Global Electric Heterogeneity Risk Score for Prediction of Sudden Cardiac Death in the General Population: The Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health (CHS) Studies

Competing risks of monomorphic vs. non-monomorphic ventricular arrhythmias in primary prevention implantable cardioverter-defibrillator recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) study

AIMS

Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors for MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models.

METHODS AND RESULTS

The multicentre retrospective cohort study included 2668 patients (age 63.1 ± 13.0 years; 23% female; 78% white; 43% non-ischaemic cardiomyopathy; left ventricular ejection fraction 28.2 ± 11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and electrocardiogram metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate implantable cardioverter-defibrillator (ICD) therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa [hazard ratio (HR) 1.16; 95% confidence interval (CI) 1.01-1.34], larger SVGel (HR 1.17; 95% CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95% CI 0.63-0.86) and SAIQRST (HR 0.84; 95% CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT [time-dependent area under the receiver operating characteristic curve (ROC(t)AUC) 0.728; 95% CI 0.668-0.788] identified high-risk (> 50%) patients with 75% sensitivity and 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95% CI 0.868-0.962), both satisfactory calibration.

CONCLUSION

We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future randomized controlled trials of prophylactic ventricular tachycardia ablation.

CLINICAL TRIAL REGISTRATION

URL:www.clinicaltrials.gov Unique identifier:NCT03210883.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs

Background

Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.

Objectives

To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.

Methods

An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa.

Results

The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78.

Conclusion

Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning

BACKGROUND

Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug-induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predicting BrS diagnosis.

METHODS AND RESULTS

Consecutive patients who underwent ajmaline testing for BrS following a standardized protocol were included. ECG tracings from baseline and during ajmaline were transformed using wavelet analysis and a deep convolutional neural network was separately trained to (1) recognize and (2) predict BrS type I pattern. The resultant networks are referred to as BrS-Net. A total of 1188 patients were included, of which 361 (30.3%) patients developed BrS type I pattern during ajmaline infusion. When trained and evaluated on ECG tracings during ajmaline, BrS-Net recognized a BrS type I pattern with an AUC-ROC of 0.945 (0.921-0.969) and an AUC-PR of 0.892 (0.815-0.939). When trained and evaluated on ECG tracings at baseline, BrS-Net predicted a BrS type I pattern during ajmaline with an AUC-ROC of 0.805 (0.845-0.736) and an AUC-PR of 0.605 (0.460-0.664).

CONCLUSIONS

BrS-Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS-Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.

The Spatial Ventricular Gradient Is Associated With Inducibility of Ventricular Arrhythmias During Electrophysiology Study

BACKGROUND

Myocardial electrical heterogeneity is critical for normal cardiac electromechanical function, but abnormal or excessive electrical heterogeneity is proarrhythmic. The spatial ventricular gradient (SVG), a vectorcardiographic measure of electrical heterogeneity, has been associated with arrhythmic events during long-term follow-up, but its relationship with short-term inducibility of ventricular arrhythmias (VAs) is unclear.

OBJECTIVE

This study was designed to determine associations between SVG and inducible VAs during electrophysiology study.

METHODS

A retrospective study was conducted of adults without prior sustained VA, cardiac arrest, or implantable cardioverter-defibrillator who underwent ventricular stimulation for evaluation of syncope and nonsustained ventricular tachycardia or for risk stratification before primary prevention implantable cardioverter-defibrillator implantation. The 12-lead electrocardiograms were converted into vectorcardiograms, and SVG magnitude (SVGmag) and direction (azimuth and elevation) were calculated. Odds of inducible VA were regressed by logistic models.

RESULTS

Of 143 patients (median age, 69 years; 80% male; median left ventricular ejection fraction [LVEF], 47%; 52% myocardial infarction), 34 (23.8%) had inducible VAs. Inducible patients had lower median LVEF (38% vs 50%; P < .0001), smaller SVGmag (29.5 vs 39.4 mV·ms; P = .0099), and smaller cosine SVG azimuth (cosSVGaz; 0.64 vs 0.89; P = .0007). When LVEF, SVGmag, and cosSVGaz were dichotomized at their medians, there was a 39-fold increase in adjusted odds (P = .002) between patients with all low LVEF, SVGmag, and cosSVGaz (65% inducible) compared with patients with all high LVEF, SVGmag, and cosSVGaz (4% [n = 1] inducible). After multivariable adjustment, SVGmag, cosSVGaz, and sex but not LVEF or other characteristics remained associated with inducible VAs.

CONCLUSION

Assessment of electrical heterogeneity by SVG, which reflects abnormal electrophysiologic substrate, adds to LVEF and identifies patients at high and low risk of inducible VA at electrophysiology study.

Possible Mechanisms for Adverse Cardiac Events Caused by Exercise-Induced Hypertension in Long-Distance Middle-Aged Runners: A Review

Sudden cardiac death (SCD) is rare among athletes. However, hypertrophic cardiomyopathy is the leading cause of SCD among those <35 years of age. Meanwhile, coronary artery disease (CAD) is the primary SCD cause among those ≥35 years of age. CAD-induced plaque ruptures are believed to be a significant cause of cardiovascular diseases in middle-aged individuals who participate in extreme long-distance running activities such as marathons. A total of 1970 articles related to EIH were identified using search terms. Out of these, 1946 studies were excluded for reasons such as arterial hypertension, exercise-induced pulmonary hypertension, the absence of exercise stress testing (EST), and a lack of relevance to EIH. The study analyzed 24 studies related to both long-distance runners with exercise-induced hypertension (EIH) and the general public. Among these, 11 studies were quasi-experimentally designed studies used in randomized controlled trials (RCTs) on long-distance runners with EIH. Additionally, 12 studies utilized cohort designs, and one study with a quasi-experimental design was conducted among the general population. Recent studies suggest that an imbalance between oxygen demand and supply due to ventricular hypertrophy may be the actual cause of cardiovascular disease, regardless of CAD. Exercising excessively over an extended period can reduce endothelial function and increase arterial stiffness, which in turn increases afterload and leads to an excessive increase in blood pressure during exercise. Exercise-induced hypertension (EIH), which increases the morbidity rate of resting hypertension and is a risk factor for cardio-cerebro-vascular diseases, is more prevalent in middle-aged long-distance runners than in runners from other age groups, and it increases the prevalence of critical arrhythmias, such as atrial fibrillation or ventricular arrhythmias. EIH is associated with angiotensin II activity, and angiotensin II receptor blockers show promising effects in middle-aged runners. Further, guidelines for preventing excessive participation in races and restricting exercise intensity and frequency would be useful. This review identifies EIH as a potential risk factor for cardiovascular diseases and describes how EIH induces SCD.

Risk Prediction for Sudden Cardiac Death in the General Population: A Systematic Review and Meta-Analysis

Objective: Identification of SCD risk is important in the general population from a public health perspective. The objective is to summarize and appraise the available prediction models for the risk of SCD among the general population. Methods: Data were obtained searching six electronic databases and reporting prediction models of SCD risk in the general population. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Results: Out of 8,407 studies identified, fifteen studies were included in the systematic review, while five studies were included in the meta-analysis. The Cox proportional hazards model was used in thirteen studies (96.67%). Study locations were limited to Europe and the United States. Our pooled meta-analyses included four predictors: diabetes mellitus (ES = 2.69, 95%CI: 1.93, 3.76), QRS duration (ES = 1.16, 95%CI: 1.06, 1.26), spatial QRS-T angle (ES = 1.46, 95%CI: 1.27, 1.69) and factional shortening (ES = 1.37, 95%CI: 1.15, 1.64). Conclusion: Risk prediction model may be useful as an adjunct for risk stratification strategies for SCD in the general population. Further studies among people except for white participants and more accessible factors are necessary to explore.

Dynamic electrocardiogram changes are a novel risk marker for sudden cardiac death

BACKGROUND AND AIMS

Electrocardiogram (ECG) abnormalities have been evaluated as static risk markers for sudden cardiac death (SCD), but the potential importance of dynamic ECG remodelling has not been investigated. In this study, the nature and prevalence of dynamic ECG remodelling were studied among individuals who eventually suffered SCD.

