Protein biomarkers identify patients unlikely to benefit from primary prevention implantable cardioverter defibrillators: findings from the Prospective Observational Study of Implantable Cardioverter Defibrillators (PROSE-ICD)

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Elevated Interleukin-6 Levels Are Associated With an Increased Risk of QTc Interval Prolongation in a Large Cohort of US Veterans

BACKGROUND

Although accumulating data indicate that IL-6 (interleukin-6) can promote heart rate-corrected QT interval (QTc) prolongation via direct and indirect effects on cardiac electrophysiology, current evidence comes from basic investigations and small clinical studies only. Therefore, IL-6 is still largely ignored in the clinical management of long-QT syndrome and related arrhythmias. The aim of this study was to estimate the risk of QTc prolongation associated with elevated IL-6 levels in a large population of unselected subjects.

METHODS AND RESULTS

An observational study using the Veterans Affairs Informatics and Computing Infrastructure was performed. Participants were US veterans who had an ECG and were tested for IL-6. Descriptive statistics and univariate and multivariate regression analyses were performed to study the relationship between IL-6 and QTc prolongation risk. Study population comprised 1085 individuals, 306 showing normal (<5 pg/mL), 376 moderately high (5-25 pg/mL), and 403 high (>25 pg/mL) IL-6 levels. Subjects with elevated IL-6 showed a concentration-dependent increase in the prevalence of QTc prolongation, and those presenting with QTc prolongation exhibited higher circulating IL-6 levels. Stepwise multivariate regression analyses demonstrated that increased IL-6 level was significantly associated with a risk of QTc prolongation up to 2 times the odds of the reference category of QTc (e.g. QTc >470 ms men/480 ms women ms: odds ratio, 2.28 [95% CI, 1.12-4.50] for IL-6 >25 pg/mL) regardless of the underlying cause. Specifically, the mean QTc increase observed in the presence of elevated IL-6 was quantitatively comparable (IL-6 >25 pg/mL:+6.7 ms) to that of major recognized QT-prolonging risk factors, such as hypokalemia and history of myocardial infarction.

CONCLUSIONS

Our data provide evidence that a high circulating IL-6 level is a robust risk factor for QTc prolongation in a large cohort of US veterans, supporting a potentially important arrhythmogenic role for this cytokine in the general population.

Heart transplantation and biomarkers: a review about their usefulness in clinical practice

Advanced heart failure (AdvHF) can only be treated definitively by heart transplantation (HTx), yet problems such right ventricle dysfunction (RVD), rejection, cardiac allograft vasculopathy (CAV), and primary graft dysfunction (PGD) are linked to a poor prognosis. As a result, numerous biomarkers have been investigated in an effort to identify and prevent certain diseases sooner. We looked at both established biomarkers, such as NT-proBNP, hs-troponins, and pro-inflammatory cytokines, and newer ones, such as extracellular vesicles (EVs), donor specific antibodies (DSA), gene expression profile (GEP), donor-derived cell free DNA (dd-cfDNA), microRNA (miRNA), and soluble suppression of tumorigenicity 2 (sST2). These biomarkers are typically linked to complications from HTX. We also highlight the relationships between each biomarker and one or more problems, as well as their applicability in routine clinical practice.

Association of epicardial and intramyocardial fat with ventricular arrhythmias

BACKGROUND

Among patients with ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM), myocardial fibrosis is associated with an increased risk for ventricular arrhythmia (VA). Growing evidence suggests that myocardial fat contributes to ventricular arrhythmogenesis. However, little is known about the volume and distribution of epicardial adipose tissue and intramyocardial fat and their relationship with VAs.

OBJECTIVE

The purpose of this study was to assess the association of contrast-enhanced computed tomography (CE-CT)-derived left ventricular (LV) tissue heterogeneity, epicardial adipose tissue volume, and intramyocardial fat volume with the risk of VA in ICM and NICM patients.

METHODS

Patients enrolled in the PROSE-ICD registry who underwent CE-CT were included. Intramyocardial fat volume (voxels between -180 and -5 Hounsfield units [HU]), epicardial adipose tissue volume (between -200 and -50 HU), and LV tissue heterogeneity were calculated. The primary endpoint was appropriate ICD shocks or sudden arrhythmic death.

RESULTS

Among 98 patients (47 ICM, 51 NICM), LV tissue heterogeneity was associated with VA (odds ratio [OR] 1.10; P = .01), particularly in the ICM cohort. In the NICM subgroup, epicardial adipose tissue and intramyocardial fat volume were associated with VA (OR 1.11, P = .01; and OR = 1.21, P = .01, respectively) but not in the ICM patients (OR 0.92, P =.22; and OR = 0.96, P =.19, respectively).

CONCLUSION

In ICM patients, increased fat distribution heterogeneity is associated with VA. In NICM patients, an increased volume of intramyocardial fat and epicardial adipose tissue is associated with a higher risk for VA. Our findings suggest that fat's contribution to VAs depends on the underlying substrate.

Risk of Arrhythmic Death in Patients With Nonischemic Cardiomyopathy: JACC Review Topic of the Week

Nonischemic cardiomyopathy (NICM) is common and patients are at significant risk for early mortality secondary to ventricular arrhythmias. Current guidelines recommend implantable cardioverter-defibrillator (ICD) therapy to decrease sudden cardiac death (SCD) in patients with heart failure and reduced left ventricular ejection fraction. However, in randomized clinical trials comprised solely of patients with NICM, primary prevention ICDs did not confer significant mortality benefit. Moreover, left ventricular ejection fraction has limited sensitivity and specificity for predicting SCD. Therefore, precise risk stratification algorithms are needed to define those at the highest risk of SCD. This review examines mechanisms of sudden arrhythmic death in patients with NICM, discusses the role of ICD therapy and treatment of heart failure for prevention of SCD in patients with NICM, examines the role of cardiac magnetic resonance imaging and computational modeling for SCD risk stratification, and proposes new strategies to guide future clinical trials on SCD risk assessment in patients with NICM.

