter GWAS, which are unbiased by previous hypotheses relative to candidate genes and pathways, have the power to illuminate novel biological pathways involved in the genesis of lethal ventricular arrhythmias, which could ultimately lead to new therapeutic approaches for SCD prevention. An initial GWAS from the Oregon Sudden Unexpected Death Study has identified a novel genetic locus (glypican 5) that is protective against SCD, a finding that has been replicated in the ARIC and CHS cohorts.88 Other investigators studied individuals with and without VF in the first 90 minutes of a first myocardial infarction (MI), and have identified a risk locus at chromosome 21q21.89 These studies underscore the importance of conducting GWAS in significantly larger numbers of cases and controls.

As outlined earlier, for a significant proportion of SCD cases the final event is the first outward manifestation of disease (ie, there have been no premonitory warning symptoms or signs that would prompt medical attention). Even when symptoms are reported prior to SCD, these have not been found to be specific for the phenotype. Given the complexity of the SCD phenotype and overlap with conditions such as CAD, congestive heart failure, and diabetes mellitus, it is likely that any prediction of risk will involve a combination of risk factors and/or tests as opposed to a single marker or test. The generally accepted paradigm of requiring both substrates and triggers for genesis of ventricular arrhythmia90 lends additional complexity to SCD risk prediction. Further, recent studies have implicated a wide range of environmental influences, such as socioeconomic status, psychosocial factors, and even particulate matter, as possibly playing roles in SCD.7,91,92

One approach to identifying risk has been to study device therapies as end points in ICD cohorts. Although these are likely to contribute useful information, it is important to recognize that the nature of this study design and population are likely to limit any useful findings to the optimal selection of ICD candidates relatively late in the natural history of LV dysfunction. These studies will not contribute importantly to detection of risk factors early in the disease process.14

Knowledge Gaps

  • Current methods of clinical risk prediction are inadequate and there is increasing recognition that employment of the LV ejection fraction as a risk predictor is effective in only a small subgroup of patients.
  • Other risk markers have been discovered, but individually these markers appear to have only modest effects. Examples include LV hypertrophy, prolonged QT interval, fragmented QRS complex, diabetes mellitus, elevated resting heart rate, specific serum biomarkers, and novel genetic variants.
  • There is a conspicuous lack of studies that combine panels of SCD risk markers to assess additive or synergistic effects on risk.
  • There is a need for early detection of risk factors for SCD.

Specific Recommendations 2

  • Facilitate studies that will discover novel risk markers for SCD. There is a role for 2 distinct categories of studies:
    • Optimization of risk prediction late in the natural history of SCD for improved efficiency of the ICD using large cohort studies of patients with heart failure and an ICD.
    • Discovery of novel risk predictors early in the natural history of conditions predisposing to SCD from large population-based studies that perform comprehensive evaluations among all subjects who suffer SCD.
  • Facilitate studies that combine novel risk markers and testing to create risk scores for prediction of SCD.

Recommendation 3: Develop Novel Risk Stratification Strategies to Improve Outcomes in Select Populations at Risk of SCD, Including Patients With ICD Indications Based on Current Guidelines and Other Patients at Risk Such as Those With CAD and LV Ejection Fraction >35%; Early Phase Postacute MI; Heart Failure With Preserved Systolic Function; and/or LV Hypertrophy

Background

Numerous trials of empirical antiarrhythmic drug therapies have been conducted in patients with recent or remote MI and LV dysfunction as well as nonischemic cardiomyopathies, with disappointing results.93,94 In such studies, an antiarrhythmic drug is often considered to be of value if it does not increase overall mortality. Over the last 25 years, clinical studies have shown that ICD therapy in high risk populations can reduce total, cardiac, and to a very high degree, arrhythmic mortality.95 However, there are numerous well recognized limitations to ICD therapy. These include the cost of the devices, complications related both to the implantation procedure and to subsequent device function, device malfunction, and limited efficacy despite normal device function in the presence of significant concomitant disease.96,97 Evidence-based guidelines for ICD therapy derived from these studies have been published and recently updated.98,99 Current criteria are based largely on history of arrhythmia (resuscitated cardiac arrest, sustained ventricular tachycardia, syncope with induced VT, LV dysfunction, and heart failure functional class. Guideline recommendations for less common conditions such as inherited ion channelopathies or many cardiomyopathies are usually based on consensus opinion, because clinical trial data are not available.98,99

In real world practice where ICD recipients are often older and have more comorbidities than the average clinical trial enrollee, the ratio of nonsudden to sudden deaths among ICD recipients may even be higher.100 Numerous tests have been proposed to improve the prediction of SCD as opposed to total mortality.46,101,102 These include programmed electric stimulation, various tests of autonomic nervous system function, standard ECG findings such as QT variability or dispersion, microvolt T wave alternans, and others. Recent data suggest that because of the complexities of the substrates underlying SCD, multiple risk factors used in combination are likely to provide better prediction of SCD risk than any individual risk marker.51,103

Although positive results have been reported in selected populations (eg, programmed stimulation in post MI patients with intermediate LV ejection fraction values),39,59 no single test strategy has proven to be sufficiently sensitive and specific to justify widespread adoption. New imaging techniques now exist for assessing a range of myocardial pathophysiological processes that may be implicated in the pathways that lead to SCD. Magnetic resonance based imaging can quantify cardiac structure and function and the presence and extent of myocardial fibrosis and ischemia. New imaging tracers using positron emission tomography provide measures of cardiac sympathetic function. These sophisticated imaging techniques are promising but have not been tested to date in large studies.104109

Analyses using combinations of new and old risk factors may be more valuable. For example, in a retrospective analysis of the MADIT-II data, Goldenberg et al95 identified 5 variables that might predict benefit of ICD therapy benefit: New York Heart Association functional class, atrial fibrillation, QRS duration, age, and moderate renal dysfunction. Patients with 1, 2, and to a lesser degree 3 risk factors showed benefit whereas those with 0, >3, or severe renal dysfunction alone did not. Studies examining various combinations of risk factors might well improve the efficiency of ICD therapy in patients with current indications.

There are several populations known to be at substantial risk for SCD for whom effective management guidelines have not yet emerged. The early period after MI is associated with a very high mortality rate, but 2 studies, the DINAMIT110 and IRIS111 trials, failed to show benefit in total mortality after ICD implantation. It is noteworthy, however, that deaths classified as arrhythmic were reduced in patients randomized to ICD treatment in both studies. Although medical therapies directed at ischemia and heart failure have substantially improved outcomes in the early postinfarction period,112 the ability to identify and treat those specifically at high risk for arrhythmia would be of importance. There are other populations that may not benefit because they are at low risk: for example, patients in the early period after coronary revascularization.113

Specific pharmacological or device-based antiarrhythmic therapy has not been well studied in other populations with moderate risk, including patients with genetic primary arrhythmia or cardiomyopathy disorders, those with known or probable ischemic heart disease, patients with heart failure with normal or only mildly impaired systolic function, and patients with LV hypertrophy without clinical heart failure. Although the individual annual SCD risk in these populations is relatively low, the number of events in some of these groups may be large. Current strategies to identify the subsets of patients with sufficient risk to justify intervention and prescribe appropriate therapy are very limited. Most therapeutic approaches have been directed at the underlying disease processes of the patients (eg, atherosclerosis, ischemia, and hypertension) rather than the arrhythmogenic potentials for SCD of these conditions. SCD risk detection strategies in these intermediate-risk but numerically substantial populations would need to be relatively simple and inexpensive to justify their widespread use. Even for the familial disorders, such as hypertrophic cardiomyopathy and the long QT syndrome that increase SCD risk, efforts continue to refine prediction of risk.1518

Knowledge Gaps

  • Current methods to differentiate patients at highest risk for arrhythmic death from all-cause death are insufficient and lack robustness in guiding the use of ICD therapy.
  • Data on SCD risk are best developed in patients with moderate or severe LV dysfunction either after MI or with chronic ischemic or nonischemic cardiomyopathies. Although patients without severe systolic dysfunction are at lower individual risk, many sudden deaths occur in such patients. Strategies for effective risk stratification in these moderate risk populations should be investigated.
  • The optimal approaches for combining potential risk factors to identify individuals at risk and to target risk factors for treatment have not been determined.
  • The utility of interventions other than ICD therapy, including the wearable cardioverter-defibrillator and cardiac resynchronization without defibrillation capability in select populations needs to be better defined.
  • The influence of comorbidities including advanced age, atrial arrhythmias, QRS duration or other ECG parameters, and renal or other organ dysfunction on the effectiveness and efficiency of ICD is not well understood.

Specific Recommendations 3

For patients with ICD indications based on current guidelines, research should assess new approaches that may provide incremental information on SCD risk beyond LV ejection fraction.

  • For patient groups known to have high all-cause mortality, research that uses new approaches in assessing risk in SCD versus non-SCD should be encouraged. Approaches that involve a combination of risk factors (identified by novel biomarkers, genetic profiles, and new imaging methods of cardiac structure and physiology) should be evaluated in clinical studies.
  • At the population level, simple and inexpensive tools should be developed to identify patients at elevated risk of SCD.

Recommendation 4: Establish Strategies for SCD Prevention by Targeting Intermediate-Risk Phenotypes

Background

There are significant differences in prediction and prevention of SCD at the level of the individual versus the general population. A particular test or risk factor may enhance risk prediction in an individual, but may not be deployable as a screening tool in the general population because of low overall specificity and limited cost-effectiveness.14 Similarly, only selected prevention modalities may be deployed in the general population. The ICD is clearly an effective prevention modality for the appropriately selected patient, but it has long been recognized that burgeoning healthcare costs are likely to limit its use in the community.56 Novel and more cost-effective methods of SCD screening and prevention will need to be discovered. There are a number of intermediate-risk traits (for example, heart failure, CAD, and LV hypertrophy), that are already being targeted with consequent attenuation of SCD risk. Measures to prevent CAD will continue to have a significant and lasting effect on SCD prevention.1 Similarly, drugs such as β-blockers and angiotensin-converting enzyme inhibitors contribute to prevention of SCD in the large number of patients with heart failure and LV systolic dysfunction.114 It is also likely that prevention, reversal, and attenuation of LV hypertrophy have an impact on SCD prevention.115 However there are other traits such as heart rate abnormalities,64,116,117 prolonged QT interval,28,63 and fragmented QRS118 that contribute to an increasing list of clinical phenotypes associated with SCD risk. These could be targeted by focused investigation to explore potential beneficial effects on the burden of SCD in the community.