METHODS

The study population was drawn from two prospective community-based SCD studies in Oregon (2002, discovery cohort) and California, USA (2015, validation cohort). For this present sub-study, 231 discovery cases (2015-17) and 203 validation cases (2015-21) with ≥2 archived pre-SCD ECGs were ascertained and were matched to 234 discovery and 203 validation controls based on age, sex, and duration between the ECGs. Dynamic ECG remodelling was measured as progression of a previously validated cumulative six-variable ECG electrical risk score.

RESULTS

Oregon SCD cases displayed greater electrical risk score increase over time vs. controls [+1.06 (95% confidence interval +0.89 to +1.24) vs. -0.05 (-0.21 to +0.11); P < .001]. These findings were successfully replicated in California [+0.87 (+0.7 to +1.04) vs. -0.11 (-0.27 to 0.05); P < .001]. In multivariable models, abnormal dynamic ECG remodelling improved SCD prediction over baseline ECG, demographics, and clinical SCD risk factors in both Oregon [area under the receiver operating characteristic curve 0.770 (95% confidence interval 0.727-0.812) increased to area under the receiver operating characteristic curve 0.869 (95% confidence interval 0.837-0.902)] and California cohorts.

CONCLUSIONS

Dynamic ECG remodelling improved SCD risk prediction beyond clinical factors combined with the static ECG, with successful validation in a geographically distinct population. These findings introduce a novel concept of SCD dynamic risk and warrant further detailed investigation.

An ECG-based artificial intelligence model for assessment of sudden cardiac death risk

BACKGROUND

Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment.

METHODS

Out-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model.

RESULTS

The DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort.

CONCLUSIONS

An ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Sex differences in the association between minor nonspecific ST-segment and T-wave abnormalities and coronary artery calcification

BACKGROUND AND AIMS

Although minor nonspecific ST-segment and T-wave abnormalities (NSSTTA) have been associated with adverse cardiovascular outcomes, their relationship with subclinical atherosclerosis remains controversial. Therefore, the associations between electrocardiographic (ECG) abnormalities, including NSSTTA, and coronary artery calcification (CAC) were investigated in this study.

METHODS

This cross-sectional study included 136,461 Korean participants with no known cardiovascular disease or cancer, who underwent a health checkup including ECG and computed tomography to measure the coronary artery calcium score (CACS) by Agatston method between 2010 and 2018. ECG abnormalities were defined in accordance with the Minnesota Code using an automated ECG analysis program. A multinomial logistic regression model was used to calculate prevalence ratios (PRs) with 95% confidence intervals (CI) for each CACS category.

RESULTS

In men, both NSSTTA and major ECG abnormalities were associated with all levels of CACS. The multivariable-adjusted PRs (95% CI) for CACS >400 comparing NSSTTA and major ECG abnormalities to the reference (neither NSSTTA nor major ECG abnormalities) were 1.88 (1.29-2.74) and 1.50 (1.18-1.91), respectively. Women with major ECG abnormalities were more likely to have a CACS of 101-400, the PRs (95% CI) comparing major ECG abnormalities to the reference group was 1.75 (1.18-2.57). NSSTTA were not associated with any CACS level in women.

CONCLUSIONS

NSSTTA and major ECG abnormalities are associated with CAC in men, though NSSTTA were not associated with CAC in women, suggesting that NSSTTA should be considered sex-specific risk factors for coronary artery disease in men, but not in women.

Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race

INTRODUCTION

Measurement of the spatial ventricular gradient (SVG), spatial QRST angles, and other vectorcardiographic measures of myocardial electrical heterogeneity have emerged as novel risk stratification methods for sudden cardiac death and other adverse cardiovascular events. Prior studies of normal limits of these measurements included primarily young, healthy, White volunteers, but normal limits in older patients are unknown. The influence of race and body mass index (BMI) on these measurements is also unclear.

METHODS

Normal 12-lead electrocardiograms (ECGs) from a single center were identified. Patients with abnormal cardiovascular, pulmonary, or renal history (assessed by International Classification of Disease [ICD-9/ICD-10] codes) or abnormal cardiovascular imaging were excluded. The SVG and QRST angles were measured and stratified by age, sex, and race. Multivariable linear regression was used to assess the influence of age, BMI, and heart rate (HR) on these measurements.

RESULTS

Among 3292 patients, observed ranges of SVG and QRST angles (peak and mean) differed significantly based on sex, age, and race. Sex differences attenuated with increasing age. Men tended to have larger SVG magnitude (60.4 [46.1-77.8] vs. 52.5 [41.3-65.8] mv*ms, p < .0001) and elevation, and more anterior/negative SVG azimuth (-14.8 [-25.1 to -4.3] vs. 1.3 [-9.8 to 10.5] deg, p < .0001) compared to women. Men also had wider QRST angles. Observed ranges varied significantly with BMI and HR. SVG and QRST angle measurements were robust to different filtering bandwidths and moderate fiducial point annotation errors, but were heavily affected by changes in baseline correction.

CONCLUSIONS

Age, sex, race, BMI, and HR significantly affect the range of SVG and QRST angles in patients with normal ECGs and no known cardiovascular disease, and should be accounted for in future studies. An online calculator for prediction of these "normal limits" given demographics is provided at https://bivectors.github.io/gehcalc/.

An electrophysiological substrate of COVID-19

SARS-CoV-2 infection is associated with an increased risk of late cardiovascular (CV) outcomes. However, more data is needed to describe the electrophysiologic (EP) manifestation of post-acute CV sequelae of COVID-19. We compared two cohorts of adult patients with SARS-CoV-2 polymerase chain reaction (PCR) test and an electrocardiogram (ECG) performed between March 1, 2020, and September 13, 2020, in a retrospective double-cohort study, "Cardiovascular Risk Stratification in Covid-19" (CaVaR-Co19; NCT04555187). Patients with positive PCR comprised a COVID-19(+) cohort (n = 41; 61% women; 80% symptomatic), whereas patients with negative tests formed the COVID-19(-) cohort (n = 155; 56% women). In longitudinal analysis, comparing 3 ECGs recorded before, during, and on average 40 days after index COVID-19 episode, after adjustment for demographic and socioeconomic characteristics, baseline CV risk factors and comorbidities, use of prescription medications (including QT-prolonging drugs) before and during index COVID-19 episode, and the longitudinal changes in RR' intervals, heart rhythm, and ventricular conduction type, only in the COVID-19(+) cohort QTc increased by +30.2(95% confidence interval [CI] 0.1-60.3) ms and the spatial ventricular gradient (SVG) elevation increased by +13.5(95%CI 1.2-25.9)°. In contrast, much smaller, statistically nonsignificant changes were observed in the COVID-19(-) cohort. In conclusion, post-acute CV sequelae of SARS-CoV-2 infection manifested on ECG by QTc prolongation and rotation of the SVG vector upward.

The Spatial Ventricular Gradient Is an Independent Predictor of Anthracycline-Associated Cardiotoxicity

Background

Anthracyclines are effective chemotherapies that are limited by cardiotoxicity. The spatial ventricular gradient (SVG) is a marker of electrical heterogeneity linked to adverse cardiovascular outcomes, including sudden cardiac death and heart failure (HF).

Objectives

The purpose of this study was to assess if SVG values before chemotherapy are associated with the risk of anthracycline-associated HF or cardiomyopathy (CM).

Methods

We analyzed 12-lead electrocardiograms obtained within 6 months before initiation of anthracyclines in a retrospective cohort treated for cancer between 1992 and 2019 at a single academic medical center. Incident HF and CM were defined by ICD-9/10 codes and confirmed by chart review. Vectorcardiograms were constructed from baseline electrocardiograms, and the SVG was calculated. The cumulative incidence of anthracycline-associated HF or CM was regressed on SVG vector orientation and magnitude with death as a competing risk.