Admission triglyceride-glucose index is predictor of long-term mortality and appropriate implantable cardiac defibrillator therapy in patients with heart failure

Background: In this study, the main aim was to evaluate the relation of the triglyceride-glucose (TyG) index to long-term mortality and proper shock therapy in patients with an implantable cardiac defibrillator (ICD) implanted for heart failure with reduced ejection fraction. Methods: This retrospective study group consisted of 773 patients treated with ICD for heart failure with reduced ejection fraction. The long-term prognostic effect of the TyG index among tertiles was evaluated regarding mortality and appropriate ICD therapy. Results: In the adjusted model, the mortality rates were 14.0% (hazard ratio: 2.24; 95% CI: 1.42-6.88) in tertile 2 and 23.3% (hazard ratio: 3.88; 95% CI: 1.84-14.38) in tertile 3. Conclusion: The TyG index was found to be an independent predictive marker for both long-term mortality and appropriate ICD therapy.

Role of N-terminal pro-B type natriuretic peptide as a predictor of poor outcomes in patients with HFrEF receiving primary prevention implantable cardioverter-defibrillator therapy: a systematic review and dose-response meta-analysis

INTRODUCTION

Several studies have demonstrated that combining left ventricular ejection fraction and New York Heart Association functional class is insufficient for predicting risk of appropriate implantable cardioverter-defibrillator (ICD) shock in primary prevention candidates. Hence, our aim was to assess the relationship between N-terminal pro-B type natriuretic peptide (NT-pro BNP) along with appropriate ICD shock and all-cause mortality in order to improve the stratification process of patients with heart failure with reduced ejection fraction (HFrEF) being considered for primary preventive ICD therapy.

METHODS

A systematic literature search from several databases was conducted up until 9 June 2022. Studies were eligible if they investigated the relationship of NT-pro BNP with all-cause mortality and appropriate ICD shock.

RESULTS

This meta-analysis comprised nine studies with a total of 5117 participants. Our study revealed that high levels of NT-pro BNP were associated with all-cause mortality (HR=2.12 (95% CI=1.53 to 2.93); p<0.001, I²=78.1%, p<0.001 for heterogeneity) and appropriate ICD shock (HR=1.71 (95% CI=1.18 to 2.49); p<0.001, I²=43.4%, p=0.102 for heterogeneity). The adjusted HR for all-cause mortality and appropriate ICD shock increased by approximately 3% and 5%, respectively per 100 pg/mL increment pursuant to concentration-response model (P_{non-linearity} <0.001). The curves became steeper after NT-pro BNP reached its inflection point (3000 pg/mL).

CONCLUSION

A positive concentration-dependent association between elevated NT-pro BNP levels along with the risk of all-cause mortality and appropriate ICD shock was found in patients with HFrEF with ICD.

PROSPERO REGISTRATION NUMBER

CRD42022339285.

Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed.

Association of Left Ventricular Tissue Heterogeneity and Intramyocardial Fat on Computed Tomography with Ventricular Arrhythmias in Ischemic Cardiomyopathy

Background

Gray zone, a measure of tissue heterogeneity on late gadolinium enhanced–cardiac magnetic resonance (LGE-CMR) imaging, has been shown to predict ventricular arrhythmias (VAs) in ischemic cardiomyopathy (ICM) patients. However, no studies have described whether left ventricular (LV) tissue heterogeneity and intramyocardial fat mass on contrast-enhanced computed tomography (CE-CT), which provides greater spatial resolution, is useful for assessing the risk of VAs in ICM patients with LV systolic dysfunction and no previous VAs.

Objective

The purpose of this proof-of-concept study was to determine the feasibility of measuring global LV tissue heterogeneity and intramyocardial fat mass by CE-CT for predicting the risk of VAs in ICM patients with LV systolic dysfunction and no previous history of VAs.

Methods

Patients with left ventricular ejection fraction ≤35% and no previous VAs were enrolled in a prospective, observational registry and underwent LGE-CMR. From this cohort, patients with ICM who additionally received CE-CT were included in the present analysis. Gray zone on LGE-CMR was defined as myocardium with signal intensity (SI) > peak SI of healthy myocardium but <50% maximal SI. Tissue heterogeneity on CE-CT was defined as the standard deviation of the Hounsfield unit image gradients (HU/mm) within the myocardium. Intramyocardial fat on CE-CT was identified as regions of image pixels between –180 and –5 HU. The primary outcome was VAs, defined as appropriate implantable cardioverter-defibrillator shock or sudden arrhythmic death.

Results

The study consisted of 47 ICM patients, 13 (27.7%) of whom experienced VA events during mean follow-up of 5.6 ± 3.4 years. Increasing tissue heterogeneity (per HU/mm) was significantly associated with VAs after multivariable adjustment, including for gray zone (odds ratio [OR] 1.22; P = .019). Consistently, patients with tissue heterogeneity values greater than or equal to the median (≥22.2 HU/mm) had >13-fold significantly increased risk of VA events, relative to patients with values lower than the median, after multivariable adjustment that included gray zone (OR 13.13; P = .028). The addition of tissue heterogeneity to gray zone improved prediction of VAs (area under receiver operating characteristic curve increased from 0.815 to 0.876). No association was found between intramyocardial fat mass on CE-CT and VAs (OR 1.00; P = .989).

