BACKGROUND:We previously developed an automated approach based on pace mapping to localise early left ventricular (LV) activation origin. To avoid a singular system, we require pacing from at least 2 more known sites than the number of electrocardiography (ECG) leads used. Fewer leads used means fewer pacing sites required. We sought to identify an optimal minimal ECG lead set for the automated approach.METHODS:We used 1715 LV endocardial pacing sites to create derivation and testing data sets. The derivation data set, consisting of 1012 known pacing sites pooled from 38 patients, was used to identify an optimal 3-lead set by means of random forest regression (RFR), and a second 3-lead set by means of exhaustive search. The performance of these sets and the calculated Frank leads was compared within the testing data set with 703 pacing sites pooled from 25 patients.RESULTS:The RFR yielded III, V1, and V4, whereas the exhaustive search identified leads II, V2 and V6. Comparison of these sets and the calculated Frank leads demonstrated similar performance when using 5 or more known pacing sites. Accuracy improved with additional pacing sites, achieving mean accuracy of < 5 mm, after including up to 9 pacing sites when they were focused on a suspected area of ventricular activation origin (radius < 10 mm).CONCLUSIONS:The RFR identified the quasi-orthogonal leads set to localise the source of LV activation, minimizing the training set of pacing sites. Localization accuracy was high with the use of these leads and was not significantly different from using leads identified by exhaustive search or empiric use of Frank leads.

We have previously developed an intraprocedural system to identify VT exit sites in real-time on the patient-specific CT/MRI geometry using 3-lead ECGs (leads III, V2, V6) during catheter ablation for ventricular tachycardia (RAPID-VT).

Background: We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA). Objectives: To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors. Methods: Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used "population" regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm. Results: The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5 mm, p < 0.05) and dataset #2 (7.2 vs. 9.5 mm, p < 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (p < 0.05). Conclusion: The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.

Background We have previously developed an intraprocedural automatic arrhythmia‐origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3‐lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers. The AAOL system combines 120‐ms QRS integrals of 3 leads (III, V2, V6) with pace mapping to predict VT exit/PVC origin site and projects that site onto the patient‐specific electroanatomic mapping surface. VT exit/PVC origin sites were clinically identified by activation mapping and/or pace mapping. The localization error of the VT exit/PVC origin site was assessed by the distance between the clinically identified site and the estimated site. In the retrospective study of 19 patients with structural heart disease, the AAOL system achieved a mean localization accuracy of 6.5±2.6 mm for 25 induced VTs. In the prospective study with 23 patients, mean localization accuracy was 5.9±2.6 mm for 26 VT exit and PVC origin sites. There was no difference in mean localization error in epicardial sites compared with endocardial sites using the AAOL system (6.0 versus 5.8 mm, P =0.895). Conclusions The AAOL system achieved accurate localization of VT exit/PVC origin sites in patients with structural heart disease; its performance is superior to current systems, and thus, it promises to have potential clinical utility.

We have previously developed an intraprocedural automatic arrhythmia origin localization (AAOL) system to localize idiopathic ventricular arrhythmia origins onto patient-specific electroanatomic (EAM) surface in real time using a 3-lead ECG.

We have previously developed an intraprocedural RAPID-VT localization system to identify VT exit sites in real-time on the patient-specific CT/MRI mesh surface using 3-lead ECGs (leads III, V2, V6). To assess prospectively the localization performance and ablation outcomes of the RAPID-VT system. Eleven consecutive consenting patients with ischemic cardiomyopathy undergoing VT ablation had preprocedural CT/MRI scans. A reconstructed LV endocardial mesh surface was registered to the electroanatomic shell during the procedure and imported into the RAPID-VT program. During the procedure, predicted VT exit sites were used to guide mapping and ablation. The localization accuracy was quantified by comparing estimated sites against sites clinically identified using activation, entrainment and pace mapping. Thirteen accurately identified VT exit sites, pooled from 9 patients (mean procedure time 286.7±31.8 mins, were identified by using a combination of activation, entrainment and pace-mapping, and were used as the “ground truth” for comparisons. The RAPID-VT system had a mean localization error of 8.6±3.2 mm for the 13 VTs. Two patients were excluded due to technical problems with data acquisition. After targeting all inducible VTs, no VT was inducible at the end of the procedure in 7/9 patients. The RAPID-VT system based on the patient-specific CT/MRI mesh surface can be feasibly used in real-time to guide VT mapping and ablation.