Results

In 889 patients (47% male; mean age 58 ± 16 years; 71% hematologic malignancies), larger SVG magnitude prechemotherapy was associated with decreased risk of HF or CM after multivariable adjustment, with a subhazard ratio of 0.76 per 1 SD increase (95% CI: 0.59-0.96; P = 0.024). SVG vector orientation, specifically a more leftward oriented VGx, was associated with decreased risk of HF or CM with a subhazard ratio of 0.77 per 1 SD increase (95% CI: 0.61-0.96; P = 0.023).

Conclusions

Larger SVG magnitude and more leftward SVG orientation were associated with a decreased risk of anthracycline cardiotoxicity in a large retrospective cohort. Improved cardiac risk stratification algorithms incorporating the SVG could personalize both cancer and cardioprotective therapy.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

The spatial ventricular gradient is associated with adverse outcomes in acute pulmonary embolism

BACKGROUND

The spatial ventricular gradient (SVG) is a vectorcardiographic measurement that reflects cardiac loading conditions via electromechanical coupling.

OBJECTIVES

We hypothesized that the SVG is correlated with right ventricular (RV) strain and is prognostic of adverse events in patients with acute pulmonary embolism (PE).

METHODS

Retrospective, single-center study of patients with acute PE. Electrocardiogram (ECG), imaging, and outcome data were obtained. SVG components were regressed on tricuspid annular plane systolic excursion (TAPSE), qualitative RV dysfunction, and RV/left ventricular (LV) ratio. Odds of adverse outcomes (30-day mortality, vasopressor requirement, or advanced therapy) after PE were regressed on demographics, RV/LV ratios, traditional ECG signs of RV dysfunction, and SVG components using a logit model.

RESULTS

ECGs from 317 patients (48% male, age 63.1 ± 16.6 years) with acute PE were analyzed; 36 patients (11.4%) experienced an adverse event. Worse RV hypokinesis, larger RV/LV ratio, and smaller TAPSE were associated with smaller SVG X and Y components, larger SVG Z components, and smaller SVG vector magnitude (p < .001 for all). In multivariable logistic regression, odds of adverse events after PE decreased with increasing SVG magnitude and TAPSE (OR 0.32 and 0.54 per standard deviation increase; p = .03 and p = .004, respectively). Receiver operating characteristic (ROC) analysis showed that, when combined with imaging, replacing traditional ECG criteria with the SVG significantly improved the area under the ROC from 0.70 to 0.77 (p = .01).

CONCLUSION

The SVG is correlated with RV dysfunction and adverse outcomes in acute PE and has a better prognostic value than traditional ECG markers.

Dynamic Change of Cardiovascular Health Metrics and Long‐Term Risk of Sudden Cardiac Death: The ARIC Study

Background

The change of cardiovascular health (CVH) status has been associated with risk of cardiovascular disease. However, no studies have explored the change patterns of CVH in relation to risk of sudden cardiac death (SCD). We aim to examine the link between baseline CVH and change of CVH over time with the risk of SCD.

Methods and Results

Analyses were conducted in the prospective cohort ARIC (Atherosclerosis Risk in Communities) study, started in 1987 to 1989. ARIC enrolled 15 792 individuals 45 to 64 years of age from 4 US communities (Forsyth County, North Carolina; Jackson, Mississippi; suburbs of Minneapolis, Minnesota; and Washington County, Maryland). Subjects with 0 to 2, 3 to 4, and 5 to 7 ideal metrics of CVH were categorized as having poor, intermediate, or ideal CVH, respectively. Change in CVH over 6 years between 1987 to 1989 and 1993 to 1995 was considered. The primary study outcome was physician adjudicated SCD. The study population consisted of 15 026 subjects, of whom 12 207 had data about CVH change. Over a median follow‐up of 23.0 years, 583 cases of SCD were recorded. There was a strong inverse association between baseline CVH metrics and time varying CVH metrics with risk of SCD. Compared with subjects with consistently poor CVH, risk of SCD was lower in those changed from poor to intermediate/ideal (hazard ratio [HR], 0.67 [95% CI, 0.48–0.94]), intermediate to poor (HR, 0.73 [95% CI, 0.54–0.99]), intermediate to ideal (HR, 0.49 [95% CI, 0.24–0.99]), ideal to poor/intermediate CVH (HR, 0.23 [95% CI, 0.10–0.52]), or those with consistently intermediate (HR, 0.49 [95% CI, 0.36–0.66]) or consistently ideal CVH (HR, 0.31 [95% CI, 0.13–0.76]). Similar results were also observed for non‐SCD.

Conclusions

Compared with consistently poor CVH, other patterns of change in CVH were associated with lower risk of SCD. These findings highlight the importance of promotion of ideal CVH in the primordial prevention of SCD.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

Risk of hypertension and treatment on out-of-hospital cardiac arrest incidence: A case-control study

Hypertension (HTN) is a high risk factor for major cardiovascular adverse events. This study aimed to investigate the effect of HTN risk on out-of-hospital cardiac arrest (OHCA) incidence and determine whether the effect of HTN on OHCA incidence differs according to antihypertensive medication.This case-control study used the Korean Cardiac Arrest Resuscitation Consortium and Korean Community Health Survey (CHS). Cases were defined as emergency medical service-treated adult OHCA patients presumed to have a cardiac etiology from 2015 to 2017. Patients without information on HTN diagnosis were excluded from the study. The Korean CHS database's controls were matched at a 1:2 ratio with strata, including age, gender, and county of residence. Multivariable conditional logistic regression analysis was conducted to estimate HTN risk and antihypertensive treatment on OHCA incidence,A total of 2633 OHCA patients and 5266 community-based controls were enrolled in this study. Among them, 1176 (44.7%) patients and 2049 (38.9%) controls were diagnosed with HTN. HTN was associated with an increased risk of OHCA (adjusted odds ratio [AOR]: 1.19 [1.07-1.32]). On comparing HTN with or without the antihypertensive treatment group with the non-HTN-diagnosed group (as a reference), the HTN without treatment group had the highest AOR (95% confidence interval) (3.41 [2.74-4.24]). The AOR in the HTN treatment group was reduced to that in the non-HTN-diagnosed group (0.96 [0.86-1.08]).HTN increased OHCA risk, and the HTN without treatment group had the highest OHCA risk. Conversely, OHCA risk decreased to the non-HTN-diagnosed group level with HTN treatment.

Digitizing ECG image: A new method and open-source software code

BACKGROUND AND OBJECTIVE

We aimed to develop and validate an open-source code ECG-digitizing tool and assess agreements of ECG measurements across three types of median beats, comprised of digitally recorded simultaneous and asynchronous ECG leads and digitized asynchronous ECG leads.

METHODS

We used the data of clinical studies participants (n = 230; mean age 30±15 y; 25% female; 52% had the cardiovascular disease) with available both digitally recorded and printed on paper and then scanned ECGs, split into development (n = 150) and validation (n = 80) datasets. The agreement between ECG and VCG measurements on the digitally recorded time-coherent median beat, representative asynchronous digitized, and digitally recorded beats was assessed by Bland-Altman analysis.

RESULTS

The sample-per-sample comparison of digitally recorded and digitized signals showed a very high correlation (0.977), a small mean difference (9.3 µV), and root mean squared error (25.9 µV). Agreement between digitally recorded and digitized representative beat was high [area spatial ventricular gradient (SVG) elevation bias 2.5(95% limits of agreement [LOA] -7.9-13.0)°; precision 96.8%; inter-class correlation [ICC] 0.988; Lin's concordance coefficient ρ_c 0.97(95% confidence interval [CI] 0.95-0.98)]. Agreement between digitally recorded asynchronous and time-coherent median beats was moderate for area-based VCG metrics (spatial QRS-T angle bias 1.4(95%LOA -33.2-30.3)°; precision 94.8%; ICC 0.95; Lin's concordance coefficient ρ_c 0.90(95%CI 0.82-0.95)].

CONCLUSIONS

We developed and validated an open-source software tool for paper-ECG digitization. Asynchronous ECG leads are the primary source of disagreement in measurements on digitally recorded and digitized ECGs.