Conclusion

In ICM patients, CE-CT–derived LV tissue heterogeneity was independently associated with VAs and may represent a novel marker useful for risk stratification.

Evaluating systemic immune-inflammation index in patients with implantable cardioverter defibrillator for heart failure with reduced ejection fraction

BACKGROUND

Pro-inflammatory pathways play an important role in the follow-ups of patients with intracardiac defibrillators (ICDs) for heart failure (HF) reduced with ejection fraction (HFrEF). A newly defined index - the systemic immune-inflammation index (SII)-has recently been reported to have prognostic value in patients with cardiovascular disease. This study's aim is to evaluate the SII value regarding its association with long-term mortality and appropriate ICD therapy during a 10-year follow-up.

METHODS

This retrospective study included 1011 patients with ICD for HFrEF. The SII was calculated as the neutrophil-to-lymphocyte ratio × total platelet count in the peripheral blood. The study population was divided into two groups according to the SII's optimal cut-off value to predict long-term mortality. The long-term prognostic impact of SII on these patients was evaluated regarding mortality and appropriate ICD therapy.

RESULTS

The patients with a higher SII (≥1119) had significantly higher long-term mortality and appropriate ICD therapy rates. After adjustment for all confounding factors, the long-term mortality rate was 5.1 for a higher SII. (95% CI: 2.9-8.1). The long-term appropriate ICD therapy rate was 2.0 for a higher SII (95% CI: 1.4-3.0).

CONCLUSION

SII may be an independent predictive marker for both long-term mortality and appropriate ICD therapy in patients with HFrEF.

Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy

Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.

Markers of Myocardial Stress, Myocardial Injury, and Subclinical Inflammation and the Risk of Sudden Death

BACKGROUND

The majority of sudden cardiac deaths (SCDs) occur in low-risk populations often as the first manifestation of cardiovascular disease (CVD). Biomarkers are screening tools that may identify subclinical cardiovascular disease and those at elevated risk for SCD. We aimed to determine whether the total to high-density lipoprotein cholesterol ratio, high-sensitivity cardiac troponin I, NT-proBNP (N-terminal pro-B-type natriuretic peptide), or high-sensitivity C-reactive protein individually or in combination could identify individuals at higher SCD risk in large, free-living populations with and without cardiovascular disease.

METHODS

We performed a nested case-control study within 6 prospective cohort studies using 565 SCD cases matched to 1090 controls (1:2) by age, sex, ethnicity, smoking status, and presence of cardiovascular disease.

RESULTS

The median study follow-up time until SCD was 11.3 years. When examined as quartiles or continuous variables in conditional logistic regression models, each of the biomarkers was significantly and independently associated with SCD risk after mutually controlling for cardiac risk factors and other biomarkers. The mutually adjusted odds ratios for the top compared with the bottom quartile were 1.90 (95% CI, 1.30-2.76) for total to high-density lipoprotein cholesterol ratio, 2.59 (95% CI, 1.76-3.83) for high-sensitivity cardiac troponin I, 1.65 (95% CI, 1.12-2.44) for NT-proBNP, and 1.65 (95% CI, 1.13-2.41) for high-sensitivity C-reactive protein. A biomarker score that awarded 1 point when the concentration of any of those 4 biomarkers was in the top quartile (score range, 0-4) was strongly associated with SCD, with an adjusted odds ratio of 1.56 (95% CI, 1.37-1.77) per 1-unit increase in the score.

CONCLUSIONS

Widely available measures of lipids, subclinical myocardial injury, myocardial strain, and vascular inflammation show significant independent associations with SCD risk in apparently low-risk populations. In combination, these measures may have utility to identify individuals at risk for SCD.

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

BACKGROUND

Despite the promise of machine learning (ML) to inform individualized medical care, the clinical utility of ML in medicine has been limited by the minimal interpretability and black box nature of these algorithms.

OBJECTIVE

The study aimed to demonstrate a general and simple framework for generating clinically relevant and interpretable visualizations of black box predictions to aid in the clinical translation of ML.

METHODS

To obtain improved transparency of ML, simplified models and visual displays can be generated using common methods from clinical practice such as decision trees and effect plots. We illustrated the approach based on postprocessing of ML predictions, in this case random forest predictions, and applied the method to data from the Left Ventricular (LV) Structural Predictors of Sudden Cardiac Death (SCD) Registry for individualized risk prediction of SCD, a leading cause of death.

RESULTS

With the LV Structural Predictors of SCD Registry data, SCD risk predictions are obtained from a random forest algorithm that identifies the most important predictors, nonlinearities, and interactions among a large number of variables while naturally accounting for missing data. The black box predictions are postprocessed using classification and regression trees into a clinically relevant and interpretable visualization. The method also quantifies the relative importance of an individual or a combination of predictors. Several risk factors (heart failure hospitalization, cardiac magnetic resonance imaging indices, and serum concentration of systemic inflammation) can be clearly visualized as branch points of a decision tree to discriminate between low-, intermediate-, and high-risk patients.