Knowledge Gaps

  • Intermediate-risk phenotypes or endophenotypes of SCD remain to be discovered and validated as targets for risk stratification and ultimately SCD prevention.

Specific Recommendations 4

  • Facilitate investigative approaches that target discovery of SCD intermediate-risk phenotypes.
  • Facilitate investigative approaches that target modulation of SCD intermediate-risk phenotypes for prevention of SCD.

Recommendation 5: Develop High-Throughput Strategies to Efficiently Establish the Functional Relevance of Newly Discovered Genetic Information

Background

There has been an explosion in the discovery of genes contributing to normal and abnormal myocyte biology, and with that there has formed a new understanding of the functional relevance of this emerging genetic information. For example, disease genes responsible for monogenic syndromes associated with increased SCD risk have added importantly to our understanding of the broad problem of SCD susceptibility by identifying new biological interactions and pathways whose perturbation increases SCD risk. This paradigm has highlighted the role of genes encoding ion channel pore-forming (eg, SCN5A,119 KCNQ1120) and accessory subunits (eg, CACNB2121 and SCN4B122), as well as cytoskeletal (SNTA1123 ANK2,124 ANK3125) and trafficking (CAV3126) proteins. Common variants in these genes are now being studied as modulators of SCD-related phenotypes: KCNE1 D85N as a risk factor for drug-induced torsades or the congenital Long QT syndrome127,128 and SCN5A S1103Y as a modulator of SCD and of Sudden Infant Death Syndrome risk in African Americans129131 are examples. Findings in mouse models of “monogenic” disease also have important implications for the broad problem of SCD. One example is the strain-dependence of electrophysiological phenotypes, reinforcing the idea that genetic background plays a crucial role in modulating clinical phenotypes.132 Another is the striking contrast between near-normal electrophysiological properties of a mutant channel in heterologous expression and the obvious abnormal phenotype observed in patients133 and in mice.134 Similarly, cellular mechanisms supporting arrhythmias in the monogenic Timothy Syndrome gene (CACNA1C) require recruitment of signaling pathways that are not present in heterologous expression systems, suggesting that there will not be a 1-size-fits-all approach to successfully evaluate arrhythmia-causing disease genes.135

Unbiased strategies, exemplified by GWAS approaches, are identifying new genetic loci and, in some cases, pathways implicated in arrhythmic disease syndromes. Some of these GWAS “hits” are in genetic regions known to be important for normal electrogenesis, such as those encoding ion channels or intracellular calcium control mechanisms, whereas others are in regions not previously implicated in cardiac electrophysiology. The best-studied example to date is NOS1AP, which as described above is a regulator of the normal QT interval, and variants also seem to predict SCD in the community (ARIC, CHS).87 These findings also reinforce previous observations suggesting that QT prolongation (eg, post-MI) is a marker for SCD28,136 NOS1AP variants have been implicated as modulators of risk in the congenital long QT syndromes,137 and have been associated with variability in calcium channel blocker-associated SCD.138 The latter observations highlight the facts that (1) drug-induced arrhythmias may represent a useful model within which to explore SCD risk variation, and (2) SCD due to drug exposure may be more common than previously appreciated and may have a “non-QT” component. Another example is sequence variation in SCN10A, which encodes the Nav1.8 sodium channel pore-forming subunit, which has recently been associated with cardiac conduction parameters, including QRS duration,139,140 which is a predictor of SCD.141 New pathways and mechanisms regulating cellular electrophysiology have the potential to influence SCD susceptibility. Examples are stretch,142 trafficking,143,144 and intracellular signaling pathways.145 Dysregulation of microRNA expression has also recently been implicated as a modulator of channel dysfunction that leads to SCD susceptibility.146,147

A range of methods, including heterologous expression in cells and genetically modified animal models, are available to study the function of protein-coding genes and to assess the consequences of missense and nonsense sequence variants. However, all of these approaches suffer from their relatively low throughput, particularly when detailed electrophysiological function is to be ascertained. Efficient strategies to determine how sequence variants in noncoding regions influence SCD predisposition are even more elusive. Novel approaches, such as screens using zebrafish148,149 or Drosophila,150 are an important advance. It is also conceivable that embryonic stem cell and induced pluripotent stem cell-derived cardiomyocytes may provide additional high-throughput assay systems151,152 relevant to SCD prediction and prevention.

Knowledge Gaps

  • The functional relevance and mechanisms of action of sequence variants in genes associated with increased risk of SCD are largely unknown.
  • Experimental strategies to evaluate the physiological relevance of individual genes/gene products, sequence variants in genes, and pathways are inefficient and may not provide relevant information for human disease susceptibility.

Specific Recommendations 5

  • Establish high-throughput tools to determine functional relevance of newly discovered genetic information.
  • Evaluate candidate genes in multiple systems.

Recommendation 6: Establish Multiscale Integrative Models, Including Molecular, Cellular, and Organ Level, Animal, and Computational, Relevant to Human Electrophysiology and Disease

Background

A fundamental tenet in the field is that SCD represents the interaction between triggers and a susceptible substrate. A corollary of this concept, yet unproven, is the notion that improved understanding of triggers and substrate, at varying levels of complexity ranging from molecular through the emerging field of systems biology, is likely to provide insight into SCD prediction and prevention.153,154 For example, recent theoretical, experimental, and clinical data suggest that the Purkinje fiber network is an important arrhythmic trigger in disease states, including acquired and inherited syndromes.155158 Experimental data with potential relevance to arrhythmic behavior and SCD derives from multiple domains, including molecular structure and dynamics,159,160 gene expression networks,161 cellular,162 organ-level163165 and whole organism166 behavior. Multiscale models representing the different levels of structural and functional integration will be required to explore behavior from the molecule to the organ to the patient.167 This involves bridging the spatial and temporal scales, from nanometer to meter, and from nanoseconds to minutes, hours or longer.168 Such an endeavor will require developing new algorithms and approaches to achieve necessary levels of integration. Furthermore, to address the contribution of various factors to the origin and maintenance of arrhythmias, simulations will increasingly become multi-faceted, representing the consequences of factors such as soft tissue mechanics and fluid dynamics on electrophysiological behaviors.169,170 Finally, the relationship between structure and electric function at the various (molecular, cellular, tissue, organ) levels of complexity in the heart will have to be incorporated in a comprehensive manner in arrhythmia models and simulations.171 Cardiac function at any level cannot be dissociated from the underlying structure. This relationship holds special prominence in the mechanisms of arrhythmogenesis in cardiac disease and needs to be reflected in the modeling efforts.172

Some of these modeling and simulation approaches are already under development, including (1) a canine epicardial action potential model that reproduces a wide range of experimentally observed rate-dependent behaviors such as adaptation, restitution, and accommodation173; (2) an updated mathematical model of CaMKII signaling in the canine epicardial infarct border zone,174 which establishes abnormal CaMKII signaling as an important component of remodeling; (3) new models of the neonatal mouse ventricular myocyte,175 rabbit ventricular myocyte,176 and human atrial myocyte177; (4) a human Purkinje cell model155; (5) the first action potential model that integrates excitation-contraction coupling and mitochondrial bioenergetics178 and the application of this model to examine the control and regulation of oxygen consumption179; (6) a mathematical model of Ca2+ spark triggering under voltage-clamp conditions that predicts changes in excitation-contraction coupling “gain” resulting from diverse experimental interventions180; and (7) a rabbit sino-atrial node model featuring coupled subsarcolemmal Ca2+ and sarcolemmal voltage clocks.181

Similarly, there is progress in understanding the dynamic mechanisms that underlie alternans, arrhythmogenesis, and the transition from VT to VF.182,183 This new framework builds on the knowledge that alternans at the cellular level can be caused by dynamical instabilities arising from either membrane voltage (Vm) attributable to steep APD restitution and/or to calcium (Ca) cycling.184,185 Emerging novel insights include mechanistic links between Ca sparks and whole-cell Ca alternans,186 as well as the role of fibroblast-myocyte coupling in cardiac alternans.187

Progress in SCD prediction may also derive from a new class of integrative models that rely on reconstructions of cardiac structure from histology or structural imaging modalities. Examples include 3-dimensional reconstructions of sinoatrial and atrioventricular nodes,188 as well as image-based reconstructions of the heart and remodeling associated with infarction or heart failure. These may serve to demonstrate the role of infarct scar morphology in VT and the emergence of 3-dimensional electromechanical delay in heart failure.189

The electrocardiographic imaging technique,190 which represents an inverse problem where the epicardial potential is determined from body surface potentials and computed tomography, has made a major foray into clinical applications. In a series of studies, the investigators imaged noninvasively atrial repolarization, ventricular bigeminy, and ablation of accessory pathways.191194 One can easily imagine extension of this approach to use functional imaging to enhance prediction of those at risk of SCD.

Knowledge Gaps

  • Strategies to integrate data across scales of increasing complexity, from molecule to cell, tissue, the whole heart, and ultimately the patient are lacking.
  • Methods to extrapolate mechanisms from animal models of arrhythmogenesis to the human heart are incompletely developed.
  • Patient-specific approaches to prediction and prevention of SCD are not well established.
  • Accessible interfaces that allow utilization of computer models by nonexperts are needed.
  • The theoretical and mechanistic bases of how complex systems undergo transitions from stable to unstable behavior are poorly understood.

Specific Recommendations 6

  • Establish multiscale models to integrate behavior from the molecule to the organ and the patient.
  • Enhance acceptance and utilization of such models to achieve improved mechanistic understanding of arrhythmogenesis as well as the effects and implications of new antiarrhythmia therapies.

Conclusion

The prediction and prevention of SCD remains an enormous challenge. Despite the accumulation of remarkable insight into the genetic basis and regulation of cardiac excitability, translation of this knowledge into novel strategies to identify the majority of individuals at risk of SCD is lacking, as it has targeted antiarrhythmic therapy. Translating new genetic information into improved understanding of physiology and disease represents a bottleneck to progress in mechanistic SCD research. Recent population, clinical, and basic science research studies, however, suggest there are real opportunities to improve our ability to identify individuals at moderate and high risk of SCD and to intervene to diminish such risk. Nonetheless, the complexity of the problem cannot be overstated and integrative strategies spanning a broad range of scales from molecular through organism and population studies, will be required to make progress in this area.