Heat Shock Protein 27 Levels Predict Myocardial Inhomogeneities in Hemodialysis Patients

Background

Sudden cardiac death (SCD) is the single major cause of death in hemodialysis (HD) patients. QRS-T angle is an established marker of global repolarization heterogeneity associated with electrical instability and SCD. Heat shock protein 27 (HSP27) plays an important, protective role against noxious factors in the cardiovascular (CV) system. This study is aimed at assessing whether low HSP27 is associated with myocardial inhomogeneities in HD patients, as expressed by increases in the spatial QRS-T angle.

Methods

Clinical data and biochemical, echocardiographic, and electrocardiographic parameters were evaluated in 182 HD patients. Patients were split into normal and abnormal QRS-T angle groups.

Results

Patients with abnormally high QRS-T angles were older and had higher prevalence of diabetes as well as myocardial infarction, higher left ventricular mass index (LVMI) and C-reactive protein, worse oxidant/antioxidant status, and lower ejection fraction and HSP27. Multiple regression analysis revealed that abnormal QRS-T values were independently, negatively associated with serum HSP27 and positively associated with LVMI.

Conclusions

Low HSP27 levels are associated with increased heterogeneity of myocardial action potential, as expressed by increased spatial QRS-T angle.

Comorbid Hypertension Reduces the Risk of Ventricular Arrhythmia in Chronic Heart Failure Patients with Implantable Cardioverter-Defibrillators

AIMS

Low blood pressure (BP) has been shown to be associated with increased mortality in patients with chronic heart failure. This study was designed to evaluate the relationships between diagnosed hypertension and the risk of ventricular arrhythmia (VA) and all-cause death in chronic heart failure (CHF) patients with implantable cardioverter-defibrillators (ICD), including those with preserved left ventricular ejection fraction (HFpEF) and indication for ICD secondary prevention. We hypothesized that a stable hypertension status, along with an increasing BP level, is associated with a reduction in the risk of VA in this population, thereby limiting ICD efficacy.

METHODS

We retrospectively enrolled 964 CHF patients, with hypertension diagnosis and hospitalized BP measurements obtained before ICD implantation. The primary outcome measure was defined as the composite of SCD, appropriate ICD therapy, and sustained VT. The secondary endpoint was time to death or heart transplantation (HTx). We performed multivariable Cox proportional hazard regression and entropy balancing to calculate weights to control for baseline imbalances with or without hypertension. The Fine-Gray subdistribution hazard model was used to confirm the results. The effect of random BP measurements on the primary outcome was illustrated in the Cox model with inverse probability weighting.

RESULTS

The 964 patients had a mean (SD) age of 58.9 (13.1) years; 762 (79.0%) were men. During the interrogation follow-up [median 2.81 years (interquartile range: 1.32-5.27 years)], 380 patients (39.4%) reached the primary outcome. A total of 244 (45.2%) VA events in non-hypertension patients and 136 (32.1%) in hypertension patients were observed. A total of 202 (21.0%) patients died, and 31 (3.2%) patients underwent heart transplantation (incidence 5.89 per 100 person-years; 95% CI: 5.16-6.70 per 100 person-years) during a median survival follow-up of 4.5 (IQR 2.8-6.8) years. A lower cumulative incidence of VA events was observed in hypertension patients in the initial unadjusted Kaplan-Meier time-to-event analysis [hazard ratio (HR): 0.65, 95% confidence interval (CI): 0.53-0.80]. The protective effect was robust after entropy balancing (HR: 0.71, 95% CI: 0.56-0.89) and counting death as a competing risk (HR: 0.71, 95% CI: 0.51-1.00). Hypertension diagnosis did not associate with all-cause mortality in this population. Random systolic blood pressure was negatively associated with VA outcomes (p = 0.065).

CONCLUSIONS

In hospitalized chronic heart failure patients with implantable cardioverter-defibrillators, the hypertension status and higher systolic blood pressure measurements are independently associated with a lower risk of combined endpoints of ventricular arrhythmia and sudden cardiac death but not with all-cause mortality. Randomized controlled trials are needed to confirm the protective effect of hypertension on ventricular arrhythmia in chronic heart failure patients.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring

PURPOSE

Cardiac arrest (CA) is the most serious death-related event in critically ill patients and the early detection of CA is beneficial to reduce mortality according to clinical research. This study aims to develop and verify a real-time, interpretable machine learning model, namely cardiac arrest prediction index (CAPI), to predict CA of critically ill patients based on bedside vital signs monitoring.

METHODS

A total of 1,860 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Based on vital signs, we extracted a total of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to capture the overall and real-time interpretability of the model.

RESULTS

On the test set, CAPI predicted 95% of CA events, 80% of which were identified more than 25 min in advance, resulting in an area under the receiver operating characteristic curve (AUROC) of 0.94. The sensitivity, specificity, area under the precision-recall curve (AUPRC) and F1-score were 0.86, 0.85, 0.12 and 0.05, respectively.

CONCLUSION

CAPI can help predict patients with CA in the vital signs monitoring at bedside. Compared with previous studies, CAPI can give more timely notifications to doctors for CA events. However, current performance was at the cost of alarm fatigue. Future research is still needed to achieve better clinical application.

The value of ventricular gradient for predicting pulmonary hypertension and mortality in hemodialysis patients

Pulmonary hypertension (PHT) is associated with increased mortality in hemodialysis (HD) patients. The ventricular gradient optimized for right ventricular pressure overload (VG-RVPO) is sensitive to early changes in right ventricular overload. The study aimed to assess the ability of the VG-RVPO to detect PHT and predict all-cause and cardiac mortality in HD patients. 265 selected HD patients were enrolled. Clinical, biochemical, electrocardiographic, and echocardiographic parameters were evaluated. Patients were divided into normal and abnormal VG-RVPO groups, and were followed-up for 3 years. Abnormal VG-RVPO patients were more likely to be at high or intermediate risk for PHT, were older, had longer HD vintage, higher prevalence of myocardial infarction, higher parathormone levels, shorter pulmonary flow acceleration time, lower left ventricular ejection fraction, higher values of left atrial volume index, left ventricular mass index, and peak tricuspid regurgitant velocity. Both all-cause and CV mortality were higher in abnormal VG-RVPO group. In multivariate Cox analysis, VG-RVPO remained an independent and strong predictor of all-cause and CV mortality. In HD patients, abnormal VG-RVPO not only predicts PHT, but also all-cause and CV mortality.

OUP accepted manuscript

More than 40 years after the first implantable cardioverter-defibrillator (ICD) implantation, sudden cardiac death (SCD) still accounts for more than five million deaths worldwide every year. Huge efforts in the field notwithstanding, it is now increasingly evident that the current strategy of long-term prevention based on left ventricular ejection fraction as the key selection criterion is actually of very limited impact, also because the largest absolute numbers of SCD are encountered in the general population not known to be at risk. It has been recently reemphasized that SCD is often not so sudden, with almost half of the victims experiencing typical warning symptoms preceding the event. Importantly, heeded and prompt medical attention can dramatically improve survival. Essentially, such timely action increases the chances of the SCD event being witnessed by emergency medical services and provides the opportunity for early intervention. In addition, newer technologies incorporating digital data acquisition, transfer between interconnected devices, and artificial intelligence, should allow dynamic, real-time monitoring of diverse parameters and therefore better identification of subjects at short-term SCD risk. Along with warning symptoms, these developments allow a new approach of near-term prevention based on the hours and minutes preceding SCD. In the present review, we challenge the current paradigm of mid- and long-term prevention using ICD in patients at the highest risk of SCD, and introduce a complementary concept applicable to the entire population that would aim to pre-empt SCD by timely detection and intervention within the minutes or hours prior to the event.

Reproducibility of global electrical heterogeneity measurements on 12-lead ECG: The Multi-Ethnic Study of Atherosclerosis

OBJECTIVE

Vectorcardiographic (VCG) global electrical heterogeneity (GEH) metrics showed clinical usefulness. We aimed to assess the reproducibility of GEH metrics.