CONCLUSIONS

Through a clinically important example, we illustrate a general and simple approach to increase the clinical translation of ML through clinician-tailored visual displays of results from black box algorithms. We illustrate this general model-agnostic framework by applying it to SCD risk prediction. Although we illustrate the methods using SCD prediction with random forest, the methods presented are applicable more broadly to improving the clinical translation of ML, regardless of the specific ML algorithm or clinical application. As any trained predictive model can be summarized in this manner to a prespecified level of precision, we encourage the use of simplified visual displays as an adjunct to the complex predictive model. Overall, this framework can allow clinicians to peek inside the black box and develop a deeper understanding of the most important features from a model to gain trust in the predictions and confidence in applying them to clinical care.

[Possibilities of modern biomarkers for assessing the risk of developing ventricular tachyarrhythmias and sudden cardiac death in patients with chronic heart failure]

Current biomarkers allow diagnosing a wide array of pathological processes and evaluating effects of therapies and prognosis for cardiological patients. This review focuses on a possibility of using N-terminal pro-brain natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity 2 (sST2), galectin-3, and other biomarkers in patients with chronic heart failure for evaluating the risk of life-threatening ventricular tachyarrhythmias and sudden cardiac death.

Classic and Novel Biomarkers as Potential Predictors of Ventricular Arrhythmias and Sudden Cardiac Death

Sudden cardiac death (SCD), most often induced by ventricular arrhythmias, is one of the main reasons for cardiovascular-related mortality. While coronary artery disease remains the leading cause of SCD, other pathologies like cardiomyopathies and, especially in the younger population, genetic disorders, are linked to arrhythmia-related mortality. Despite many efforts to enhance the efficiency of risk-stratification strategies, effective tools for risk assessment are still missing. Biomarkers have a major impact on clinical practice in various cardiac pathologies. While classic biomarkers like brain natriuretic peptide (BNP) and troponins are integrated into daily clinical practice, inflammatory biomarkers may also be helpful for risk assessment. Indeed, several trials investigated their application for the prediction of arrhythmic events indicating promising results. Furthermore, in recent years, active research efforts have brought forward an increasingly large number of “novel and alternative” candidate markers of various pathophysiological origins. Investigations of these promising biological compounds have revealed encouraging results when evaluating the prediction of arrhythmic events. To elucidate this issue, we review current literature dealing with this topic. We highlight the potential of “classic” but also “novel” biomarkers as promising tools for arrhythmia prediction, which in the future might be integrated into clinical practice.

Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis

Background

Clinical research and medical practice can be advanced through the prediction of an individual’s health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary.

Methods

We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance.

Results

We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment.

Conclusions

RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time.Trial registration: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010

Primary prevention implantable cardioverter-defibrillator and opportunities for sudden cardiac death risk assessment in non-ischaemic cardiomyopathy

Sudden cardiac death (SCD) accounts for approximately one-third of all deaths among patients with non-ischaemic cardiomyopathy (NICM). Implantable cardioverter-defibrillator (ICD) therapy has been the primary intervention for managing individuals at high risk for SCD. However, individual ICD trials in the NICM population have failed to demonstrate a mortality benefit with prophylactic ICD implantation. Current guidelines recommend ICD implantation in NICM patients with symptomatic heart failure and a left ventricular ≤35% and are based on meta-analyses of multiple trials that span three decades and include the recent Danish Study to Assess the Efficacy of ICDs in Patients with Non-ischaemic Heart Failure on Mortality (DANISH) trial. These pooled analyses report a significant reduction in all-cause mortality with ICD implantation compared with medical therapy alone. In addition, each of these trials has demonstrated consistently a reduction in the risk of SCD compared with medical therapy alone. As a result, a refined approach of risk stratification that selects patients at the highest risk for SCD may lead to a significant improvement in ICD efficacy. In this clinical review, we first discuss the evolution of clinical trials that have evaluated ICDs in the NICM population. We then highlight some key markers of arrhythmia risk that hold promise in personalizing risk stratification for SCD.

Sudden cardiac death in nonischemic cardiomyopathy

Sudden cardiac death (SCD) is a major cause of mortality in patients with nonischemic cardiomyopathy (NICM). Identifying patients who are at highest risk for SCD is an ongoing challenge. At present, guidelines recommend the use of an implantable cardioverter-defibrillator (ICD) in patients with NICM with a reduced left ventricular ejection fraction (LVEF) and heart failure (HF) symptoms. Some recent data, however, suggest that ICDs may not increase longevity in this population. Conversely, community-based studies have demonstrated that many at-risk individuals who may benefit from ICD therapy remain unprotected. Current recommendations for ICD implantation are continually debated, justifying comprehensive individualized risk assessment. Various promising techniques for further risk stratification are under evaluation, including cardiac magnetic resonance imaging, electrocardiographic assessment of electrical instability, and genetic testing. However, none of these strategies has been fully adapted into guidelines. Hence, clinical risk stratification practice today depends on LVEF and HF symptoms, which have poor sensitivity and specificity for predicting SCD risk.

Baroreflex sensitivity assessed with the sequence method is associated with ventricular arrhythmias in patients implanted with a defibrillator for the primary prevention of sudden cardiac death

BACKGROUND

Left ventricular ejection fraction lacks accuracy in predicting sudden cardiac death, resulting in unnecessary implantation of cardioverter defibrillators for the primary prevention of sudden cardiac death. Baroreflex sensitivity could help to stratify patients at risk of ventricular arrhythmia.

AIM

To assess the association between cardiac baroreflex sensitivity and ventricular arrhythmias in patients implanted with an implantable cardioverter defibrillator for the primary prevention of sudden cardiac death after myocardial infarction.