Acknowledgments

Acknowledgments

None.

Sources of Funding

Dr Albert received National Institutes of Health (NIH) research grants HL068070 and HL091069 and is the recipient of the American Heart Association Established Investigator Award; Dr Anderson received NIH research grants HL70250, HL079031 and HL096652 and funding from Fondation Leducq Alliance for CaMKII Signaling in Heart; Dr Chen received NIH research grants HL78931, HL78932, and HL71140 and funding from the Medtronic-Zipes Endowment; Dr Chugh received NIH research grants HL105170, HL088416, and HL088416; Dr Fishman received NIH research grants HL64757, HL82727, and HL081336; Dr Nerbonne received NIH research grants HL034161 and HL066388; Dr Roden received NIH research grants HL65962 and HL49989 and funding from Fondation Leducq Preventing Sudden Death Trans-Atlantic Alliance; Dr Rosenbaum received NIH research grant RO1-HL54807; Dr Sotoodehnia received NIH research grant R01HL088456; Dr Trayanova received NIH research grant HL082729 and National Science Foundation grant CBET-0601935.

Footnotes

Disclosures

Dr Albert received research grants from St. Jude Medical, is a consultant to Novartis, and is on the clinical trial end point committee of GlaxoSmithKline; Dr Buxton received honoraria from GE Healthcare and Medtronic; Dr Chen received research support from Medtronic and St. Jude Medical; Dr DiMarco received a research grant from Medtronic, a training grant from Boston Scientific, and honoraria from St. Jude Medical and is a consultant to Medtronic; Dr Estes received a research grant and honoraria from and is a consultant and expert witness for Boston Scientific; Dr Fishman received a New York State NYSTEM grant; Dr Lathrop is an employee of NIH; Dr Mascette is an employee of NIH and a member of the PRECISION Trial Executive Committee; Dr Roden is a consultant to Merck, Sanofi, Daiichi, and Vitae Pharmaceutical; Dr Rosenbaum is on the Cambridge Heart Advisory Board; Dr Tray-anova is the cofounder of Cardiosolv LLC; and Dr Zheng is an employee of NIH.

