Importance of the heart vector origin point definition for an ECG analysis: The Atherosclerosis Risk in Communities (ARIC) study

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.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

BACKGROUND AND OBJECTIVES

Electrocardiographic (ECG) and vectorcardiographic (VCG) analyses are used to diagnose current cardiovascular disease and for risk stratification for future adverse cardiovascular events. With increasing use of digital ECGs, research into novel ECG/VCG parameters has increased, but widespread computer-based ECG/VCG analysis is limited because there are no currently available, open-source, and easily customizable software packages designed for automated and reproducible analysis.

METHODS AND RESULTS

We present BRAVEHEART, an open-source, modular, customizable, and easy to use software package implemented in the MATLAB programming language, for scientific analysis of standard 12-lead ECGs acquired in a digital format. BRAVEHEART accepts a wide variety of digital ECG formats and provides complete and automatic ECG/VCG processing with signal denoising to remove high- and low-frequency artifact, non-dominant beat identification and removal, accurate fiducial point annotation, VCG construction, median beat construction, customizable measurements on median beats, and output of measurements and results in numeric and graphical formats.

CONCLUSIONS

The BRAVEHEART software package provides easily customizable scientific analysis of ECGs and VCGs. We hope that making BRAVEHART available will allow other researchers to further the field of ECG/VCG analysis without having to spend significant time and resources developing their own ECG/VCG analysis software and will improve the reproducibility of future studies. Source code, compiled executables, and a detailed user guide can be found at http://github.com/BIVectors/BRAVEHEART. The source code is distributed under the GNU General Public License version 3.

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.

Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

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.

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.

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.

Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): A randomized controlled trial

BACKGROUND

Adaptive cardiac resynchronization therapy (aCRT) is known to have clinical benefits over conventional CRT, but the mechanisms are unclear.

OBJECTIVE

Compare effects of aCRT and conventional CRT on electrical dyssynchrony.

METHODS

A prospective, double-blind, 1:1 parallel-group assignment randomized controlled trial in patients receiving CRT for routine clinical indications. Participants underwent cardiac computed tomography and 128-electrode body surface mapping. The primary outcome was change in electrical dyssynchrony measured on the epicardial surface using noninvasive electrocardiographic imaging before and 6 months post-CRT. Ventricular electrical uncoupling (VEU) was calculated as the difference between the mean left ventricular (LV) and right ventricular (RV) activation times. An electrical dyssynchrony index (EDI) was computed as the standard deviation of local epicardial activation times.

RESULTS

We randomized 27 participants (aged 64 ± 12 years; 34% female; 53% ischemic cardiomyopathy; LV ejection fraction 28% ± 8%; QRS duration 155 ± 21 ms; typical left bundle branch block [LBBB] in 13%) to conventional CRT (n = 15) vs aCRT (n = 12). In atypical LBBB (n = 11; 41%) with S waves in V5-V6, conduction block occurred in the anterior RV, as opposed to the interventricular groove in strict LBBB. As compared to baseline, VEU reduced post-CRT in the aCRT (median reduction 18.9 [interquartile range 4.3-29.2 ms; P = .034]), but not in the conventional CRT (21.4 [-30.0 to 49.9 ms; P = .525]) group. There were no differences in the degree of change in VEU and EDI indices between treatment groups.

CONCLUSION

The effect of aCRT and conventional CRT on electrical dyssynchrony is largely similar, but only aCRT harmoniously reduced interventricular dyssynchrony by reducing RV uncoupling.

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.

Spatial distribution of physiologic 12-lead QRS complex

The normal physiologic range of QRS complex duration spans between 80 and 125 ms with known differences between females and males which cannot be explained by the anatomical variations of heart sizes. To investigate the reasons for the sex differences as well as for the wide range of normal values, a technology is proposed based on the singular value decomposition and on the separation of different orthogonal components of the QRS complex. This allows classification of the proportions of different components representing the 3-dimensional representation of the electrocardiographic signal as well as classification of components that go beyond the 3-dimensional representation and that correspond to the degree of intricate convolutions of the depolarisation sequence. The technology was applied to 382,019 individual 10-s ECG samples recorded in 639 healthy subjects (311 females and 328 males) aged 33.8 ± 9.4 years. The analyses showed that QRS duration was mainly influenced by the proportions of the first two orthogonal components of the QRS complex. The first component demonstrated statistically significantly larger proportion of the total QRS power (expressed by the absolute area of the complex in all independent ECG leads) in females than in males (64.2 ± 11.6% vs 59.7 ± 11.9%, p < 0.00001—measured at resting heart rate of 60 beats per minute) while the second component demonstrated larger proportion of the QRS power in males compared to females (33.1 ± 11.9% vs 29.6 ± 11.4%, p < 0.001). The analysis also showed that the components attributable to localised depolarisation sequence abnormalities were significantly larger in males compared to females (2.85 ± 1.08% vs 2.42 ± 0.87%, p < 0.00001). In addition to the demonstration of the technology, the study concludes that the detailed convolution of the depolarisation waveform is individual, and that smoother and less intricate depolarisation propagation is the mechanism likely responsible for shorter QRS duration in females.

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

•

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

•

These errors influence the classification of clinically relevant abnormal values.

•

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

•

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.

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.