METHODS

This case-control single-centre study took place between 2015 and 2016. Cases (n=10) had experienced ventricular arrhythmias treated by the implantable cardioverter defibrillator in the previous 3 years; controls (n=22) had no arrhythmia during the same period. Baroreflex sensitivity was assessed using the temporal sequence method (mean slope) and cross-spectral analysis (low-frequency gain and high-frequency gain).

RESULTS

The mean age was 65 years; 94% of the patients were men. 24-hour Holter electrocardiogram autonomous nervous system variables, left ventricular ejection fraction and N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) concentration did not differ between cases and controls. The mean slope was lower in cases than in controls (8 vs. 15ms/mmHg [P=0.009] in the supine position; 7 vs. 12ms/mmHg [P=0.038] in the standing position). The mean slope in the supine position was still significantly different between groups after adjustment for age, left ventricular ejection fraction and NT-proBNP (P=0.03). By comparison, low-frequency gain and high-frequency gain did not differ between groups in either the supine or the standing position.

CONCLUSION

Patients with ventricular arrhythmias had a lower mean slope compared with those who were free of arrhythmia. A prospective study is needed to confirm this association.

Device Therapies Among Patients Receiving Primary Prevention Implantable Cardioverter-Defibrillators in the Cardiovascular Research Network

BACKGROUND

Primary prevention implantable cardioverter-defibrillators (ICDs) reduce mortality in selected patients with left ventricular systolic dysfunction by delivering therapies (antitachycardia pacing or shocks) to terminate potentially lethal arrhythmias; inappropriate therapies also occur. We assessed device therapies among adults receiving primary prevention ICDs in 7 healthcare systems.

METHODS AND RESULTS

We linked medical record data, adjudicated device therapies, and the National Cardiovascular Data Registry ICD Registry. Survival analysis evaluated therapy probability and predictors after ICD implant from 2006 to 2009, with attention to Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups: left ventricular ejection fraction, 31% to 35%; nonischemic cardiomyopathy <9 months' duration; and New York Heart Association class IV heart failure with cardiac resynchronization therapy defibrillator. Among 2540 patients, 35% were <65 years old, 26% were women, and 59% were white. During 27 (median) months, 738 (29%) received ≥1 therapy. Three-year therapy risk was 36% (appropriate, 24%; inappropriate, 12%). Appropriate therapy was more common in men (adjusted hazard ratio [HR], 1.84; 95% confidence interval [CI], 1.43-2.35). Inappropriate therapy was more common in patients with atrial fibrillation (adjusted HR, 2.20; 95% CI, 1.68-2.87), but less common among patients ≥65 years old versus younger (adjusted HR, 0.72; 95% CI, 0.54-0.95) and in recent implants (eg, in 2009 versus 2006; adjusted HR, 0.66; 95% CI, 0.46-0.95). In Centers for Medicare and Medicaid Services Coverage With Evidence Development analysis, inappropriate therapy was less common with cardiac resynchronization therapy defibrillator versus single chamber (adjusted HR, 0.55; 95% CI, 0.36-0.84); therapy risk did not otherwise differ for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups.

CONCLUSIONS

In this community cohort of primary prevention patients receiving ICD, therapy delivery varied across demographic and clinical characteristics, but did not differ meaningfully for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups.

Sudden Cardiac Death in Ischemic Heart Disease: Pathophysiology and Risk Stratification

Sudden cardiac death (SCD) accounts for approximately 360,000 deaths annually in the United States. Ischemic heart disease is the major cause of death in the general adult population. SCD can be due to arrhythmic or nonarrhythmic cardiac causes. Arrhythmic SCD may be caused by ventricular tachyarrhythmia or pulseless electrical activity/asystole. This article reviews the most recent pathophysiology and risk stratification strategies for SCD, emphasizing electrophysiologic surrogates of conduction disorder, dispersion of repolarization, and autonomic imbalance. Factors that modify arrhythmic death are addressed.

Red cell distribution width predicts death and appropriate therapy in patients with implantable cardioverter defibrillator: A simple measurement with prognostic value in a variety of diseases, may help in better selection of patients who will benefit the most from this device

BACKGROUND

Red cell distribution width (RDW) is a measure of the size variation of erythrocytes. Its prognostic value has been described in a variety of cardiac and noncardiac diseases. Implantable cardioverter defibrillator (ICD) is useful in preventing sudden cardiac death in high-risk patients, but many of these patients continue to survive without needing ICD therapy. We sought to examine whether RDW, with its prognostic values, can benefit in risk stratification of patients with ICD by predicting death and ICD therapy, and thus help in the selection of patients who will benefit the most from ICD, and minimizing its implantation in others at low risk of death and arrhythmias.

METHODS

In a retrospective study, we enrolled patients with ICD implanted for both primary and secondary prevention of sudden cardiac death. Baseline RDW values, demographics, and clinical characteristics, as well as the occurrence of death or first appropriate ICD therapy in postimplantation follow-up were collected. We examined whether RDW can predict higher-risk ICD-implanted patients prone to death and first appropriate ICD therapy (the combined outcome).

RESULTS

Final population included 432 patients. Compared to others, patients in the upper RDW tertile were older and had more comorbidities and outcomes. In multivariate analysis including RDW, age, gender, and ejection fraction, RDW was the only predictor of the combined outcome.

CONCLUSION

RDW may be useful in risk stratification of patients selected for ICD implantation. But larger prospective randomized trials are needed.