References

1. Fox CS, Evans JC, Larson MG, Kannel WB, Levy D. Temporal trends in coronary heart disease mortality and sudden cardiac death from 1950 to 1999: the Framingham Heart Study. Circulation. 2004;110:522–527. [PubMed]
2. Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Stafford R, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation. 2010;121:e46–e215. [PubMed]
3. Byrne R, Constant O, Smyth Y, Callagy G, Nash P, Daly K, Crowley J. Multiple source surveillance incidence and aetiology of out-of-hospital sudden cardiac death in a rural population in the West of Ireland. Eur Heart J. 2008;29:1418–1423. [PubMed]
4. Chugh SS, Jui J, Gunson K, Stecker EC, John BT, Thompson B, Ilias N, Vickers C, Dogra V, Daya M, Kron J, Zheng ZJ, Mensah G, McAnulty J. Current burden of sudden cardiac death: multiple source surveillance versus retrospective death certificate-based review in a large U.S. Community. J Am Coll Cardiol. 2004;44:1268–1275. [PubMed]
5. de Vreede-Swagemakers JJ, Gorgels AP, Dubois-Arbouw WI, van Ree JW, Daemen MJ, Houben LG, Wellens HJ. Out-of-hospital cardiac arrest in the 1990’s: a population-based study in the Maastricht area on incidence, characteristics and survival. J Am Coll Cardiol. 1997;30:1500–1505. [PubMed]
6. Vaillancourt C, Stiell IG. Cardiac arrest care and emergency medical services in Canada. Can J Cardiol. 2004;20:1081–1090. [PubMed]
7. Chugh SS, Reinier K, Teodorescu C, Evanado A, Kehr E, Al Samara M, Mariani R, Gunson K, Jui J. Epidemiology of sudden cardiac death: clinical and research implications. Prog Cardiovasc Dis. 2008;51:213–228. [PMC free article] [PubMed]
8. Nichol G, Thomas E, Callaway CW, Hedges J, Powell JL, Aufderheide TP, Rea T, Lowe R, Brown T, Dreyer J, Davis D, Idris A, Stiell I. Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA. 2008;300:1423–1431. [PMC free article] [PubMed]
9. Goldberger JJ, Cain ME, Hohnloser SH, Kadish AH, Knight BP, Lauer MS, Maron BJ, Page RL, Passman RS, Siscovick D, Siscovick D, Stevenson WG, Zipes DP. American Heart Association, American College of Cardiology Foundation, Heart Rhythm Society. American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society scientific statement on noninvasive risk stratification techniques for identifying patients at risk for sudden cardiac death: a scientific statement from the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention. Circulation. 2008;118:1497–1518. [PubMed]
10. Myerburg RJ. Scientific gaps in the prediction and prevention of sudden cardiac death. J Cardiovasc Electrophysiol. 2002;13:709–723. [PubMed]
11. Spooner PM, Albert C, Benjamin EJ, Boineau R, Elston RC, George AL, Jr, Jouven X, Kuller LH, MacCluer JW, Marbán E, Muller JE, Schwartz PJ, Siscovick DS, Tracy RP, Zareba W, Zipes DP. Sudden cardiac death, genes, and arrhythmogenesis: consideration of new population and mechanistic approaches from a National Heart, Lung, and Blood Institute workshop, part I. Circulation. 2001;103:2361–2364. [PubMed]
12. Spooner PM, Albert C, Benjamin EJ, Boineau R, Elston RC, George AL, Jr, Jouven X, Kuller LH, MacCluer JW, Marbán E, Muller JE, Schwartz PJ, Siscovick DS, Tracy RP, Zareba W, Zipes DP. Sudden cardiac death, genes, and arrhythmogenesis: consideration of new population and mechanistic approaches from a National Heart, Lung, and Blood Institute workshop, part II. Circulation. 2001;103:2447–2452. [PubMed]
13. Zipes DP. Sudden cardiac death: future approaches. Circulation. 1992;85:I-160–I-166. [PubMed]
14. Chugh SS. Early identification of risk factors for sudden cardiac death. Nat Rev Cardiol. 2010;7:318–326. [PMC free article] [PubMed]
15. Probst V, Veltmann C, Eckardt L, Meregalli PG, Gaita F, Tan HL, Babuty D, Sacher F, Giustetto C, Schulze-Bahr E, Borggrefe M, Hais-saguerre M, Mabo P, Le Marec H, Wolpert C, Wilde AA. Long-term prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry. Circulation. 2010;121:635–643. [PubMed]
16. Moss AJ, Shimizu W, Wilde AA, Towbin JA, Zareba W, Robinson JL, Qi M, Vincent GM, Ackerman MJ, Kaufman ES, Hofman N, Seth R, Kamakura S, Miyamoto Y, Goldenberg I, Andrews ML, McNitt S. Clinical aspects of type-1 long-QT syndrome by location, coding type, and biophysical function of mutations involving the KCNQ1 gene. Circulation. 2007;115:2481–2489. [PMC free article] [PubMed]
17. Shimizu W, Moss AJ, Wilde AA, Towbin JA, Ackerman MJ, January CT, Tester DJ, Zareba W, Robinson JL, Qi M, Vincent GM, Kaufman ES, Hofman N, Noda T, Kamakura S, Miyamoto Y, Shah S, Amin V, Goldenberg I, Andrews ML, McNitt S. Genotype-phenotype aspects of type 2 long QT syndrome. J Am Coll Cardiol. 2009;54:2052–2062. [PMC free article] [PubMed]
18. Maron BJ, Spirito P, Shen WK, Haas TS, Formisano F, Link MS, Epstein AE, Almquist AK, Daubert JP, Lawrenz T, Boriani G, Estes NA, III, Favale S, Piccininno M, Winters SL, Santini M, Betocchi S, Arribas F, Sherrid MV, Buja G, Semsarian C, Bruzzi P. Implantable cardioverter-defibrillators and prevention of sudden cardiac death in hypertrophic cardiomyopathy. JAMA. 2007;298:405–412. [PubMed]
19. Bardy GH, Lee KL, Mark DB, Poole JE, Packer DL, Boineau R, Domanski M, Troutman C, Anderson J, Johnson G, McNulty SE, Clapp-Channing N, Davidson-Ray LD, Fraulo ES, Fishbein DP, Luceri RM, Ip JH. Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J Med. 2005;352:225–237. [PubMed]
20. Moss AJ, Zareba W, Hall WJ, Klein H, Wilber DJ, Cannom DS, Daubert JP, Higgins SL, Brown MW, Andrews ML. Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med. 2002;346:877–883. [PubMed]
21. Stecker EC, Vickers C, Waltz J, Socoteanu C, John BT, Mariani R, McAnulty JH, Gunson K, Jui J, Chugh SS. Population-based analysis of sudden cardiac death with and without left ventricular systolic dysfunction: two-year findings from the Oregon Sudden Unexpected Death Study. J Am Coll Cardiol. 2006;47:1161–1166. [PubMed]
22. Fox CS, Evans JC, Larson MG, Lloyd-Jones DM, O’Donnell CJ, Sorlie PD, Manolio TA, Kannel WB, Levy D. A comparison of death certificate out-of-hospital coronary heart disease death with physician-adjudicated sudden cardiac death. Am J Cardiol. 2005;95:856–859. [PubMed]
23. Myerburg RJ, Kessler KM, Castellanous A. Epidemiology of sudden cardiac death: Population characteristics, conditioning risk factors, and dynamic risk factors. Ion Channels in the Cardiovascular System: Function and Dysfunction. 1994:15–35.
24. Albert CM, Chae CU, Grodstein F, Rose LM, Rexrode KM, Ruskin JN, Stampfer MJ, Manson JE. Prospective study of sudden cardiac death among women in the United States. Circulation. 2003;107:2096–2101. [PubMed]
25. Jouven X, Desnos M, Guerot C, Ducimetière P. Predicting sudden death in the population: the Paris Prospective Study I. Circulation. 1999;99:1978–1983. [PubMed]
26. Friedlander Y, Siscovick DS, Weinmann S, Austin MA, Psaty BM, Lemaitre RN, Arbogast P, Raghunathan TE, Cobb LA. Family history as a risk factor for primary cardiac arrest. Circulation. 1998;97:155–160. [PubMed]
27. Chugh SS, Socoteanu C, Reinier K, Waltz J, Jui J, Gunson K. A community-based evaluation of sudden death associated with therapeutic levels of methadone. Am J Med. 2008;121:66–71. [PMC free article] [PubMed]
28. Chugh SS, Reinier K, Singh T, Uy-Evanado A, Socoteanu C, Peters D, Mariani R, Gunson K, Jui J. Determinants of prolonged QT interval and their contribution to sudden death risk in coronary artery disease: the Oregon Sudden Unexpected Death Study. Circulation. 2009;119:663–670. [PMC free article] [PubMed]
29. Hinkle L, Jr, Thaler H. Clinical classification of cardiac deaths. Circulation. 1982;65:457–464. [PubMed]
30. Kannel WB, Schatzkin A. Sudden death: lessons from subsets in population studies. J Am Coll Cardiol. 1985;5:141B–149B. [PubMed]
31. Cobb LA, Fahrenbruch CE, Olsufka M, Copass MK. Changing incidence of out-of-hospital ventricular fibrillation, 1980–2000. JAMA. 2002;288:3008–3013. [PubMed]
32. Herlitz J, Andersson E, Bång A, Engdahl J, Holmberg M, Lindqvist J, Karlson BW, Waagstein L. Experiences from treatment of out-of-hospital cardiac arrest during 17 years in Göteborg. Eur Heart J. 2000;21:1251–1258. [PubMed]
33. Zipes DP, Wellens HJ. Sudden cardiac death. Circulation. 1998;98:2334–2351. [PubMed]
34. Bailey JJ, Berson AS, Handelsman H, Hodges M. Utility of current risk stratification tests for predicting major arrhythmic events after myocardial infarction. J Am Coll Cardiol. 2001;38:1902–1911. [PubMed]
35. Wilber DJ, Olshansky B, Moran JF, Scanlon PJ. Electrophysiological testing and nonsustained ventricular tachycardia: use and limitations in patients with coronary artery disease and impaired ventricular function. Circulation. 1990;82:350–358. [PubMed]
36. Wellens HJ, Schuilenburg RM, Durrer D. Electrical stimulation of the heart in patients with ventricular tachycardia. Circulation. 1972;46:216–226. [PubMed]
37. Buxton AE, Lee KL, Fisher JD, Josephson ME, Prystowsky EN, Hafley G. A randomized study of the prevention of sudden death in patients with coronary artery disease. Multicenter Unsustained Tachycardia Trial Investigators. N Engl J Med. 1999;341:1882–1890. [PubMed]
38. Moss AJ, Hall WJ, Cannom DS, Daubert JP, Higgins SL, Klein H, Levine JH, Saksena S, Waldo AL, Wilber D, Brown MW, Heo M. Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia. Multicenter Automatic Defibrillator Implantation Trial Investigators. N Engl J Med. 1996;335:1933–1940. [PubMed]
39. Buxton AE, Lee KL, DiCarlo L, Gold MR, Greer GS, Prystowsky EN, O’Toole MF, Tang A, Fisher JD, Coromilas J, Talajic M, Hafley G. Electrophysiologic testing to identify patients with coronary artery disease who are at risk for sudden death. Multicenter Unsustained Tachycardia Trial Investigators. N Engl J Med. 2000;342:1937–1945. [PubMed]