METHODS

GEH was measured on two 10-s 12‑lead ECGs recorded on the same day in 4316 participants of the Multi-Ethnic Study of Atherosclerosis (age 69.4 ± 9.4 y; 2317(54%) female, 1728 (40%) white, 1138(26%) African-American, 519(12%) Asian-American, 931(22%) Hispanic-American). GEH was measured on a median beat, comprised of the normal sinus (N), atrial fibrillation/flutter (S), and ventricular-paced (VP) beats. Spatial ventricular gradient's (SVG's) scalar was measured as sum absolute QRST integral (SAIQRST) and vector magnitude QT integral (VMQTi).

RESULTS

Two N ECGs with heart rate (HR) bias of -0.64 (95% limits of agreement [LOA] -5.68 to 5.21) showed spatial area QRS-T angle (aQRST) bias of -0.12 (95%LOA -14.8 to 14.5). Two S ECGs with HR bias of 0.20 (95%LOA -15.8 to 16.2) showed aQRST bias of 1.37 (95%LOA -33.2 to 35.9). Two VP ECGs with HR bias of 0.25 (95%LOA -3.0 to 3.5) showed aQRST bias of -1.03 (95%LOA -11.9 to 9.9). After excluding premature atrial or ventricular beat and two additional beats (before and after extrasystole), the number of cardiac beats included in a median beat did not affect the GEH reproducibility. Mean-centered log-transformed values of SAIQRST and VMQTi demonstrated perfect agreement (Bias 0; 95%LOA -0.092 to 0.092).

CONCLUSION

GEH measurements on N, S, and VP median beats are reproducible. SVG's scalar can be measured as either SAIQRST or VMQTi.

SIGNIFICANCE

Satisfactory reproducibility of GEH metrics supports their implementation.

Prognostic significance of flat T-waves in the lateral leads in general population

BACKGROUND

Negative T-waves are associated with sudden cardiac death (SCD) risk in the general population. Whether flat T-waves also predict SCD is not known. The aim of the study was to examine the clinical characteristics and risk of SCD in general population subjects with flat T-waves.

METHODS

We examined the electrocardiograms of 6750 Finnish general population adults aged ≥30 years and classified the subjects into 3 groups: 1) negative T-waves with an amplitude ≥0.1 mV in ≥2 of the leads I, II, aVL, V4-V6, 2) negative or positive low amplitude T-waves with an amplitude <0.1 mV and the ratio of T-wave and R-wave <10% in ≥2 of the leads I, II, aVL, V4-V6, and 3) normal positive T-waves (not meeting the aforesaid criteria). The association between T-wave classification and SCD was assessed during a 10-year follow-up.

RESULTS

A total of 215 (3.2%) subjects had negative T-waves, 856 (12.7%) flat T-waves, and 5679 (84.1%) normal T-waves. Flat T-wave subjects were older and had more often cardiovascular morbidities compared to normal T-wave subjects, while negative T-wave subjects were the oldest and had most often cardiovascular morbidities. After adjusting for multiple factors, both flat T-waves (hazard ratio [HR] 1.81; 95% confidence interval [CI] 1.13-2.91) and negative T-waves (HR 3.27; 95% CI 1.85-5.78) associated with SCD.

CONCLUSIONS

Cardiovascular risk factors and disease are common among subjects with flat T-waves, but these minor T-wave abnormalities are also independently associated with increased SCD risk.

Develop and Apply Electrocardiography-Based Risk Score to Identify Community-Based Elderly Individuals at High-Risk of Mortality

With an aging world population, risk stratification of community-based, elderly population is required for primary prevention. This study proposes a combined score developed using electrocardiographic (ECG) parameters and determines its long-term prognostic value for predicting risk of cardiovascular mortality. A cohort-study, conducted from December 2008 to April 2019, enrolled 5,380 subjects in Taiwan, who were examined, using three-serial-12-lead ECGs, and their health/demographic information were recorded. To understand the predictive effects of ECG parameters on overall-survival, Cox hazard regression analysis were performed. The mean age at enrollment was 69.04 ± 8.14 years, and 47.4% were males. ECG abnormalities, LVH [hazard ratio (HR) = 1.39, 95% confidence intervals (CI) = (1.16-1.67), P = 0.0003], QTc [HR = 1.31, CI = (1.07-1.61), P = 0.007] and PR interval [HR = 1.40, CI = (1.01-1.95), P = 0.04], were significantly associated with primary outcome all-cause death. Furthermore, LVH [HR = 2.37, CI = (1.48-3.79), P = 0.0003] was significantly associated with cardiovascular death, while PR interval [HR = 2.63, CI = (1.24- 5.57), P = 0.01] with unexplained death. ECG abnormality (EA) score was defined based on the number of abnormal ECG parameters for each patient, which was used to divide all patients into sub-groups. Competing risk survival analysis using EA score were performed by using the Gray's test, which reported that high-risk EA groups showed significantly higher cumulative incidence for all three outcomes. Prognostic models using the EA score as predictor were developed and a 10-fold cross validation design was adopted to conduct calibration and discrimination analysis, to establish the efficacy of the proposed models. Overall, ECG model could successfully predict people, susceptible to all three death outcomes (P < 0.05), with high efficacy. Statistically significant (P < 0.001) improvement of the c-indices further demonstrated the robustness of the prediction model with ECG parameters, as opposed to a traditional model with no EA predictor. The EA score is highly associated with increased risk of mortality in elderly population and may be successfully used in clinical practice.

Spectrum of clinical applications of interlead ECG heterogeneity assessment: From myocardial ischemia detection to sudden cardiac death risk stratification

Heterogeneity in depolarization and repolarization among regions of cardiac cells has long been recognized as a major factor in cardiac arrhythmogenesis. This fundamental principle has motivated development of noninvasive techniques for quantification of heterogeneity using the surface electrocardiogram (ECG). The initial approaches focused on interval analysis such as interlead QT dispersion and T_peak -T_end difference. However, because of inherent difficulties in measuring the termination point of the T wave and commonly encountered irregularities in the apex of the T wave, additional techniques have been pursued. The newer methods incorporate assessment of the entire morphology of the T wave and in some cases of the R wave as well. This goal has been accomplished using a number of promising vectorial approaches with the resting 12-lead ECG. An important limitation of vectorcardiographic analyses is that they require exquisite stability of the recordings and are not inherently suitable for use in exercise tolerance testing (ETT) and/or ambulatory ECG monitoring for provocative stress testing or evaluation of the influence of daily activities on cardiac electrical instability. The objectives of the present review are to describe a technique that has been under clinical evaluation for nearly a decade, termed "interlead ECG heterogeneity." Preclinical testing data will be briefly reviewed. We will discuss the main clinical findings with regard to sudden cardiac death risk stratification, heart failure evaluation, and myocardial ischemia detection using standard recording platforms including resting 12-lead ECG, ambulatory ECG monitoring, ETT, and pharmacologic stress testing in conjunction with single-photon emission computed tomography myocardial perfusion imaging.

Electrophysiological ventricular substrate of stroke: a prospective cohort study in the Atherosclerosis Risk in Communities (ARIC) study

OBJECTIVES

The goal of the study was to determine an association of cardiac ventricular substrate with thrombotic stroke (TS), cardioembolic stroke (ES) and intracerebral haemorrhage (ICH).

DESIGN

Prospective cohort study.

SETTING

The Atherosclerosis Risk in Communities (ARIC) study in 1987-1989 enrolled adults (45-64 years), selected as a probability sample from four US communities (Minneapolis, Minnesota; Washington, Maryland; Forsyth, North Carolina; Jackson, Mississippi). Visit 2 was in 1990-1992, visit 3 in 1993-1995, visit 4 in 1996-1998 and visit 5 in 2011-2013.

PARTICIPANTS

ARIC participants with analysable ECGs and no history of stroke were included (n=14 479; age 54±6 y; 55% female; 24% black). Ventricular substrate was characterised by cardiac memory, spatial QRS-T angle (QRS-Ta), sum absolute QRST integral (SAIQRST), spatial ventricular gradient magnitude (SVGmag), premature ventricular contractions (PVCs) and tachycardia-dependent intermittent bundle branch block (TD-IBBB) on 12-lead ECG at visits 1-5.

OUTCOME

Adjudicated TS included a first definite or probable thrombotic cerebral infarction, ES-a first definite or probable non-carotid cardioembolic brain infarction. Definite ICH was included if it was the only stroke event.