Cardiac troponin T as a predictor of cardiac death in patients with left ventricular dysfunction

BACKGROUND

Cardiac troponin T (cTnT) has been reported to be associated with cardiac mortality. In the present study, we evaluated the role of routine assessment of cTnT as a predictor of future cardiac death in patients with left ventricular (LV) dysfunction.

METHODS

Patients who were eligible for prophylactic implantable cardioverter defibrillator (ICD) were included from cardiac catheterization database. Inclusion criteria were patients with LV ejection fraction of ≤ 35% and with New York Heart Association (NYHA) ≥class II. Exclusion criteria were patients with acute coronary syndrome, ICD for secondary prevention, NYHA class IV, and lack of data. The final study patients were divided into the following three groups in accordance with two quartile points of serum cTnT levels: low cTnT, intermediate cTnT, and high cTnT groups. The primary endpoint of this study was cardiac death.

RESULTS

A total of 70 patients were included (mean age, 62±13 years; male individuals, 56; ischemic, 36; and non-ischemic, 34). During the observation period of 2.2 years, cardiac death was observed in 17 patients (fatal arrhythmic event, 9; heart failure, 7; myocardial infarction, 1). In the Kaplan-Meier analysis, the high cTnT group showed the highest risk among all the groups (p<0.001). Even in sub-analyses for ischemic and non-ischemic patients, the results were the same, and the high cTnT group showed the highest event rate (p<0.05). In contrast, no cardiac death was observed in the low cTnT group.

CONCLUSION

The cTnT levels in a stable state were associated with cardiac death in patients with LV dysfunction, even in those with non-ischemic diseases.

New York Heart Association class and the survival benefit from primary prevention implantable cardioverter defibrillators: A pooled analysis of 4 randomized controlled trials

BACKGROUND

Primary prevention implantable cardioverter defibrillator (ICD) reduce all-cause mortality by reducing sudden cardiac death. There are conflicting data regarding whether patients with more advanced heart failure derive ICD benefit owing to the competing risk of nonsudden death.

METHODS

We performed a patient-level meta-analysis of New York Heart Association (NYHA) class II/III heart failure patients (left ventricular ejection fraction ≤35%) from 4 primary prevention ICD trials (MADIT-I, MADIT-II, DEFINITE, SCD-HeFT). Bayesian-Weibull survival regression models were used to assess the impact of NYHA class on the relationship between ICD use and mortality.

RESULTS

Of the 2,763 patients who met study criteria, 68% (n=1,867) were NYHA II and 52% (n=1,435) were randomized to an ICD. In a multivariable model including all study patients, the ICD reduced mortality (hazard ratio [HR] 0.65, 95% posterior credibility interval [PCI]) 0.40-0.99). The interaction between NYHA class and the ICD on mortality was significant (posterior probability of no interaction=.036). In models including an interaction term for the NYHA class and ICD, the ICD reduced mortality among NYHA class II patients (HR 0.55, PCI 0.35-0.85), and the point estimate suggested reduced mortality in NYHA class III patients (HR 0.76, PCI 0.48-1.24), although this was not statistically significant.

CONCLUSIONS

Primary prevention ICDs reduce mortality in NYHA class II patients and trend toward reducing mortality in the heterogeneous group of NYHA class III patients. Improved risk stratification tools are required to guide patient selection and shared decision making among NYHA class III primary prevention ICD candidates.

Predicting appropriate shocks in patients with heart failure: Patient level meta-analysis from SCD-HeFT and MADIT II

BACKGROUND

No precise tools exist to predict appropriate shocks in patients with a primary prevention ICD. We sought to identify characteristics predictive of appropriate shocks in patients with a primary prevention implantable cardioverter defibrillator (ICD).

METHODS

Using patient-level data from the Multicenter Automatic Defibrillator Implantation Trial II (MADIT II) and the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), we identified patients with any appropriate shock. Clinical and demographic variables were included in a logistic regression model to predict appropriate shocks.

RESULTS

There were 1,463 patients randomized to an ICD, and 285 (19%) had ≥1 appropriate shock over a median follow-up of 2.59 years. Compared with patients without appropriate ICD shocks, patients who received any appropriate shock tended to have more severe heart failure. In a multiple logistic regression model, predictors of appropriate shocks included NYHA class (NYHA II vs. I: OR 1.65, 95% CI 1.07-2.55; NYHA III vs. I: OR 1.74, 95% CI 1.10-2.76), lower LVEF (per 1% change) (OR 1.04, 95% CI 1.02-1.06), absence of beta-blocker therapy (OR 1.61, 95% CI 1.23-2.12), and single chamber ICD (OR 1.67, 95% CI 1.13-2.45).

CONCLUSION

In this meta-analysis of patient level data from MADIT-II and SCD-HeFT, higher NYHA class, lower LVEF, no beta-blocker therapy, and single chamber ICD (vs. dual chamber) were significant predictors of appropriate shocks.

Arrhythmic risk stratification in heart failure: Time for the next step?

BACKGROUND

Primary prevention of sudden cardiac death by means of implantable cardioverter-defibrillators constitutes the holy grail of arrhythmology. However, current risk stratification algorithms lead to suboptimal outcomes, by both allocating ICDs to patients not deriving any meaningful survival benefit and withholding them from those erroneously considered as low-risk for arrhythmic mortality.

METHODS

In the present review article we will attempt to present shortcomings of contemporary guidelines regarding sudden death prevention in ischemic and dilated cardiomyopathy patients and present available data suggesting encouraging results following implementation of multifactorial approaches, by using multiple modalities, both noninvasive and invasive. Invasive electrophysiological testing, namely programmed ventricular stimulation, will be discussed in greater length to highlight both its potential usefulness and currently ongoing multicenter studies aiming to provide evidence necessary to make the next step in sudden death risk stratification.