40. Kleiger RE, Miller JP, Bigger JT, Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987;59:256–262. [PubMed]
41. Bauer A, Schmidt G. Heart rate turbulence. J Electrocardiol. 2003;36 (Suppl):89–93. [PubMed]
42. Nishime EO, Cole CR, Blackstone EH, Pashkow FJ, Lauer MS. Heart rate recovery and treadmill exercise score as predictors of mortality in patients referred for exercise ECG. JAMA. 2000;284:1392–1398. [PubMed]
43. La Rovere MT, Bigger JT, Jr, Marcus FI, Mortara A, Schwartz PJ. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet. 1998;351:478–484. [PubMed]
44. Kuchar DL, Thorburn CW, Sammel NL. Prediction of serious arrhythmic events after myocardial infarction: signal-averaged electrocardiogram, Holter monitoring and radionuclide ventriculography. J Am Coll Cardiol. 1987;9:531–538. [PubMed]
45. Das MK, Khan B, Jacob S, Kumar A, Mahenthiran J. Significance of a fragmented QRS complex versus a Q wave in patients with coronary artery disease. Circulation. 2006;113:2495–2501. [PubMed]
46. Rosenbaum DS, Jackson LE, Smith JM, Garan H, Ruskin JN, Cohen RJ. Electrical alternans and vulnerability to ventricular arrhythmias. N Engl J Med. 1994;330:235–241. [PubMed]
47. Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H, Tomaselli GF. Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation. 1997;96:1557–1565. [PubMed]
48. Couderc JP, Zareba W, McNitt S, Maison-Blanche P, Moss AJ. Repolarization variability in the risk stratification of MADIT II patients. Europace. 2007;9:717–723. [PubMed]
49. Hohnloser SH, Klingenheben T, Li YG, Zabel M, Peetermans J, Cohen RJ. T wave alternans as a predictor of recurrent ventricular tachyarrhythmias in ICD recipients: prospective comparison with conventional risk markers. J Cardiovasc Electrophysiol. 1998;9:1258–1268. [PubMed]
50. Feld GK, Clopton P. Comparability of noninvasive microvolt T-wave alternans versus invasive ventricular programmed stimulation to guide implantable cardioverter-defibrillator implantation in patients at risk of sudden death. J Am Coll Cardiol. 2009;53:480–482. [PubMed]
51. Costantini O, Hohnloser SH, Kirk MM, Lerman BB, Baker JH, II, Sethuraman B, Dettmer MM, Rosenbaum DS. The ABCD (Alternans Before Cardioverter Defibrillator) trial: strategies using T-wave alternans to improve efficiency of sudden cardiac death prevention. J Am Coll Cardiol. 2009;53:471–479. [PubMed]
52. Chow T, Kereiakes DJ, Onufer J, Woelfel A, Gursoy S, Peterson BJ, Brown ML, Pu W, Benditt DG. Does microvolt T-wave alternans testing predict ventricular tachyarrhythmias in patients with ischemic cardiomyopathy and prophylactic defibrillators? the MASTER (Microvolt T Wave Alternans Testing for Risk Stratification of Post-Myocardial Infarction Patients) trial. J Am Coll Cardiol. 2008;52:1607–1615. [PubMed]
53. Gold MR, Ip JH, Costantini O, Poole JE, McNulty S, Mark DB, Lee KL, Bardy GH. Role of microvolt T-wave alternans in assessment of arrhythmia vulnerability among patients with heart failure and systolic dysfunction: primary results from the T-Wave Alternans Sudden Cardiac Death in Heart Failure trial substudy. Circulation. 2008;118:2022–2028. [PMC free article] [PubMed]
54. Follansbee WP, Michelson EL, Morganroth J. Nonsustained ventricular tachycardia in ambulatory patients: characteristics and association with sudden cardiac death. Ann Intern Med. 1980;92:741–747. [PubMed]
55. Cannom DS. Prevention of sudden cardiac death. J Cardiovasc Electrophysiol. 2005;16 (Suppl 1):S21–24. [PubMed]
56. Josephson M, Wellens HJ. Implantable defibrillators and sudden cardiac death. Circulation. 2004;109:2685–2691. [PubMed]
57. Chugh SS, Uy-Evanado A, Teodorescu C, Reinier K, Mariani R, Gunson K, Jui J. Women have a lower prevalence of structural heart disease as a precursor to sudden cardiac arrest: the Ore-SUDS (Oregon Sudden Unexpected Death Study) J Am Coll Cardiol. 2009;54:2006–2011. [PMC free article] [PubMed]
58. Adabag AS, Peterson G, Apple FS, Titus J, King R, Luepker RV. Etiology of sudden death in the community: results of anatomical, metabolic, and genetic evaluation. Am Heart J. 2010;159:33–39. [PMC free article] [PubMed]
59. Buxton AE, Lee KL, Hafley GE, Pires LA, Fisher JD, Gold MR, Josephson ME, Lehmann MH, Prystowsky EN. Limitations of ejection fraction for prediction of sudden death risk in patients with coronary artery disease: lessons from the MUSTT study. J Am Coll Cardiol. 2007;50:1150–1157. [PubMed]
60. Balkau B, Jouven X, Ducimetière P, Eschwège E. Diabetes as a risk factor for sudden death. Lancet. 1999;354:1968–1969. [PubMed]
61. Dekker JM, Schouten EG, Klootwijk P, Pool J, Kromhout D. Association between QT interval and coronary heart disease in middle-aged and elderly men: the Zutphen study. Circulation. 1994;90:779–785. [PubMed]
62. Haider AW, Larson MG, Benjamin EJ, Levy D. Increased left ventricular mass and hypertrophy are associated with increased risk for sudden death. J Am Coll Cardiol. 1998;32:1454–1459. [PubMed]
63. Straus SM, Kors JA, De Bruin ML, van der Hooft CS, Hofman A, Heeringa J, Deckers JW, Kingma JH, Sturkenboom MC, Stricker BH, Witteman JC. Prolonged QTC interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol. 2006;47:362–367. [PubMed]
64. Jouven X, Zureik M, Desnos M, Guèrot C, Ducimetière P. Resting heart rate as a predictive risk factor for sudden death in middle-aged men. Cardiovasc Res. 2001;50:373–378. [PubMed]
65. Jouven X, Charles MA, Desnos M, Ducimetière P. Circulating nonesterified fatty acid level as a predictive risk factor for sudden death in the population. Circulation. 2001;104:756–761. [PubMed]
66. Albert CM, Campos H, Stampfer MJ, Ridker PM, Manson JE, Willett WC, Ma J. Blood levels of long-chain n-3 fatty acids and the risk of sudden death. N Engl J Med. 2002;346:1113–1118. [PubMed]
67. Lemaitre RN, King IB, Mozaffarian D, Sotoodehnia N, Siscovick DS. Trans-fatty acids and sudden cardiac death. Atheroscler Suppl. 2006;7:13–15. [PubMed]
68. Korngold EC, Januzzi JL, Jr, Gantzer ML, Moorthy MV, Cook NR, Albert CM. Amino-terminal pro-B-type natriuretic peptide and high-sensitivity C-reactive protein as predictors of sudden cardiac death among women. Circulation. 2009;119:2868–2876. [PMC free article] [PubMed]
69. Finn AV, Nakano M, Narula J, Kolodgie FD, Virmani R. Concept of vulnerable/unstable plaque. Arterioscler Thromb Vasc Biol. 2010;30:1282–1292. [PubMed]
70. Greenland P, Bonow RO, Brundage BH, Budoff MJ, Eisenberg MJ, Grundy SM, Lauer MS, Post WS, Raggi P, Redberg RF, Rodgers GP, Shaw LJ, Taylor AJ, Weintraub WS, Harrington RA, Abrams J, Anderson JL, Bates ER, Grines CL, Hlatky MA, Lichtenberg RC, Lindner JR, Pohost GM, Schofield RS, Shubrooks SJ, Jr, Stein JH, Tracy CM, Vogel RA, Wesley DJ. ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography) Circulation. 2007;115:402–426. [PubMed]
71. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, Go AS, Harrell FE, Jr, Hong Y, Howard BV, Howard VJ, Hsue PY, Kramer CM, McConnell JP, Normand SL, O’Donnell CJ, Smith SC, Jr, Wilson PW. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119:2408–2416. [PMC free article] [PubMed]
72. Dekker LR, Bezzina CR, Henriques JP, Tanck MW, Koch KT, Alings MW, Arnold AE, de Boer MJ, Gorgels AP, Michels HR, Verkerk A, Verheugt FW, Zijlstra F, Wilde AA. Familial sudden death is an important risk factor for primary ventricular fibrillation: a case-control study in acute myocardial infarction patients. Circulation. 2006;114:1140–1145. [PubMed]
73. Kaikkonen KS, Kortelainen ML, Linna E, Huikuri HV. Family history and the risk of sudden cardiac death as a manifestation of an acute coronary event. Circulation. 2006;114:1462–1467. [PubMed]
74. Snapir A, Mikkelsson J, Perola M, Penttila A, Scheinin M, Karhunen PJ. Variation in the alpha2B-adrenoceptor gene as a risk factor for pre-hospital fatal myocardial infarction and sudden cardiac death. J Am Coll Cardiol. 2003;41:190–194. [PubMed]
75. Sotoodehnia N, Siscovick DS, Vatta M, Psaty BM, Tracy RP, Towbin JA, Lemaitre RN, Rea TD, Durda JP, Chang JM, Lumley TS, Kuller LH, Burke GL, Heckbert SR. Beta2-adrenergic receptor genetic variants and risk of sudden cardiac death. Circulation. 2006;113:1842–1848. [PubMed]
76. Aarnoudse AJ, Newton-Cheh C, de Bakker PI, Straus SM, Kors JA, Hofman A, Uitterlinden AG, Witteman JC, Stricker BH. Common NOS1AP variants are associated with a prolonged QTc interval in the Rotterdam study. Circulation. 2007;116:10–16. [PubMed]
77. Fan YM, Lehtimäki T, Rontu R, Ilveskoski E, Goebeler S, Kajander O, Mikkelsson J, Viiri LE, Perola M, Karhunen PJ. The hepatic lipase gene C-480T polymorphism in the development of early coronary atherosclerosis: the Helsinki Sudden Death Study. Eur J Clin Invest. 2007;37:472–477. [PubMed]
78. Albert CM, Nam EG, Rimm EB, Jin HW, Hajjar RJ, Hunter DJ, MacRae CA, Ellinor PT. Cardiac sodium channel gene variants and sudden cardiac death in women. Circulation. 2008;117:16–23. [PubMed]
79. Hernesniemi JA, Karhunen PJ, Rontu R, Ilveskoski E, Kajander O, Goebeler S, Viiri LE, Pessi T, Hurme M, Lehtimäki T. Interleukin-18 promoter polymorphism associates with the occurrence of sudden cardiac death among Caucasian males: the Helsinki Sudden Death Study. Atherosclerosis. 2008;196:643–649. [PubMed]
80. Newton-Cheh C, Eijgelsheim M, Rice KM, de Bakker PI, Yin X, Estrada K, Bis JC, Marciante K, Rivadeneira F, Noseworthy PA, Sotoo-dehnia N, Smith NL, Rotter JI, Kors JA, Witteman JC, Hofman A, Heckbert SR, O’Donnell CJ, Uitterlinden AG, Psaty BM, Lumley T, Larson MG, Stricker BH. Common variants at ten loci influence QT interval duration in the QTGEN study. Nat Genet. 2009;41:399–406. [PMC free article] [PubMed]
81. Sotoodehnia N, Li G, Johnson CO, Lemaitre RN, Rice KM, Rea TD, Siscovick DS. Genetic variation in angiotensin-converting enzyme-related pathways associated with sudden cardiac arrest risk. Heart Rhythm. 2009;6:1306–1314. [PMC free article] [PubMed]