RESULTS

Over a median 24.5 years follow-up, there were 899 TS, 400 ES and 120 ICH events. Cox proportional hazard risk models were adjusted for demographics, cardiovascular disease, risk factors, atrial fibrillation, atrial substrate and left ventricular hypertrophy. After adjustment, PVCs (HR 1.72; 95% CI 1.02 to 2.92), QRS-Ta (HR 1.15; 95% CI 1.03 to 1.28), SAIQRST (HR 1.20; 95% CI 1.07 to 1.34) and time-updated SVGmag (HR 1.19; 95% CI 1.08 to 1.32) associated with ES. Similarly, PVCs (HR 1.53; 95% CI 1.03 to 2.26), QRS-Ta (HR 1.08; 95% CI 1.01 to 1.16), SAIQRST (HR 1.07; 95% CI 1.01 to 1.14) and time-updated SVGmag (HR 1.11; 95% CI 1.04 to 1.19) associated with TS. TD-IBBB (HR 3.28; 95% CI 1.03 to 10.46) and time-updated SVGmag (HR 1.23; 95% CI 1.03 to 1.47) were associated with ICH.

CONCLUSIONS

PVC burden (reflected by cardiac memory) is associated with ischaemic stroke. Transient cardiac memory (likely through TD-IBBB) precedes ICH.

Airflow obstruction, impaired lung function and risk of sudden cardiac death: a prospective cohort study

BACKGROUND

Growing evidence suggests that compromised lung health may be linked to cardiovascular disease. However, little is known about its association with sudden cardiac death (SCD).

OBJECTIVES

We aimed to assess the link between impaired lung function, airflow obstruction and risk of SCD by race and gender in four US communities.

METHODS

A total of 14 708 Atherosclerosis Risk in Communities (ARIC) study participants who underwent spirometry and were asked about lung health (1987-1989) were followed. The main outcome was physician-adjudicated SCD. Fine-Gray proportional subdistribution hazard models with Firth's penalised partial likelihood correction were used to estimate the HRs.

RESULTS

Over a median follow-up of 25.4 years, 706 (4.8%) subjects experienced SCD. The incidence of SCD was inversely associated with FEV₁ in each of the four race and gender groups and across all smoking status categories. After adjusting for multiple measured confounders, HRs of SCD comparing the lowest with the highest quintile of FEV₁ were 2.62 (95% CI 1.62 to 4.26) for white males, 1.80 (95% CI 1.03 to 3.15) for white females, 2.07 (95% CI 1.05 to 4.11) for black males and 2.62 (95% CI 1.21 to 5.65) for black females. The above associations were consistently observed among the never smokers. Moderate to very severe airflow obstruction was associated with increased risk of SCD. Addition of FEV₁ significantly improved the predictive power for SCD.

CONCLUSIONS

Impaired lung function and airflow obstruction were associated with increased risk of SCD in general population. Additional research to elucidate the underlying mechanisms is warranted.

Vectorcardiography as a prognostic tool in hypertrophic cardiomyopathy

BACKGROUND

Vectorcardiography (VCG) has demonstrated diagnostic value in the assessment of hypertrophic cardiomyopathy (HCM), however, determining its prognostic value over time has not yet been investigated. This study sought to assess the correlation of VCG parameters with the progression of HCM.

METHODS

A retrospective chart review of 119 pediatric patients with diagnosis of HCM at the University of Minnesota. Eighty-three cases were excluded because of age, presence of congenital heart disease, not meeting criteria for HCM or negative phenotype. Sample was divided into 2 groups based on the presence or not of cardiac events (ventricular tachycardia, cardiac arrest, ventricular assist device, heart transplant). Derived vectorcardiography from standard 12‑lead ECG was obtained for the first ECG and last available or prior to sentinel event.

RESULTS

Of the 36 cases that met inclusion criteria, 9 (25%) developed a sentinel event. The median age for the event group was 10.1 ± 7.5 years and for the non-event group was 8.7 ± 6.35 years. There was no significant difference in age or sex between the groups. The T wave vector magnitude value was significantly smaller in the event group than in the non-event group (0.302 ± 0.146 mV Vs. 0.561 ± 0.305 mV, p 0.002), with a hazard ratio of 0.651 (95% CI 0.463 to 0.915). No other parameter showed significant difference between the two groups.

CONCLUSIONS

The T wave vector magnitude may predict sentinel events in HCM. Prospective studies are necessary to evaluate the utility of the evolution of VCG parameters.

ECGAug: A novel method of generating augmented annotated electrocardiogram QRST complexes and rhythm strips

Applications of neural networks (NNs) in medicine have increased dramatically in recent years. In order to train a NN that performs ECG segmentation, it can be very time consuming, or even completely prohibitive, to manually annotate fiducial points on enough QRST complexes to reach a high level of performance. Existing methods for time series data augmentation risk creating non-physiological ECG signals that may hamper NN training, and are unable to provide accurate fiducial point locations in the augmented data. We therefore developed ECGAug, a new method which generates an augmented training set of QRST signals (single beats or rhythm strips) with accurate fiducial point annotations. Our algorithm recombines a library of existing, annotated QRS complexes and T waves in physiologic ways, and then performs additional physiological transformations to generate a set of new annotated QRST complexes or rhythm strips to be used for NN training or validation of ECG annotation algorithms. In experiments where we trained NNs to annotate QRST complexes with a limited training dataset, QRST complexes added to the training dataset by ECGAug significantly improved NN performance. We present the ECGAug process, demonstrate its efficacy, and provide links for downloading the open source ECGAug software.

Detection and removal of pacing artifacts prior to automated analysis of 12-lead ECG

BACKGROUND

Pacing artifacts must be excluded from the analysis of paced ECG waveform. This study aimed to develop and validate an algorithm to identify and remove the pacing artifacts on ECG and vectorcardiogram (VCG).

METHODS

We developed a semi-automatic algorithm that identifies the onset and offset of a pacing artifact based on the VCG signal slope steepness and designed a graphical user interface that permits quality control and fine-tuning the constraining threshold values. We used 1054 ECGs from the retrospective, multicenter cohort study "Global Electrical Heterogeneity and Clinical Outcomes," including 3825 atrial and 10,031 ventricular pacing artifacts for the algorithm development and 22 ECGs including 108 atrial and 241 ventricular pacing artifacts for validation. Validation was performed per digital sample. We used the kappa-statistic of interrater agreement between manually labeled sample (ground-truth) and automated detection.

RESULTS

The constraining parameter values were for onset threshold 13.06 ± 6.21 μV/ms, offset threshold 34.77 ± 17.80 μV/ms, and maximum window size 27.23 ± 3.53 ms. The automated algorithm detected a digital sample belonging to pacing artifact with a sensitivity of 74.5% and specificity of 99.6% and classified correctly 98.8% of digital samples (ROC AUC 0.871; 95%CI 0.853-0.878). The kappa-statistic was 0.785, indicating substantial agreement. The agreement was on 98.81% digital samples, significantly (P < 0.00001) larger than the random agreement on 94.43% of digital samples.

CONCLUSIONS

The semi-automated algorithm can detect and remove ECG pacing artifacts with high accuracy and provide a user-friendly interface for quality control.

Competing risks in patients with primary prevention implantable cardioverter-defibrillators: Global Electrical Heterogeneity and Clinical Outcomes study

BACKGROUND

Global electrical heterogeneity (GEH) is associated with sudden cardiac death in the general population. Its utility in patients with systolic heart failure who are candidates for primary prevention (PP) implantable cardioverter-defibrillators (ICDs) is unclear.

OBJECTIVE

The purpose of this study was to investigate whether GEH is associated with sustained ventricular tachycardia/ventricular fibrillation leading to appropriate ICD therapies in patients with heart failure and PP ICDs.

METHODS

We conducted a multicenter retrospective cohort study. GEH was measured by spatial ventricular gradient (SVG) direction (azimuth and elevation) and magnitude, QRS-T angle, and sum absolute QRST integral on preimplant 12-lead electrocardiograms. Survival analysis using cause-specific hazard functions compared the strength of associations with 2 competing outcomes: sustained ventricular tachycardia/ventricular fibrillation leading to appropriate ICD therapies and all-cause death without appropriate ICD therapies.