Background: To facilitate ablation of ventricular tachycardia (VT), an automated localization system to identify the site of origin of left ventricular activation in real time using the 12-lead ECG was developed. The objective of this study was to prospectively assess its accuracy. Methods: The automated site of origin localization system consists of 3 steps: (1) localization of ventricular segment based on population templates, (2) population-based localization within a segment, and (3) patient-specific site localization. Localization error was assessed by the distance between the known reference site and the estimated site. Results: In 19 patients undergoing 21 catheter ablation procedures of scar-related VT, site of origin localization accuracy was estimated using 552 left ventricular endocardial pacing sites pooled together and 25 VT-exit sites identified by contact mapping. For the 25 VT-exit sites, localization error of the population-based localization steps was within 10 mm. Patient-specific site localization achieved accuracy of within 3.5 mm after including up to 11 pacing (training) sites. Using 3 remotes (67.8±17.0 mm from the reference VT-exit site), and then 5 close pacing sites, resulted in localization error of 7.2±4.1 mm for the 25 identified VT-exit sites. In 2 emulated clinical procedure with 2 induced VTs, the site of origin localization system achieved accuracy within 4 mm. Conclusions: In this prospective validation study, the automated localization system achieved estimated accuracy within 10 mm and could thus provide clinical utility.

BACKGROUND To facilitate catheter ablation of ventricular tachycardia (VT), we previously developed an automated method to identify sources of left ventricular (LV) activation in real time using 12-lead electrocardiography (ECG), the accuracy of which depends on acquisition of a complete electroanatomic (EA) map. OBJECTIVE The purpose of this study was to assess the feasibility of using a registered cardiac computed tomogram (CT) rather than an EA map to permit real-time localization and avoid errors introduced by incomplete maps. METHODS Before LV VT ablation, 10 patients underwent CT imaging and 3-dimensional reconstruction of the cardiac surface to create a triangle mesh surface, which was registered to the EA map during the procedure and imported into custom localization software. The software uses QRS integrals from leads III, V-2, and V-6; derives personalized regression coefficients from pacing at >= 5 sites with known locations; and estimates the location of unknown activation sites on the 3-dimensional patient-specific LV endocardial surface. Localization accuracy was quantified for VT exit sites in millimeters by comparing the calculated against the known locations. RESULTS The VT exit site was identified for 20 VTs using activation and entrainment mapping, supplemented by pace-mapping at the scar margin. The automated localization software achieved incremental accuracy with additional pacing sites and had a mean localization error of 6.9 +/- 5.7 mm for the 20 VTs. CONCLUSION Patient-specific CT geometry is feasible for use in real-time automated localization of ventricular activation and may avoid reliance on a complete EA map.

OBJECTIVES The objective of this study was to present a new system, the Automatic Arrhythmia Origin Localization (AAOL) system, which used incomplete electroanatomic mapping (EAM) for localization of idiopathic ventricular arrhythmia (IVA) origin on the patient-specific geometry of left ventricular, right ventricular, and neighboring vessels. The study assessed the accuracy of the system in localizing IVA source sites on cardiac structures where pace mapping is challenging. BACKGROUND An intraprocedural automated site of origin localization system was previously developed to identify the origin of early left ventricular activation by using 12-lead electrocardiograms (ECGs). However, it has limitations, as it could not identify the site of origin in the right ventricle and relied on acquiring a complete EAM. METHODS Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The new system combined 3-lead (III, V-2, and V-6) 120-ms QRS integrals and patient-specific EAM geometry with pace mapping to predict the site of earliest ventricular activation. The predicted site was projected onto EAM geometry. RESULTS Twenty-three IVA origin sites were clinically identified by activation mapping and/or pace mapping (8, right ventricle; 15, left ventricle, including 8 from the posteromedial papillary muscle, 2 from the aortic root, and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs. CONCLUSIONS The new intraprocedural AAOL system achieved accurate localization of IVA origin in ventricles and neighboring vessels, which could facilitate ablation procedures for patients with IVAs. (C) 2021 by the American College of Cardiology Foundation.