Bringing Critical Race Praxis Into the Study of Electrophysiological Substrate of Sudden Cardiac Death: The ARIC Study

Background Race is an established risk factor for sudden cardiac death (SCD). We sought to determine whether the association of electrophysiological substrate with SCD varies between black and white individuals. Methods and Results Participants from the ARIC (Atherosclerosis Risk in Communities) study with analyzable ECGs (n=14 408; age, 54±6 years; 74% white) were included. Electrophysiological substrate was characterized by ECG metrics. Two competing outcomes were adjudicated: SCD and non-SCD. Interaction of ECG metrics with race was studied in Cox proportional hazards and Fine-Gray competing risk models, adjusted for prevalent cardiovascular disease, risk factors, and incident nonfatal cardiovascular disease. At the baseline visit, adjusted for age, sex, and study center, blacks had larger spatial ventricular gradient magnitude (0.30 mV; 95% CI, 0.25-0.34 mV), sum absolute QRST integral (18.4 mV*ms; 95% CI, 13.7-23.0 mV*ms), and Cornell voltage (0.30 mV; 95% CI, 0.25-0.35 mV) than whites. Over a median follow-up of 24.4 years, SCD incidence was higher in blacks (2.86 per 1000 person-years; 95% CI, 2.50-3.28 per 1000 person-years) than whites (1.37 per 1000 person-years; 95% CI, 1.22-1.53 per 1000 person-years). Blacks with hypertension had the highest rate of SCD: 4.26 (95% CI, 3.66-4.96) per 1000 person-years. Race did not modify an association of ECG variables with SCD, except QRS-T angle. Spatial QRS-T angle was associated with SCD in whites (hazard ratio, 1.38; 95% CI, 1.25-1.53) and hypertension-free blacks (hazard ratio, 1.52; 95% CI, 1.09-2.12), but not in blacks with hypertension (hazard ratio, 1.15; 95% CI, 0.99-1.32) (P-interaction=0.004). Conclusions Race did not modify associations of electrophysiological substrate with SCD and non-SCD. Electrophysiological substrate does not explain racial disparities in SCD rate.

Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study

BACKGROUND

The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD).

METHODS

Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured.

RESULTS

Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD).

CONCLUSION

Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Changes in global electrical heterogeneity associated with dofetilide, quinidine, ranolazine, and verapamil

BACKGROUND

Electrocardiographic (ECG) markers of antiarrhythmic drug (AAD) activity could be used to optimize efficacy and minimize toxicity. Vectorcardiographic global electrical heterogeneity (GEH) is associated with ventricular arrhythmias and sudden death, but it is unclear how GEH measurements change in response to AADs.

OBJECTIVE

The purpose of this study was to characterize acute effects of AADs on GEH measurements.

METHODS

We analyzed double-blind placebo-controlled trial data from healthy volunteers given 1 dose of placebo, dofetilide, quinidine, ranolazine, or verapamil on subsequent visits. Serial ECGs and plasma drug concentrations were collected. Vectorcardiographic GEH parameters (spatial ventricular gradient [SVG], spatial QRST angle, sum absolute QRST integral, and SVG-QRS peak angle) were measured. Placebo-corrected change from baseline was regressed on drug concentration stratified by sex using linear mixed effects models.

RESULTS

Among 22 persons (11 (50%) male median age 27 ± 5 years), 5232 ECGs were analyzed. Dofetilide and quinidine were associated with significant changes in more GEH parameters (5) compared with verapamil (2) and ranolazine (1). The most notable change occurred in SVG azimuth, with largest changes (degrees per unit normalized drug concentration) in dofetilide (6.1; 95% confidence interval [CI] 4.2-8.0) and quinidine (9.4; 95% CI 6.7-12.0), and smaller effects in verapamil (4.4; 95% CI 2.9-5.9) and ranolazine (5.4; 95% CI 3.5-7.3). AAD-induced GEH changes significantly differed in men and women.

CONCLUSION

AADs change GEH measurements. These changes, which differ by sex, are likely driven by alterations in ion channel function and dispersion of depolarization or repolarization. GEH measurement may allow early assessment of favorable or adverse AAD effects.

Plasticizer Interaction With the Heart: Chemicals Used in Plastic Medical Devices Can Interfere With Cardiac Electrophysiology

BACKGROUND

Phthalates are used as plasticizers in the manufacturing of flexible, plastic medical products. Patients can be subjected to high phthalate exposure through contact with plastic medical devices. We aimed to investigate the cardiac safety and biocompatibility of mono-2-ethylhexyl phthalate (MEHP), a phthalate with documented exposure in intensive care patients.

METHODS

Optical mapping of transmembrane voltage and pacing studies were performed on isolated, Langendorff-perfused rat hearts to assess cardiac electrophysiology after MEHP exposure compared with controls. MEHP dose was chosen based on reported blood concentrations after an exchange transfusion procedure.

RESULTS

Thirty-minute exposure to MEHP increased the atrioventricular node (147 versus 107 ms) and ventricular (117 versus 77.5 ms) effective refractory periods, compared with controls. Optical mapping revealed prolonged action potential duration at slower pacing cycle lengths, akin to reverse use dependence. The plateau phase of the action potential duration restitution curve steepened and became monophasic in MEHP-exposed hearts (0.18 versus 0.06 slope). Action potential duration lengthening occurred during late-phase repolarization resulting in triangulation (70.3 versus 56.6 ms). MEHP exposure also slowed epicardial conduction velocity (35 versus 60 cm/s), which may be partly explained by inhibition of Na_v1.5 (874 and 231 µmol/L half-maximal inhibitory concentration, fast and late sodium current).

CONCLUSIONS

This study highlights the impact of acute MEHP exposure, using a clinically relevant dose, on cardiac electrophysiology in the intact heart. Heightened clinical exposure to plasticized medical products may have cardiac safety implications-given that action potential triangulation and electrical restitution modifications are a risk factor for early after depolarizations and cardiac arrhythmias.