RESULTS

Promising findings have been reported by multiple study groups regarding novel strategies for both negative selection of low and positive selection of relatively preserved ejection fraction patients as candidates for ICD implantation.

CONCLUSIONS

The era of ejection fraction as the sole risk stratifier for arrhythmic risk in heart failure appears to be drawing to an end, especially if current underway large studies validate previous findings.

Neutrophil to lymphocyte ratio predicts appropriate therapy in idiopathic dilated cardiomyopathy patients with primary prevention implantable cardioverter defibrillator

OBJECTIVES

To investigate whether an inflammatory marker of neutrophil to lymphocyte ratio (NLR) predicts appropriate implantable cardioverter defibrillator (ICD) therapy (shock or anti tachycardia pacing) in idiopathic dilated cardiomyopathy (IDC) patients.

METHODS

We retrospectively examined IDC patients (mean age: 58.3 ± 11.8 years, 81.5% male) with ICD who admitted to outpatient clinic for pacemaker control at 2 tertiary care hospitals in Ankara and Edirne, Turkey from January 2013-2015. All ICDs were implanted for primary prevention. Hematological and biochemical parameters were measured prior procedure. Results: Over a median follow-up period of 43 months (Range 7-125), 68 (33.1%) patients experienced appropriate ICD therapy. The NLR was increased in patients that received appropriate therapy (4.39 ± 2.94 versus 2.96 ± 1.97, p less than 0.001).To identify independent risk factors for appropriate therapy, a multivariate linear regression model was conducted and age (β=0.163, p=0.013), fasting glucose (β=0.158, p=0.017), C-reactive protein (CRP) (β=0.289, p less than 0.001) and NLR (β=0.212, p less than 0.008) were found to be independent risk factors for appropriate ICD therapy. Conclusions: Before ICD implantation by using NLR and CRP, arrhythmic episodes may be predictable and better antiarrhythmic medical therapy optimization may protect these IDC patients from unwanted events.

WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data

The electrocardiogram (ECG) is the most commonly collected data in cardiovascular research because of the ease with which it can be measured and because changes in ECG waveforms reflect underlying aspects of heart disease. Accessed through a browser, WaveformECG is an open source platform supporting interactive analysis, visualization, and annotation of ECGs.

Serum-Based Oxylipins Are Associated with Outcomes in Primary Prevention Implantable Cardioverter Defibrillator Patients

Introduction

Individuals with systolic heart failure are at risk of ventricular arrhythmias and all-cause mortality. Little is known regarding the mechanisms underlying these events. We sought to better understand if oxylipins, a diverse class of lipid metabolites derived from the oxidation of polyunsaturated fatty acids, were associated with these outcomes in recipients of primary prevention implantable cardioverter defibrillators (ICDs).

Methods

Among 479 individuals from the PROSE-ICD study, baseline serum were analyzed and quantitatively profiled for 35 known biologically relevant oxylipin metabolites. Associations with ICD shocks for ventricular arrhythmias and all-cause mortality were evaluated using Cox proportional hazards models.

Results

Six oxylipins, 17,18-DiHETE (HR = 0.83, 95% CI 0.70 to 0.99 per SD change in oxylipin level), 19,20-DiHDPA (HR = 0.79, 95% CI 0.63 to 0.98), 5,6-DiHETrE (HR = 0.73, 95% CI 0.58 to 0.91), 8,9-DiHETrE (HR = 0.76, 95% CI 0.62 to 0.95), 9,10-DiHOME (HR = 0.81, 95% CI 0.65 to 1.00), and PGF_1α (HR = 1.33, 95% CI 1.04 to 1.71) were associated with the risk of appropriate ICD shock after multivariate adjustment for clinical factors. Additionally, 4 oxylipin-to-precursor ratios, 15S-HEPE / FA (20:5-ω3), 17,18-DiHETE / FA (20:5-ω3), 19,20-DiHDPA / FA (20:5-ω3), and 5S-HEPE / FA (20:5-ω3) were positively associated with the risk of all-cause mortality.

Conclusion

In a prospective cohort of patients with primary prevention ICDs, we identified several novel oxylipin markers that were associated with appropriate shock and mortality using metabolic profiling techniques. These findings may provide new insight into the potential biologic pathways leading to adverse events in this patient population.

New approaches to predicting the risk of sudden death

In this review article, we will explore some of the contemporary methods for predicting sudden cardiac death (SCD). These include experimental methods yet to be adopted in the clinical setting, and methods that have been extrapolated from observational data in those with a history of SCD. We will discuss how these relate to the different aetiologies and disease processes. We will also explore how these may be used in the clinical setting to decide on management.

Principles and techniques of imaging in identifying the substrate of ventricular arrhythmia

Life-threatening ventricular arrhythmias (VA) are a major cause of death in patients with cardiomyopathy. To date, impaired left ventricular ejection fraction remains the primary criterion for implantable cardioverter-defibrillator therapy to prevent sudden cardiac death. In recent years, however, advanced imaging techniques such as nuclear imaging, cardiac magnetic resonance imaging, and computed tomography have allowed for a more detailed evaluation of the underlying substrate of VA. These imaging modalities have emerged as a promising approach to assess the risk of sudden cardiac death. In addition, non-invasive identification of the critical sites of arrhythmias may guide ablation therapy. Typical anatomical substrates that can be evaluated by multiple advanced imaging techniques include perfusion abnormalities, scar and its border zone, and sympathetic denervation. Understanding the principles and techniques of different imaging modalities is essential to gain more insight in their role in identifying the arrhythmic substrate. The current review describes the principles of currently available imaging techniques to identify the substrate of VA.