82. Newton-Cheh C, Cook NR, VanDenburgh M, Rimm EB, Ridker PM, Albert CM. A common variant at 9p21 is associated with sudden and arrhythmic cardiac death. Circulation. 2009;120:2062–2068. [PMC free article] [PubMed]
83. Albert CM, MacRae CA, Chasman DI, VanDenburgh M, Buring JE, Manson JE, Cook NR, Newton-Cheh C. Common variants in cardiac ion channel genes are associated with sudden cardiac death. Circ Arrhythm Electrophysiol. 2010;3:222–229. [PMC free article] [PubMed]
84. Arking DE, Pfeufer A, Post W, Kao WH, Newton-Cheh C, Ikeda M, West K, Kashuk C, Akyol M, Perz S, Jalilzadeh S, Illig T, Gieger C, Guo CY, Larson MG, Wichmann HE, Marban E, O’Donnell CJ, Hirschhorn JN, Kaab S, Spooner PM, Meitinger T, Chakravarti A. A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat Genet. 2006;38:644–651. [PubMed]
85. Pfeufer A, Sanna S, Arking DE, Müller M, Gateva V, Fuchsberger C, Ehret GB, Orrú M, Pattaro C, Köttgen A, Perz S, Usala G, Barbalic M, Li M, Pütz B, Scuteri A, Prineas RJ, Sinner MF, Gieger C, Najjar SS, Kao WH, Muhleisen TW, Dei M, Happle C, Möhlenkamp S, Crisponi L, Erbel R, Jöckel KH, Naitza S, Steinbeck G, Marroni F, Hicks AA, Lakatta E, Müller-Myhsok B, Pramstaller PP, Wichmann HE, Schlessinger D, Boerwinkle E, Meitinger T, Uda M, Coresh J, Kääb S, Abecasis GR, Chakravarti A. Common variants at ten loci modulate the QT interval duration in the QTSCD study. Nat Genet. 2009;41:407–414. [PMC free article] [PubMed]
86. Eijgelsheim M, Newton-Cheh C, Aarnoudse AL, van Noord C, Witteman JC, Hofman A, Uitterlinden AG, Stricker BH. Genetic variation in NOS1AP is associated with sudden cardiac death: evidence from the Rotterdam Study. Hum Mol Genet. 2009;18:4213–4218. [PMC free article] [PubMed]
87. Kao WH, Arking DE, Post W, Rea TD, Sotoodehnia N, Prineas RJ, Bishe B, Doan BQ, Boerwinkle E, Psaty BM, Tomaselli GF, Coresh J, Siscovick DS, Marbán E, Spooner PM, Burke GL, Chakravarti A. Genetic variations in nitric oxide synthase 1 adaptor protein are associated with sudden cardiac death in US white community-based populations. Circulation. 2009;119:940–951. [PMC free article] [PubMed]
88. Arking DE, Reinier K, Post W, Jui J, Hilton G, O’Connor A, Prineas RJ, Boerwinkle E, Psaty BM, Tomaselli GF, Rea T, Sotoodehnia N, Siscovick DS, Burke GL, Marban E, Spooner PM, Chakravarti A, Chugh SS. Genome-wide association study identifies GPC5 as a novel genetic locus protective against sudden cardiac arrest. PLoS One. 2010;5:e9879. [PMC free article] [PubMed]
89. Bezzina CR, RP, Bardai A, Marsman RJ, de Jong JS, Blom MT, Scicluna BP, Jukema JW, Bindraban NR, Lichtner P, Pfeufer A, Bishopric NH, Roden DM, Meitinger T, Chugh SS, Myerburg RJ, Jouven X, Kääb S, Dekker LR, Tan HL, Tanck MW, Wilde AA. Genome-wide association study identifies a susceptibility locus at 21q21 for ventricular fibrillation in acute myocardial infarction. Nat Genet. 2010;42:688–691. [PubMed]
90. Jimenez RA, Myerburg RJ. Sudden cardiac death: magnitude of the problem, substrate/trigger interaction, and populations at high risk. Cardiol Clin. 1993;11:1–9. [PubMed]
91. Ljungman PL, Berglind N, Holmgren C, Gadler F, Edvardsson N, Pershagen G, Rosenqvist M, Sjögren B, Bellander T. Rapid effects of air pollution on ventricular arrhythmias. Eur Heart J. 2008;29:2894–2901. [PubMed]
92. Reinier K, Stecker EC, Vickers C, Gunson K, Jui J, Chugh SS. Incidence of sudden cardiac arrest is higher in areas of low socioeconomic status: a prospective two year study in a large United States community. Resuscitation. 2006;70:186–192. [PubMed]
93. Connolly SJ. Meta-analysis of antiarrhythmic drug trials. Am J Cardiol. 1999;84:90R–93R. [PubMed]
94. Piccini JP, Berger JS, O’Connor CM. Amiodarone for the prevention of sudden cardiac death: a meta-analysis of randomized controlled trials. Eur Heart J. 2009;30:1245–1253. [PubMed]
95. Goldenberg I, Vyas AK, Hall WJ, Moss AJ, Wang H, He H, Zareba W, McNitt S, Andrews ML. Risk stratification for primary implantation of a cardioverter-defibrillator in patients with ischemic left ventricular dysfunction. J Am Coll Cardiol. 2008;51:288–296. [PubMed]
96. Tung R, Zimetbaum P, Josephson ME. A critical appraisal of implantable cardioverter-defibrillator therapy for the prevention of sudden cardiac death. J Am Coll Cardiol. 2008;52:1111–1121. [PubMed]
97. Mason PK, DiMarco JP. Unresolved issues in implantable cardioverter-defibrillator therapy. Cardiol Clin. 2008;26:433–439. vii. [PubMed]
98. Zipes DP, Camm AJ, Borggrefe M, Buxton AE, Chaitman B, Fromer M, Gregoratos G, Klein G, Moss AJ, Myerburg RJ, Priori SG, Quinones MA, Roden DM, Silka MJ, Tracy C, Smith SC, Jr, Jacobs AK, Adams CD, Antman EM, Anderson JL, Hunt SA, Halperin JL, Nishimura R, Ornato JP, Page RL, Riegel B, Blanc JJ, Budaj A, Dean V, Deckers JW, Despres C, Dickstein K, Lekakis J, McGregor K, Metra M, Morais J, Osterspey A, Tamargo JL, Zamorano JL. ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death) J Am Coll Cardiol. 2006;48:e247–346. [PubMed]
99. Epstein AE, Dimarco JP, Ellenbogen KA, Estes NA, III, Freedman RA, Gettes LS, Gillinov AM, Gregoratos G, Hammill SC, Hayes DL, Hlatky MA, Newby LK, Page RL, Schoenfeld MH, Silka MJ, Stevenson LW, Sweeney MO. ACC/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities. Heart Rhythm. 2008;5:e1–e62. [PubMed]
100. Hammill SC, Kremers MS, Stevenson LW, Kadish AH, Heidenreich PA, Lindsay BD, Mirro MJ, Radford MJ, Wang Y, Curtis JP, Lang CM, Harder JC, Brindis RG. Review of the registry’s second year, data collected, and plans to add lead and pediatric ICD procedures. Heart Rhythm. 2008;5:1359–1363. [PubMed]
101. Buxton AE. Risk stratification for sudden death in patients with coronary artery disease. Heart Rhythm. 2009;6:836–847. [PubMed]
102. Buxton M, Caine N, Chase D, Connelly D, Grace A, Jackson C, Parkes J, Sharples L. A review of the evidence on the effects and costs of implantable cardioverter defibrillator therapy in different patient groups, and modelling of cost-effectiveness and cost-utility for these groups in a UK context. Health Technol Assess. 2006;10:iii–iv. ix–xi, 1–164. [PubMed]
103. Amit G, Rosenbaum DS, Super DM, Costantini O. Microvolt T-wave alternans and electrophysiologic testing predict distinct arrhythmia substrates: implications for identifying patients at risk for sudden cardiac death. Heart Rhythm. 2010;7:763–768. [PubMed]
104. Yan AT, Shayne AJ, Brown KA, Gupta SN, Chan CW, Luu TM, Di Carli MF, Reynolds HG, Stevenson WG, Kwong RY. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. Circulation. 2006;114:32–39. [PubMed]
105. Villuendas R, Kadish AH. Cardiac magnetic resonance for risk stratification: the sudden death risk portrayed. Prog Cardiovasc Dis. 2008;51:128–134. [PubMed]
106. Kwon DH, Smedira NG, Rodriguez ER, Tan C, Setser R, Thamilarasan M, Lytle BW, Lever HM, Desai MY. Cardiac magnetic resonance detection of myocardial scarring in hypertrophic cardiomyopathy: correlation with histopathology and prevalence of ventricular tachycardia. J Am Coll Cardiol. 2009;54:242–249. [PubMed]
107. Kim RJ, Fieno DS, Parrish TB, Harris K, Chen EL, Simonetti O, Bundy J, Finn JP, Klocke FJ, Judd RM. Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation. 1999;100:1992–2002. [PubMed]
108. Schmidt A, Azevedo CF, Cheng A, Gupta SN, Bluemke DA, Foo TK, Gerstenblith G, Weiss RG, Marban E, Tomaselli GF, Lima JA, Wu KC. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation. 2007;115:2006–2014. [PMC free article] [PubMed]
109. Tamaki S, Yamada T, Okuyama Y, Morita T, Sanada S, Tsukamoto Y, Masuda M, Okuda K, Iwasaki Y, Yasui T, Hori M, Fukunami M. Cardiac iodine-123 metaiodobenzylguanidine imaging predicts sudden cardiac death independently of left ventricular ejection fraction in patients with chronic heart failure and left ventricular systolic dysfunction: Results from a comparative study with signal-averaged electrocardiogram, heart rate variability, and QT dispersion. J Am Coll Cardiol. 2009;53:426–435. [PubMed]
110. Hohnloser SH, Kuck KH, Dorian P, Roberts RS, Hampton JR, Hatala R, Fain E, Gent M, Connolly SJ. Prophylactic use of an implantable cardioverter-defibrillator after acute myocardial infarction. N Engl J Med. 2004;351:2481–2488. [PubMed]
111. Steinbeck G, Andresen D, Seidl K, Brachmann J, Hoffmann E, Wojcie-chowski D, Kornacewicz-Jach Z, Sredniawa B, Lupkovics G, Hofgärtner F, Lubinski A, Rosenqvist M, Habets A, Wegscheider K, Senges J. Defibrillator implantation early after myocardial infarction. N Engl J Med. 2009;361:1427–1436. [PubMed]
112. Adabag AS, Therneau TM, Gersh BJ, Weston SA, Roger VL. Sudden death after myocardial infarction. JAMA. 2008;300:2022–2029. [PMC free article] [PubMed]
113. Bigger JT., Jr Prophylactic use of implanted cardiac defibrillators in patients at high risk for ventricular arrhythmias after coronary-artery bypass graft surgery. Coronary Artery Bypass Graft (CABG) Patch Trial Investigators. N Engl J Med. 1997;337:1569–1575. [PubMed]
114. Nanthakumar K, Epstein AE, Kay GN, Plumb VJ, Lee DS. Prophylactic implantable cardioverter-defibrillator therapy in patients with left ventricular systolic dysfunction: a pooled analysis of 10 primary prevention trials. J Am Coll Cardiol. 2004;44:2166–2172. [PubMed]
115. Wachtell K, Okin PM, Olsen MH, Dahlöf B, Devereux RB, Ibsen H, Kjeldsen SE, Lindholm LH, Nieminen MS, Thygesen K. Regression of electrocardiographic left ventricular hypertrophy during antihypertensive therapy and reduction in sudden cardiac death: the LIFE study. Circulation. 2007;116:700–705. [PubMed]