RESULTS

We analyzed 2668 patients (mean age 63 ± 12 years; 624 (23%) female; 78% white; 43% nonischemic cardiomyopathy; left ventricular ejection fraction 28% ± 11% from 6 academic medical centers). After adjustment for demographic, clinical, device, and traditional electrocardiographic characteristics, SVG elevation (hazard ratio [HR] per 1SD 1.14; 95% confidence interval [CI] 1.04-1.25; P = .004), SVG azimuth (HR per 1SD 1.12; 95% CI 1.01-1.24; P = .039), SVG magnitude (HR per 1SD 0.75; 95% CI 0.66-0.85; P < .0001), and QRS-T angle (HR per 1SD 1.21; 95% CI 1.08-1.36; P = .001) were associated with appropriate ICD therapies. Sum absolute QRST integral had different associations in infarct-related cardiomyopathy (HR 1.29; 95% CI 1.04-1.60) and nonischemic cardiomyopathy (HR 0.78; 95% CI 0.62-0.96) (P_interaction = .022).

CONCLUSION

In patients with PP ICDs, GEH is independently associated with appropriate ICD therapies. The SVG vector points in distinctly different directions in patients with 2 competing outcomes.

Electrocardiogram machine learning for detection of cardiovascular disease in African Americans: the Jackson Heart Study

AIMS

Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in community settings would facilitate secondary prevention of CVD. We sought to develop a tool for automated CVD detection.

METHODS AND RESULTS

Participants from the Jackson Heart Study (JHS) with analysable electrocardiograms (ECGs) (n=3679; age, 6212 years; 36% men) were included. Vectorcardiographic (VCG) metrics QRS, T, and spatial ventricular gradient vectors magnitude and direction, and traditional ECG metrics were measured on 12-lead ECG. Random forests, convolutional neural network (CNN), lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression models were developed in 80% and validated in 20% samples. We compared models with demographic, clinical, and VCG input (43 predictors) and those after the addition of ECG metrics (695 predictors). Prevalent CVD was diagnosed in 411 out of 3679 participants (11.2%). Machine learning models detected CVD with the area under the receiver operator curve (ROC AUC) 0.690.74. There was no difference in CVD detection accuracy between models with VCG and VCG + ECG input. Models with VCG input were better calibrated than models with ECG input. Plugin-based lasso model consisting of only two predictors (age and peak QRS-T angle) detected CVD with AUC 0.687 [95% confidence interval (CI) 0.6250.749], which was similar (P=0.394) to the CNN (0.660; 95% CI 0.5970.722) and better (P<0.0001) than random forests (0.512; 95% CI 0.4930.530).

CONCLUSIONS

Simple model (age and QRS-T angle) can be used for prevalent CVD detection in limited-resources community settings, which opens an avenue for secondary prevention of CVD in underserved communities.

Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

Introduction

The present study evaluated the application of incorporating non‐linear J/U‐shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non‐AMI‐related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus.

Methods

This was a territory‐wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti‐diabetic agents between January 1st, 2009 to December 31st, 2009 at government‐funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non‐AMI‐related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model.

Results

This study included 261 308 patients (age = 66.0 ± 11.8 years old, male = 47.6%, follow‐up duration = 3552 ± 1201 days, diabetes duration = 4.77 ± 2.29 years). Mean HbA1c and low high‐density lipoprotein‐cholesterol (HDL‐C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL‐C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J‐shaped relationship with non‐AMI‐related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models.

Conclusion

A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.

Characteristics of Cardiac Memory in Patients with Implanted Cardioverter-defibrillators: The Cardiac Memory with Implantable Cardioverter-defibrillator (CAMI) Study

This study sought to determine factors associated with cardiac memory (CM) in patients with implantable cardioverter-defibrillators (ICDs). Patients with structural heart disease [n = 20; mean age: 72.6 ± 11.6 years; 80% male; mean left ventricular ejection fraction (LVEF): 31.7 ± 7.6%; history of myocardial infarction in 75% and nonsustained ventricular tachycardia (NSVT) in 85%] and preserved atrioventricular conduction received dual-chamber ICDs for primary (80%) or secondary (20%) prevention. Standard 12-lead electrocardiograms were recorded in AAI and DDD modes before and after seven days of right ventricular (RV) pacing in DDD mode with a short atrioventricular delay. The direction (azimuth and elevation) and magnitude of spatial QRS, T, and spatial ventricular gradient vectors were measured before and after seven days of RV pacing. CM was quantified as the degree of alignment between QRS_DDD-7 and T_AAI-7 vectors (QRS_DDD-7 –T_AAI-7 angle). Circular statistics and mixed models with a random slope and intercept were adjusted for changes in cardiac activation, LVEF, known risk factors, and the use of medications known to affect CM occurring on days 1 through 7. The QRS_DDD-7–T_AAI-7 angle strongly correlated (circular r = −0.972; p < 0.0001) with a T_AAI-7–T_DDD-7 angle. In the mixed models, CM-T azimuth changes [+132° (95% confidence interval (CI): 80°–184°); p < 0.0001] were counteracted by the history of MI [−180° (95% CI: −320° to −40°); p = 0.011] and female sex [−162° (95% CI: −268° to −55°); p = 0.003]. A CM-T area increase [+15 (95% CI: 6–24) mV*ms; p < 0.0001] was amplified by NSVT history [+27 (95% CI: 4–46) mV*ms; p = 0.007]. These findings suggest that preexistent electrical remodeling affects CM in response to RV pacing, that CM exhibits saturation behavior, and that women reach CM saturation more easily than men.

A Method to Minimise the Impact of ECG Marker Inaccuracies on the Spatial QRS-T angle: Evaluation on 1,512 Manually Annotated ECGs

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Inaccuracies of QRS and T-wave markers significantly impact QRS-Ta estimation.

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These errors influence the classification of clinically relevant abnormal values.

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Our algorithm provides robust measurements in the presence of inaccurate VCG markers.

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We present for the first time, the distribution of the QRS-Ta in a large cohort.

The spatial QRS-T angle (QRS-Ta) derived from the vectorcardiogram (VCG) is a strong risk predictor for ventricular arrhythmia and sudden cardiac death with potential use for mass screening. Accurate QRS-Ta estimation in the presence of ECG delineation errors is crucial for its deployment as a prognostic test. Our study assessed the effect of inaccurate QRS and T-wave marker placement on QRS-Ta estimation and proposes a robust method for its calculation.

Reference QRS-Ta measurements were derived from 1,512 VCGs manually annotated by three expert reviewers. We systematically changed onset and offset timings of QRS and T-wave markers to simulate inaccurate placement. The QRS-Ta was recalculated using a standard approach and our proposed algorithm, which limits the impact of VCG marker inaccuracies by defining the vector origin as an interval preceding QRS-onset and redefines the beginning and end of QRS and T-wave loops.

Using the standard approach, mean absolute errors (MAE) in peak QRS-Ta were >40% and sensitivity and precision in the detection of abnormality (>105°) were <80% and <65% respectively, when QRS-onset was delayed or QRS-offset anticipated >15 ms. Using our proposed algorithm, MAE for peak QRS-Ta were reduced to <4% and sensitivity and precision of abnormality were >94% for inaccuracies up to ±15 ms. Similar results were obtained for mean QRS-Ta.

In conclusion, inaccuracies of QRS and T-wave markers can significantly influence the QRS-Ta. Our proposed algorithm provides robust QRS-Ta measurements in the presence of inaccurate VCG annotation, enabling its use in large datasets.

Sex differences in vectorcardiogram of African-Americans with and without cardiovascular disease: a cross-sectional study in the Jackson Heart Study cohort

OBJECTIVES

We hypothesised that (1) the prevalent cardiovascular disease (CVD) is associated with global electrical heterogeneity (GEH) after adjustment for demographic, anthropometric, socioeconomic and traditional cardiovascular risk factors, (2) there are sex differences in GEH and (3) sex modifies an association of prevalent CVD with GEH.