Non-invasive imaging to identify susceptibility for ventricular arrhythmias in ischaemic left ventricular dysfunction

OBJECTIVE

Non-invasive imaging of myocardial perfusion, sympathetic denervation and scar size contribute to enhanced risk prediction of ventricular arrhythmias (VA). Some of these imaging parameters, however, may be intertwined as they are based on similar pathophysiology. The aim of this study was to assess the predictive role of myocardial perfusion, sympathetic denervation and scar size on the inducibility of VA in patients with ischaemic cardiomyopathy in a head-to-head fashion.

METHODS

52 patients with ischaemic heart disease and left ventricular ejection fraction (LVEF) ≤35%, referred for primary prevention implantable cardioverter-defibrillator (ICD) implantation, were included. Late gadolinium-enhanced cardiovascular MRI was performed to assess LV volumes, function and scar size. Using [(15)O]H2O and [(11)C]hydroxyephedrine positron emission tomography, both resting and hyperaemic myocardial blood flow (MBF), and sympathetic innervation were assessed. After ICD implantation, an electrophysiological study (EPS) was performed and was considered positive in case of sustained VA.

RESULTS

Patients with a positive EPS (n=25) showed more severely impaired global hyperaemic MBF (p=0.003), larger sympathetic denervation size (p=0.048) and tended to have larger scar size (p=0.07) and perfusion defect size (p=0.06) compared with EPS-negative patients (n=27). No differences were observed in LV volumes, LVEF and innervation-perfusion mismatch size. Multivariable analysis revealed that impaired hyperaemic MBF was the single best independent predictor for VA inducibility (OR 0.78, 95% CI 0.65 to 0.94, p=0.007). A combination of risk markers did not yield incremental predictive value over hyperaemic MBF alone.

CONCLUSIONS

Of all previously validated approaches to evaluate the arrhythmic substrate, global impaired hyperaemic MBF was the only independent predictor of VA inducibility. Moreover, a combined approach of different imaging variables did not have incremental value.

Troponin levels after ICD implantation with and without defibrillation testing and their predictive value for outcomes: Insights from the SIMPLE trial

BACKGROUND

The Shockless IMPLant Evaluation trial randomized 2500 patients receiving a first implantable cardioverter-defibrillator (ICD)/cardiac resynchronization therapy-defibrillator device to have either defibrillation testing (DT) or no DT. It demonstrated that DT did not improve shock efficacy or reduce mortality.

OBJECTIVE

This prospective substudy evaluated the effect of DT on postoperative troponin levels and their predictive value for total and arrhythmic mortality.

METHODS

Troponin levels were measured between 6 and 24 hours after ICD implantation in 2200 of 2500 patients.

RESULTS

A postoperative serum troponin level above the upper limit of normal (ULN) was more common in patients undergoing DT (n = 509 [46.4%]) than in those not subjected to DT (n = 456 [41.3%]; P = .02). After excluding patients with known preoperative troponin levels above the ULN, consistent findings were observed (42.1% vs 37.5%; P = .04). During a mean follow-up of 3.1 ± 1.0 years, the annual mortality rate was increased in patients with postoperative troponin levels above the ULN (adjusted hazard ratio [HR] 1.43; 95% confidence interval [CI] 1.15-1.76; P = .001) irrespective of DT or no DT. Likewise, patients with elevated troponin levels had a significantly higher risk of arrhythmic death (adjusted HR 1.80; 95% CI 1.23-2.63; P = .002). The rate of first appropriate ICD shock (adjusted HR 0.89; 95% CI 0.71-1.12; P = .32) or failed appropriate shock (adjusted HR 1.02; 95% CI 0.59-1.76; P = .95) was similar in patients with or without troponin elevation.

CONCLUSION

DT at the time of ICD implantation is associated with increased troponin levels, indicating subclinical myocardial injury caused by the procedure. Elevated troponin levels but not DT seem to predict clinical outcomes in ICD recipients.

The spectrum of epidemiology underlying sudden cardiac death

Sudden cardiac death (SCD) from cardiac arrest is a major international public health problem accounting for an estimated 15%-20% of all deaths. Although resuscitation rates are generally improving throughout the world, the majority of individuals who experience a sudden cardiac arrest will not survive. SCD most often develops in older adults with acquired structural heart disease, but it also rarely occurs in the young, where it is more commonly because of inherited disorders. Coronary heart disease is known to be the most common pathology underlying SCD, followed by cardiomyopathies, inherited arrhythmia syndromes, and valvular heart disease. During the past 3 decades, declines in SCD rates have not been as steep as for other causes of coronary heart disease deaths, and there is a growing fraction of SCDs not due to coronary heart disease and ventricular arrhythmias, particularly among certain subsets of the population. The growing heterogeneity of the pathologies and mechanisms underlying SCD present major challenges for SCD prevention, which are magnified further by a frequent lack of recognition of the underlying cardiac condition before death. Multifaceted preventative approaches, which address risk factors in seemingly low-risk and known high-risk populations, will be required to decrease the burden of SCD. In this Compendium, we review the wide-ranging spectrum of epidemiology underlying SCD within both the general population and in high-risk subsets with established cardiac disease placing an emphasis on recent global trends, remaining uncertainties, and potential targeted preventive strategies.