116. Dyer AR, Persky V, Stamler J, Paul O, Shekelle RB, Berkson DM, Lepper M, Schoenberger JA, Lindberg HA. Heart rate as a prognostic factor for coronary heart disease and mortality: findings in three Chicago epidemiologic studies. Am J Epidemiol. 1980;112:736–749. [PubMed]
117. Jouven X, Schwartz PJ, Escolano S, Straczek C, Tafflet M, Desnos M, Empana JP, Ducimetière P. Excessive heart rate increase during mild mental stress in preparation for exercise predicts sudden death in the general population. Eur Heart J. 2009;30:1703–1710. [PubMed]
118. Das MK, Zipes DP. Fragmented QRS: a predictor of mortality and sudden cardiac death. Heart Rhythm. 2009;6(3 Suppl):S8–S14. [PubMed]
119. Wang Q, Shen J, Splawski I, Atkinson D, Li Z, Robinson JL, Moss AJ, Towbin JA, Keating MT. SCN5A mutations associated with an inherited cardiac arrhythmia, long QT syndrome. Cell. 1995;80:805–811. [PubMed]
120. Wang Q, Curran ME, Splawski I, Burn TC, Millholland JM, VanRaay TJ, Shen J, Timothy KW, Vincent GM, de Jager T, Schwartz PJ, Toubin JA, Moss AJ, Atkinson DL, Landes GM, Connors TD, Keating MT. Positional cloning of a novel potassium channel gene: KVLQT1 mutations cause cardiac arrhythmias. Nat Genet. 1996;12:17–23. [PubMed]
121. Antzelevitch C, Pollevick GD, Cordeiro JM, Casis O, Sanguinetti MC, Aizawa Y, Guerchicoff A, Pfeiffer R, Oliva A, Wollnik B, Gelber P, Bonaros EP, Jr, Burashnikov E, Wu Y, Sargent JD, Schickel S, Oberheiden R, Bhatia A, Hsu LF, Haissaguerre M, Schimpf R, Borggrefe M, Wolpert C. Loss-of-function mutations in the cardiac calcium channel underlie a new clinical entity characterized by ST-segment elevation, short QT intervals, and sudden cardiac death. Circulation. 2007;115:442–449. [PMC free article] [PubMed]
122. Medeiros-Domingo A, Kaku T, Tester DJ, Iturralde-Torres P, Itty A, Ye B, Valdivia C, Ueda K, Canizales-Quinteros S, Tusié-Luna MT, Makielski JC, Ackerman MJ. SCN4B-encoded sodium channel beta4 subunit in congenital long-QT syndrome. Circulation. 2007;116:134–142. [PMC free article] [PubMed]
123. Wu G, Ai T, Kim JJ, Mohapatra B, Xi Y, Li Z, Abbasi S, Purevjav E, Samani K, Ackerman MJ, Qi M, Moss AJ, Shimizu W, Towbin JA, Cheng J, Vatta M. Alpha-1-syntrophin mutation and the long-QT syndrome: a disease of sodium channel disruption. Circ Arrhythm Electrophysiol. 2008;1:193–201. [PMC free article] [PubMed]
124. Mohler PJ, Schott JJ, Gramolini AO, Dilly KW, Guatimosim S, duBell WH, Song LS, Haurogné K, Kyndt F, Ali ME, Rogers TB, Lederer WJ, Escande D, Le Marec H, Bennett V. Ankyrin-B mutation causes type 4 long-QT cardiac arrhythmia and sudden cardiac death. Nature. 2003;421:634–639. [PubMed]
125. Mohler PJ, Rivolta I, Napolitano C, LeMaillet G, Lambert S, Priori SG, Bennett V. Nav1.5 E1053K mutation causing Brugada syndrome blocks binding to ankyrin-G and expression of Nav1.5 on the surface of cardiomyocytes. Proc Natl Acad Sci U S A. 2004;101:17533–17538. [PubMed]
126. Vatta M, Ackerman MJ, Ye B, Makielski JC, Ughanze EE, Taylor EW, Tester DJ, Balijepalli RC, Foell JD, Li Z, Kamp TJ, Towbin JA. Mutant caveolin-3 induces persistent late sodium current and is associated with long-QT syndrome. Circulation. 2006;114:2104–2112. [PubMed]
127. Kaab S, Sinner MF, George AL, Jr, Wilde AA, Bezzina CR, Schulze-Bahr E, Guicheney P, Bishopric NH, Myerburg RJ, Schott J, Pfeufer A, Hinterseer M, Beckmann BM, Steinbeck G, Perz S, Meitinger T, Wichmann H, Ingram C, Carter S, Norris K, Crawford DC, Ritchie M, Roden D. Abstract 4412: High density tagSNP candidate gene analysis identifies IKs as a major modulator of genetic susceptibility to drug induced long QT syndrome. Circulation. 2008;118:S884.
128. Nishio Y, Makiyama T, Itoh H, Sakaguchi T, Ohno S, Gong YZ, Yamamoto S, Ozawa T, Ding WG, Toyoda F, Kawamura M, Akao M, Matsuura H, Kimura T, Kita T, Horie M. D85N, a KCNE1 polymorphism, is a disease-causing gene variant in long QT syndrome. J Am Coll Cardiol. 2009;54:812–819. [PubMed]
129. Van Norstrand DW, Tester DJ, Ackerman MJ. Overrepresentation of the proarrhythmic, sudden death predisposing sodium channel polymorphism S1103Y in a population-based cohort of African-American sudden infant death syndrome. Heart Rhythm. 2008;5:712–715. [PMC free article] [PubMed]
130. Plant LD, Bowers PN, Liu Q, Morgan T, Zhang T, State MW, Chen W, Kittles RA, Goldstein SA. A common cardiac sodium channel variant associated with sudden infant death in African Americans, SCN5A S1103Y. J Clin Invest. 2006;116:430–435. [PMC free article] [PubMed]
131. Burke A, Creighton W, Mont E, Li L, Hogan S, Kutys R, Fowler D, Virmani R. Role of SCN5A Y1102 polymorphism in sudden cardiac death in blacks. Circulation. 2005;112:798–802. [PubMed]
132. Remme CA, Scicluna BP, Verkerk AO, Amin AS, van BS, Beekman L, Deneer VH, Chevalier C, Oyama F, Miyazaki H, Nukina N, Wilders R, Escande D, Houlgatte R, Wilde AA, Tan HL, Veldkamp MW, de Bakker JM, Bezzina CR. Genetically determined differences in sodium current characteristics modulate conduction disease severity in mice with cardiac sodium channelopathy. Circ Res. 2009;104:1283–1292. [PubMed]
133. Groenewegen WA, Firouzi M, Bezzina CR, Vliex S, van Langen IM, Sandkuijl L, Smits JP, Hulsbeek M, Rook MB, Jongsma HJ, Wilde AA. A cardiac sodium channel mutation cosegregates with a rare connexin40 genotype in familial atrial standstill. Circ Res. 2003;92:14–22. [PubMed]
134. Watanabe H, Yang T, Chopra N, Atack T, Hwang HS, Leake B, Agochukwu NB, Kannankeril PJ, Kupershmidt S, Knollmann BC, Roden DM. SCN5A D1275N causes dilated cardiomyopathy and arrhythmia: mechanisms revealed in mice but absent by heterologous expression. American Heart Association 2008 late-breaking scientific sessions. Circ Res. 2009;103:4. Abstract.
135. Thiel WH, Chen B, Hund TJ, Koval OM, Purohit A, Song LS, Mohler PJ, Anderson ME. Proarrhythmic defects in timothy syndrome require calmodulin kinase II. Circulation. 2008;118:2225–2234. [PMC free article] [PubMed]
136. Schwartz PJ, Wolf S. QT interval prolongation as predictor of sudden death in patients with myocardial infarction. Circulation. 1978;57:1074–1077. [PubMed]
137. Crotti L, Monti MC, Insolia R, Peljto A, Goosen A, Brink PA, Greenberg DA, Schwartz PJ, George AL., Jr NOS1AP is a genetic modifier of the long-QT syndrome. Circulation. 2009;120:1657–1663. [PMC free article] [PubMed]
138. Becker ML, Visser LE, Newton-Cheh C, Hofman A, Uitterlinden AG, Witteman JC, Stricker BH. A common NOS1AP genetic polymorphism is associated with increased cardiovascular mortality in users of dihydropyridine calcium channel blockers. Br J Clin Pharmacol. 2009;67:61–67. [PMC free article] [PubMed]
139. Chambers JC, Zhao J, Terracciano CM, Bezzina CR, Zhang W, Kaba R, Navaratnarajah M, Lotlikar A, Sehmi JS, Kooner MK, Deng G, Siedlecka U, Parasramka S, El-Hamamsy I, Wass MN, Dekker LR, de Jong JS, Sternberg MJ, McKenna W, Severs NJ, de Silva R, Wilde AA, Anand P, Yacoub M, Scott J, Elliott P, Wood JN, Kooner JS. Genetic variation in SCN10A influences cardiac conduction. Nat Genet. 2010;42:149–152. [PubMed]
140. Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, Gudjonsson SA, Jonasdottir A, Mathiesen EB, Njølstad I, Nyrnes A, Wilsgaard T, Hald EM, Hveem K, Stoltenberg C, Løchen ML, Kong A, Thorsteinsdottir U, Stefansson K. Several common variants modulate heart rate, PR interval and QRS duration. Nat Genet. 2010;42:117–122. [PubMed]
141. Stein KM. Noninvasive risk stratification for sudden death: signal-averaged electrocardiography, nonsustained ventricular tachycardia, heart rate variability, baroreflex sensitivity, and QRS duration. Prog Cardiovasc Dis. 2008;51:106–117. [PubMed]
142. Otway R, Vandenberg JI, Guo G, Varghese A, Castro ML, Liu J, Zhao J, Bursill JA, Wyse KR, Crotty H, Baddeley O, Walker B, Kuchar D, Thorburn C, Fatkin D. Stretch-sensitive KCNQ1 mutation A link between genetic and environmental factors in the pathogenesis of atrial fibrillation? J Am Coll Cardiol. 2007;49:578–586. [PubMed]
143. Jespersen T, Membrez M, Nicolas CS, Pitard B, Staub O, Olesen SP, Baró I, Abriel H. The KCNQ1 potassium channel is down-regulated by ubiquitylating enzymes of the Nedd4/Nedd4-like family. Cardiovasc Res. 2007;74:64–74. [PubMed]
144. Rougier JS, van Bemmelen MX, Bruce MC, Jespersen T, Gavillet B, Apothéloz F, Cordonier S, Staub O, Rotin D, Abriel H. Molecular determinants of voltage-gated sodium channel regulation by the Nedd4/Nedd4-like proteins. Am J Physiol Cell Physiol. 2005;288:C692–701. [PubMed]
145. Busjahn A, Seebohm G, Maier G, Toliat MR, Nürnberg P, Aydin A, Luft FC, Lang F. Association of the serum and glucocorticoid regulated kinase (sgk1) gene with QT interval. Cell Physiol Biochem. 2004;14:135–142. [PubMed]
146. Luo X, Lin H, Pan Z, Xiao J, Zhang Y, Lu Y, Yang B, Wang Z. Down-regulation of miR-1/miR-133 contributes to re-expression of pacemaker channel genes HCN2 and HCN4 in hypertrophic heart. J Biol Chem. 2008;283:20045–20052. [PubMed]
147. Xiao L, Xiao J, Luo X, Lin H, Wang Z, Nattel S. Feedback remodeling of cardiac potassium current expression: a novel potential mechanism for control of repolarization reserve. Circulation. 2008;118:983–992. [PubMed]
148. Burns CG, Milan DJ, Grande EJ, Rottbauer W, MacRae CA, Fishman MC. High-throughput assay for small molecules that modulate zebrafish embryonic heart rate. Nat Chem Biol. 2005;1:263–264. [PubMed]
149. Milan DJ, Kim AM, Winterfield JR, Jones IL, Pfeufer A, Sanna S, Arking DE, Amsterdam AH, Sabeh KM, Mably JD, Rosenbaum DS, Peterson RT, Chakravarti A, K[umk]aäb S, Roden DM, MacRae CA. Drug-sensitized zebrafish screen identifies multiple genes, including GINS3, as regulators of myocardial repolarization. Circulation. 2009;120:553–559. [PMC free article] [PubMed]