Background: Machine learning models may help localize the site of origin of ventricular tachycardia (VT) using 12-lead electrocardiograms. However, population-based models suffer from inter-subject anatomical variations within ECG data, while patient-specific models face the open challenge of what pacing data to collect for training. Methods: This study presents and validates the first hybrid model that combines population and patient-specific machine learning for rapid "computer-guided pace-mapping". A population-based deep learning model was first trained offline to disentangle inter-subject variations and regionalize the site of VT origin. Given a new patient with a target VT, an on-line patient-specific model after being initialized by the population-based prediction was then built in real time by actively suggesting where to pace next and improving the prediction with each added pacing data, progressively guiding pace-mapping towards the site of VT origin. Results: The population model was trained on pace-mapping data from 38 patients and the patient-specific model was subsequently tuned on one patient. The resulting hybrid model was tested on a separate cohort of eight patients in localizing 1) 193 LV endocardial pacing sites, and 2) nine VTs with clinically determined exit sites. The hybrid model achieved a localization error of 5.3 +/- 2.6 mm using 5.4 +/- 2.5 pacing sites in localizing LV pacing sites, achieving a significantly higher accuracy with a significantly smaller amount of training sites in comparison to models without active guidance. Conclusion: The presented hybrid model has the potential to assist rapid pace-mapping of interventional targets in VT.

Introduction: Few intraprocedural localization systems have been developed to predict idiopathic ventricular arrhythmia (IVA) source sites. However, an accurate and bi-ventricular patient-specific automated site of origin localization system remains elusive. To address this issue, we have developed a new automatic arrhythmia origin localization (AAOL) system that determines the sites of earliest activation in both ventricles and provides superior accuracy. Hypothesis: We hypothesized that the AAOL system can use electroanatomic mapping (EAM) geometry and accurately localize IVA source sites on patient-specific geometry of LV, RV and neighboring vessels using 3-lead ECGs. Methods: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The AAOL system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with intracardiac pacing to predict the site of earliest ventricular activation. The predicted site was projected onto the EAM geometry using the EAM triangular-mesh site nearest to the tip of the predicted site. Results: Twenty-three IVA source sites were clinically identified by activation mapping and/or pace mapping (8 RV, 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs (Figure 1D), better than that achieved by previous systems. Conclusions: The new AAOL system offers highly accurate localization of IVA source sites in both ventricles and neighboring vessels, which could facilitate ablation procedures for patients with IVAs.

Background: Rapid accurate localization of the site of ventricular activation origin during catheter ablation for ventricular arrhythmias could facilitate the procedure. Electrocardiographic imaging (ECGI) using large lead sets can localize the origin of ventricular activation. We have developed an automated method to identify sites of early ventricular activation in real time using the 12-lead ECG. We aim to compare the localization accuracy of ECGI and the automated method, identifying pacing sites/VT exit based on a patient-specific model. Methods: A patient undergoing ablation of VT on the left-ventricular endocardium and epicardium had 120-lead body-surface potential mapping (BSPM) recorded during the procedure. (1) ECGI methodology: The L1-norm regularization was employed to reconstruct epicardial potentials based on patient-specific geometry for localizing endocardial ventricular activation origin. We used the BSPM data corresponding to known endocardial pacing sites and a VT exit site identified by 3D contact mapping to analyze them offline. (2) The automated method: location coordinates of pacing sites together with the time integral of the first 120 ms of the QRS complex of 3 ECG predictors (leads III, V2 and V6) were used to calculate patient-specific regression coefficients to predict the location of unknown sites of ventricular activation origin ("target" sites). Localization error was quantified over all pacing sites in millimeters by comparing the calculated location and the known reference location. Results: Localization was tested for 14 endocardial pacing sites and 1 epicardial VT exit site. For 14 endocardial pacing sites the mean localization error of the automated method was significantly lower than that of the ECGI (8.9 vs. 24.9 mm, p < 0.01), when 10 training pacing sites are used. Emulation of a clinical procedure demonstrated that the automated method achieved localization error of <5 mm for the VT-exit site; while the ECGI approach approximately correlates with the site of VT exit from the scar within a distance of 18.4 mm. Conclusions: The automated method using only 3 ECGs shows promise to localize the origin of ventricular activation as tested by pacing, and the VT-exit site and compares favourably to inverse solution calculation, avoiding cumbersome lead sets. As 12-lead ECG data is acquired by current 3D mapping systems, it is conceivable that the algorithm could be directly incorporated into a mapping system. Further validation in a prospective cohort study is needed to confirm and extend observations reported in this study. (C) 2018 Published by Elsevier Inc.