DESIGN

Cross-sectional, cohort study.

SETTING

Prospective African-American The Jackson Heart Study (JHS) with a nested family cohort in 2000-2004 enrolled residents of the Jackson, Mississippi metropolitan area.

PARTICIPANTS

Participants from the JHS with analysable ECGs recorded in 2009-2013 (n=3679; 62±12 y; 36% men; 863 family units). QRS, T and spatial ventricular gradient (SVG) vectors' magnitude and direction, spatial QRS-T angle and sum absolute QRST integral (SAI QRST) were measured.

OUTCOME

Prevalent CVD was defined as the history of (1) coronary heart disease defined as diagnosed/silent myocardial infarction, or (2) revascularisation procedure defined as prior coronary/peripheral arterial revascularisation, or (3) carotid angioplasty/carotid endarterectomy, or (4) stroke.

RESULTS

In adjusted mixed linear models, women had a smaller spatial QRS-T angle (-12.2 (95% CI -19.4 to -5.1)°; p=0.001) and SAI QRST (-29.8 (-39.3 to -20.3) mV*ms; p<0.0001) than men, but larger SVG azimuth (+16.2(10.5-21.9)°; p<0.0001), with a significant random effect between families (+20.8 (8.2-33.5)°; p=0.001). SAI QRST was larger in women with CVD as compared with CVD-free women or men (+15.1 (3.8-26.4) mV*ms; p=0.009). Men with CVD had a smaller T area (by 5.1 (95% CI 1.2 to 9.0) mV*ms) and T peak magnitude (by 44 (95%CI 16 to 71) µV) than CVD-free men. T vectors pointed more posteriorly in women as compared with men (peak T azimuth + 17.2(8.9-25.6)°; p<0.0001), with larger sex differences in T azimuth in some families by +26.3(7.4-45.3)°; p=0.006.

CONCLUSIONS

There are sex differences in the electrical signature of CVD in African-American men and women. There is a significant effect of unmeasured genetic and environmental factors on cardiac repolarisation.

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020

OBJECTIVE

Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020.

APPROACH

A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility.

MAIN RESULTS

A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops ([Formula: see text]10%) in performance on the hidden test data.

SIGNIFICANCE

Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020

OBJECTIVE

Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020.

APPROACH

A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility.

MAIN RESULTS

A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops ([Formula: see text]10%) in performance on the hidden test data.

SIGNIFICANCE

Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.

Association Between Silent Myocardial Infarction and Long-Term Risk of Sudden Cardiac Death

Background Although silent myocardial infarction (SMI) is prognostically important, the risk of sudden cardiac death (SCD) among patients with incident SMI is not well established. Methods and Results We examined 2 community-based cohorts: the ARIC (Atherosclerosis Risk in Communities) study (n=13 725) and the CHS (Cardiovascular Health Study) (n=5207). Incident SMI was defined as electrocardiographic evidence of new myocardial infarction during follow-up visits that was not present at the baseline. The primary study end point was physician-adjudicated SCD. In the ARIC study, 513 SMIs, 441 clinically recognized myocardial infarctions (CMIs), and 527 SCD events occurred during a median follow-up of 25.4 years. The multivariable hazard ratios of SMI and CMI for SCD were 5.20 (95% CI, 3.81-7.10) and 3.80 (95% CI, 2.76-5.23), respectively. In the CHS, 1070 SMIs, 632 CMIs, and 526 SCD events occurred during a median follow-up of 12.1 years. The multivariable hazard ratios of SMI and CMI for SCD were 1.70 (95% CI, 1.32-2.19) and 4.08 (95% CI, 3.29-5.06), respectively. The pooled hazard ratios of SMI and CMI for SCD were 2.65 (2.18-3.23) and 3.99 (3.34-4.77), respectively. The risk of SCD associated with SMI is stronger with White individuals, men, and younger age. The population-attributable fraction of SCD was 11.1% for SMI, and SMI was associated with an absolute risk increase of 8.9 SCDs per 1000 person-years. Addition of SMI significantly improved the predictive power for both SCD and non-SCD. Conclusions Incident SMI is independently associated with an increased risk of SCD in the general population. Additional research should address screening for SMI and the role of standard post-myocardial infarction therapy.

Does Sex Modify an Association of Electrophysiological Substrate with Sudden Cardiac Death? The Atherosclerosis Risk in Communities (ARIC) Study

Background

Sex is a well-recognized risk factor for sudden cardiac death (SCD). We hypothesized that sex modifies the association of electrophysiological (EP) substrate with SCD.

Objective

The purpose of this study was to determine whether there are sex differences in electrocardiographic (ECG) measures and whether sex modifies the association of ECG measures of EP substrate with SCD.

Methods

Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n = 14,725; age 54.2 ± 5.8 years; 55% female; 74% white) were included. EP substrate was characterized by heart rate, QRS, QTc, Cornell voltage, spatial ventricular gradient (SVG), and sum absolute QRST integral (SAI QRST) ECG metrics. Two competing outcomes were adjudicated: SCD and non-SCD. Interaction of ECG metrics with sex was studied in Cox proportional hazards and Fine-Gray competing risk models. Model 1 was adjusted for prevalent cardiovascular disease (CVD) and risk factors. Time-updated model 2 was additionally adjusted for incident nonfatal CVD. Relative hazard ratio (RHR) and relative subhazard ratio with 95% confidence interval (CI) for SCD and non-SCD risk for women relative to men were calculated. Model 1 was adjusted for prevalent CVD and risk factors. Time-updated model 2 was additionally adjusted for incident nonfatal CVD.

Results

Over median follow-up of 24.4 years, there were 530 SCDs (incidence 1.72; 95% CI 1.58–1.88 per 1000 person-years). Women compared to men experienced a greater risk of SCD associated with Cornell voltage (RHR 1.18; 95% CI 1.06–1.32; P = .003), SAI QRST (RHR 1.16; 95% CI 1.04–1.30; P = .007), and SVG magnitude (RHR 1.24; 95% CI 1.05–1.45; P = .009), independently from incident CVD.

Conclusion

In women, the global EP substrate is associated with up to 24% greater risk of SCD than in men, suggesting differences in underlying mechanisms and the need for sex-specific SCD risk stratification.

Automatic identification of a stable QRST complex for non-invasive evaluation of human cardiac electrophysiology

BACKGROUND

A vectorcardiography approach to electrocardiology contributes to the non-invasive assessment of electrical heterogeneity in the ventricles of the heart and to risk stratification for cardiac events including sudden cardiac death. The aim of this study was to develop an automatic method that identifies a representative QRST complex (QRSonset to Tend) from a Frank vectorcardiogram (VCG). This method should provide reliable measurements of morphological VCG parameters and signal when such measurements required manual scrutiny.

METHODS

Frank VCG was recorded in a population-based sample of 1094 participants (550 women) 50-65 years old as part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot. Standardized supine rest allowing heart rate stabilization and adaptation of ventricular repolarization preceded a recording period lasting ≥5 minutes. In the Frank VCG a recording segment during steady-state conditions and with good signal quality was selected based on QRST variability. In this segment a representative signal-averaged QRST complex from cardiac cycles during 10s was selected. Twenty-eight morphological parameters were calculated including both conventional conduction intervals and VCG-derived parameters. The reliability and reproducibility of these parameters were evaluated when using completely automatic and automatic but manually edited annotation points.

RESULTS

In 1080 participants (98.7%) our automatic method reliably selected a representative QRST complex where its instability measure effectively identified signal variability due to both external disturbances ("noise") and physiologic and pathophysiologic variability, such as e.g. sinus arrhythmia and atrial fibrillation. There were significant sex-related differences in 24 of 28 VCG parameters. Some VCG parameters were insensitive to the instability value, while others were moderately sensitive.

CONCLUSION

We developed an automatic process for identification of a signal-averaged QRST complex suitable for morphologic measurements which worked reliably in 99% of participants. This process is applicable for all non-invasive analyses of cardiac electrophysiology including risk stratification for cardiac death based on such measurements.