150. Neely GG, Kuba K, Cammarato A, Isobe K, Amann S, Zhang L, Murata M, Elmén L, Gupta V, Arora S, Sarangi R, Dan D, Fujisawa S, Usami T, Xia CP, Keene AC, Alayari NN, Yamakawa H, Elling U, Berger C, Novatchkova M, Koglgruber R, Fukuda K, Nishina H, Isobe M, Pospisilik JA, Imai Y, Pfeufer A, Hicks AA, Pramstaller PP, Subramaniam S, Kimura A, Ocorr K, Bodmer R, Penninger JM. A global in vivo drosophila RNAi screen identifies NOT3 as a conserved regulator of heart function. Cell. 2010;141:142–153. [PMC free article] [PubMed]
151. Braam SR, Tertoolen L, van de Stolpe A, Meyer T, Passier R, Mummery CL. Prediction of drug-induced cardiotoxicity using human embryonic stem cell–derived cardiomyocytes. Stem Cell Res. 2010;4:107–116. [PubMed]
152. Jiang P, Rushing SN, Kong CW, Fu J, Lieu DK, Chan CW, Deng W, Li RA. Electrophysiological properties of human induced pluripotent stem cells. Am J Physiol Cell Physiol. 2010;298:C486–C495. [PubMed]
153. Rudy Y, Silva JR. Computational biology in the study of cardiac ion channels and cell electrophysiology. Q Rev Biophys. 2006;39:57–116. [PMC free article] [PubMed]
154. Diez D, Wheelock AM, Goto S, Haeggstrom JZ, Paulsson-Berne G, Hansson GK, Hedin U, Gabrielsen A, Wheelock CE. The use of network analyses for elucidating mechanisms in cardiovascular disease. Mol Biosyst. 2010;6:289–304. [PubMed]
155. Sampson KJ, Iyer V, Marks AR, Kass RS. A computational model of Purkinje fibre single cell electrophysiology: implications for the long QT syndrome. J Physiol. 2010;588:2643–2655. [PubMed]
156. Kang G, Giovannone SF, Liu N, Liu FY, Zhang J, Priori SG, Fishman GI. Purkinje cells from RyR2 mutant mice are highly arrhythmogenic but responsive to targeted therapy. Circ Res. 2010;107:512–519. [PMC free article] [PubMed]
157. Bogun F, Good E, Reich S, Elmouchi D, Igic P, Tschopp D, Dey S, Wimmer A, Jongnarangsin K, Oral H, Chugh A, Pelosi F, Morady F. Role of Purkinje fibers in post-infarction ventricular tachycardia. J Am Coll Cardiol. 2006;48:2500–2507. [PubMed]
158. Haïssaguerre M, Shah DC, Jaïs P, Shoda M, Kautzner J, Arentz T, Kalushe D, Kadish A, Griffith M, Gaïta F, Yamane T, Garrigue S, Hocini M, Clémenty J. Role of Purkinje conducting system in triggering of idiopathic ventricular fibrillation. Lancet. 2002;359:677–678. [PubMed]
159. Ashcroft FM. From molecule to malady. Nature. 2006;440:440–447. [PubMed]
160. Silva JR, Pan H, Wu D, Nekouzadeh A, Decker KF, Cui J, Baker NA, Sept D, Rudy Y. A multiscale model linking ion-channel molecular dynamics and electrostatics to the cardiac action potential. Proc Natl Acad Sci U S A. 2009;106:11102–11106. [PubMed]
161. Gao Z, Barth AS, DiSilvestre D, Akar FG, Tian Y, Tanskanen A, Kass DA, Winslow RL, Tomaselli GF. Key pathways associated with heart failure development revealed by gene networks correlated with cardiac remodeling. Physiol Genomics. 2008;35:222–230. [PubMed]
162. Laurita KR, Rosenbaum DS. Cellular mechanisms of arrhythmogenic cardiac alternans. Prog Biophys Mol Biol. 2008;97:332–347. [PMC free article] [PubMed]
163. Jalife J. Inward rectifier potassium channels control rotor frequency in ventricular fibrillation. Heart Rhythm. 2009;6:S44–48. [PMC free article] [PubMed]
164. Vaquero M, Calvo D, Jalife J. Cardiac fibrillation: from ion channels to rotors in the human heart. Heart Rhythm. 2008;5:872–879. [PMC free article] [PubMed]
165. Glukhov AV, Fedorov VV, Lou Q, Ravikumar VK, Kalish PW, Schuessler RB, Moazami N, Efimov IR. Transmural dispersion of repolarization in failing and nonfailing human ventricle. Circ Res. 2010;106:981–991. [PMC free article] [PubMed]
166. Narayan SM, Franz MR, Lalani G, Kim J, Sastry A. T-wave alternans, restitution of human action potential duration, and outcome. J Am Coll Cardiol. 2007;50:2385–2392. [PubMed]
167. Vigmond E, Vadakkumpadan F, Gurev V, Arevalo H, Deo M, Plank G, Trayanova N. Towards predictive modelling of the electrophysiology of the heart. Exp Physiol. 2009;94:563–577. [PMC free article] [PubMed]
168. Plank G, Zhou L, Greenstein JL, Cortassa S, Winslow RL, O’Rourke B, Trayanova NA. From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales. Philos Transact A Math Phys Eng Sci. 2008;366:3381–3409. [PMC free article] [PubMed]
169. Jie X, Gurev V, Trayanova N. Mechanisms of mechanically induced spontaneous arrhythmias in acute regional ischemia. Circ Res. 2010;106:185–192. [PMC free article] [PubMed]
170. Kerckhoffs R, Omens JH, McCulloch AD, Mulligan LJ. Ventricular dilation and electrical dyssynchrony synergistically increase regional mechanical non-uniformity but not mechanical dyssynchrony: a computational model. Circ Heart Fail. 2010;3:528–536. [PMC free article] [PubMed]
171. Vadakkumpadan F, Rantner LJ, Tice B, Boyle P, Prassl AJ, Vigmond E, Plank G, Trayanova N. Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electrocardiol. 2009;42:157, e151–e110. [PMC free article] [PubMed]
172. Vadakkumpadan F, Arevalo H, Prassl AJ, Chen J, Kickinger F, Kohl P, Plank G, Trayanova N. Image-based models of cardiac structure in health and disease. Wiley Interdiscip Rev Syst Biol Med. 2010;2:489–506. [PMC free article] [PubMed]
173. Decker KF, Heijman J, Silva JR, Hund TJ, Rudy Y. Properties and ionic mechanisms of action potential adaptation, restitution, and accommodation in canine epicardium. Am J Physiol Heart Circ Physiol. 2009;296:H1017–H1026. [PubMed]
174. Christensen MD, Dun W, Boyden PA, Anderson ME, Mohler PJ, Hund TJ. Oxidized calmodulin kinase II regulates conduction following myocardial infarction: a computational analysis. PLoS Comput Biol. 2009;5:e1000583. [PMC free article] [PubMed]
175. Wang LJ, Sobie EA. Mathematical model of the neonatal mouse ventricular action potential. Am J Physiol Heart Circ Physiol. 2008;294:H2565–H2575. [PMC free article] [PubMed]
176. Mahajan A, Shiferaw Y, Sato D, Baher A, Olcese R, Xie LH, Yang MJ, Chen PS, Restrepo JG, Karma A, Garfinkel A, Qu Z, Weiss JN. A rabbit ventricular action potential model replicating cardiac dynamics at rapid heart rates. Biophys J. 2008;94:392–410. [PubMed]
177. Maleckar MM, Greenstein JL, Giles WR, Trayanova NA. K+ current changes account for the rate dependence of the action potential in the human atrial myocyte. Am J Physiol Heart Circ Physiol. 2009;297:H1398–H1410. [PubMed]
178. Cortassa S, Aon MA, O’Rourke B, Jacques R, Tseng HJ, Marban E, Winslow RL. A computational model integrating electrophysiology, contraction, and mitochondrial bioenergetics in the ventricular myocyte. Biophys J. 2006;91:1564–1589. [PubMed]
179. Cortassa S, O’Rourke B, Winslow RL, Aon MA. Control and regulation of mitochondrial energetics in an integrated model of cardiomyocyte function. Biophys J. 2009;96:2466–2478. [PubMed]
180. Sobie EA, Ramay HR. Excitation-contraction coupling gain in ventricular myocytes: insights from a parsimonious model. J Physiol. 2009;587:1293–1299. [PubMed]
181. Maltsev VA, Lakatta EG. Synergism of coupled subsarcolemmal Ca2+ clocks and sarcolemmal voltage clocks confers robust and flexible pacemaker function in a novel pacemaker cell model. Am J Physiol Heart Circ Physiol. 2009;296:H594–H615. [PubMed]
182. Pastore JM, Girouard SD, Laurita KR, Akar FG, Rosenbaum DS. Mechanism linking T-wave alternans to the genesis of cardiac fibrillation. Circulation. 1999;99:1385–1394. [PubMed]
183. Hayashi H, Shiferaw Y, Sato D, Nihei M, Lin SF, Chen PS, Garfinkel A, Weiss JN, Qu Z. Dynamic origin of spatially discordant alternans in cardiac tissue. Biophys J. 2007;92:448–460. [PubMed]
184. Sato D, Shiferaw Y, Garfinkel A, Weiss JN, Qu Z, Karma A. Spatially discordant alternans in cardiac tissue: role of calcium cycling. Circ Res. 2006;99:520–527. [PubMed]
185. Narayan SM, Bayer JD, Lalani G, Trayanova NA. Action potential dynamics explain arrhythmic vulnerability in human heart failure: a clinical and modeling study implicating abnormal calcium handling. J Am Coll Cardiol. 2008;52:1782–1792. [PMC free article] [PubMed]
186. Rovetti R, Cui X, Garfinkel A, Weiss JN, Qu Z. Spark-induced sparks as a mechanism of intracellular calcium alternans in cardiac myocytes. Circ Res. 2010;106:1582–1591. [PMC free article] [PubMed]
187. Xie Y, Garfinkel A, Weiss JN, Qu Z. Cardiac alternans induced by fibroblast-myocyte coupling: Mechanistic insights from computational models. Am J Physiol Heart Circ Physiol. 2009;297:H775–H784. [PubMed]
188. Dobrzynski H, Li J, Tellez J, Greener ID, Nikolski VP, Wright SE, Parson SH, Jones SA, Lancaster MK, Yamamoto M, Honjo H, Takagishi Y, Kodama I, Efimov IR, Billeter R, Boyett MR. Computer three-dimensional reconstruction of the sinoatrial node. Circulation. 2005;111:846–854. [PubMed]
189. Gurev V, Constantino J, Rice JJ, Trayanova NA. Distribution of electromechanical delay in the heart: insights from a three-dimensional electromechanical model. Biophys J. 2010;99:745–754. [PubMed]
190. Oster HS, Taccardi B, Lux RL, Ershler PR, Rudy Y. Noninvasive electrocardiographic imaging: reconstruction of epicardial potentials, electrograms, and isochrones and localization of single and multiple electrocardiac events. Circulation. 1997;96:1012–1024. [PubMed]
191. Rudy Y. Cardiac repolarization: insights from mathematical modeling and electrocardiographic imaging (ECGI) Heart Rhythm. 2009;6:S49–S55. [PubMed]
192. Ramanathan C, Ghanem RN, Jia P, Ryu K, Rudy Y. Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nat Med. 2004;10:422–428. [PMC free article] [PubMed]
193. Ghosh S, Avari JN, Rhee EK, Woodard PK, Rudy Y. Noninvasive electrocardiographic imaging (ECGI) of a univentricular heart with Wolff-Parkinson-White syndrome. Heart Rhythm. 2008;5:605–608. [PMC free article] [PubMed]
194. Ghosh S, Avari JN, Rhee EK, Woodard PK, Rudy Y. Noninvasive electrocardiographic imaging (ECGI) of epicardial activation before and after catheter ablation of the accessory pathway in a patient with Ebstein anomaly. Heart Rhythm. 2008;5:857–860. [PMC free article] [PubMed]