Background: Criteria for electrocardiographic detection of acute myocardial ischemia recommended by the Consensus Document of ESC/ACCF/AHA/WHF consist of two parts: The ST elevation myocardial infarction (STEMI) criteria based on ST elevation (ST up arrow) in 10 pairs of contiguous leads and the other on ST depression (ST down arrow) in the same 10 contiguous pairs. Our aim was to assess sensitivity (SE) and specificity (SP) of these criteria and to seek their possible improvements in three databases of 12-lead ECGs. Methods: We used (1) STAFF III data of controlled ischemic episodes recorded from 99 patients (pts) during per cutaneous coronary intervention (PCI) involving either left anterior descending (LAD) coronary artery, right coronary artery (RCA), or left circumflex (LCx) coronary artery. (2) Data from the University of Glasgow for 58 pts with acute myocardial infarction (AMI) and 58 pts without AMI, as confirmed by MRI. (3) Data from Lund University retrieved from a centralized ECG management system for 100 pts with various pathological ST changes other than acute coronary occlusion including ventricular pre-excitation, acute pericarditis, early repolarization syndrome, left ventricular hypertrophy, and left bundle branch block. ST measurements at J-point in ECGs of all 315 pts were obtained automatically on the averaged beat with manual review and the recommended criteria as well as their proposed modifications, were applied. Performance measures included SE, SP, positive predictive value (PPV), and benefit-to-harm ratio (BHR), defined as the ratio of true-positive vs. false-positive detections. Results: We found that the SE of widely-used STEMI criteria can be indeed improved by the additional ST down arrow criteria, but at the cost of markedly decreased SP. In contrast, using STD in only 3 additional contiguous pairs of leads (STEMI13) can boost SE without any loss of SP. In the STAFF Ill database, SE/SP/PPV were 56/98/97% for the STEMI, 79/79/79% for the STEMI with added ST down arrow and 67/97/96% for the STEMI13. In the Glasgow database, corresponding SE/SP/PPV were 43/98/96%, 84/90/89%, and 55/98/97%. For the Lund database, SP was 56% for the STEMI, 24% for the STEMI with ST down arrow and 56% for the STEMI13. Conclusion: Current recommended criteria for detecting acute myocardial ischemia, involving ST down arrow boost SE of widely-used STEMI criteria, at the cost of SP. To keep the SP high, we propose either the adjustment of threshold for the added ST down arrow criteria or a selective use of ST down arrow only in contiguous leads V2 and V3 plus ST up arrow in lead pairs (aVL, III) and (III,-aVL). (C) 2018 Elsevier Inc. All rights reserved.

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present study sought to investigate whether machine learning—namely, random-forest regression (RFR) and support-vector regression (SVR)—can improve the localization accuracy compared to MLR. For 38 patients the 12-lead ECG was acquired during LV endocardial pacing at 1012 sites with known coordinates exported from an electroanatomic mapping system; each pacing site was then registered to a generic LV endocardial surface subdivided into 16 segments tessellated into 238 triangles. ECGs were reduced to one variable per lead, consisting of 120-ms time integral of the QRS. To compare three regression models, the entire dataset (\(n=1012\)) was partitioned at random into a design set with 80% and a test set with the remaining 20% of the entire set, and the localization error—measured as geodesic distance on the generic LV surface—was assessed. Bootstrap method with replacement, using 1000 resampling trials, estimated each model’s error distribution for the left-out sample (\(n\simeq 371\)). In the design set (\(n=810\)), the mean accuracy was 8.8, 12.1, and 12.9 mm, respectively for SVR, RVR and MLR. In the test set (\(n=202\)), the mean value of the localization error in the SVR model was consistently lower than the other two models, both in comparison with the MLR (11.4 vs. 12.5 mm), and with the RFR (11.4 vs. 12.0 mm); the RFR model was also better than the MLR model for estimating localization accuracy (12.0 vs. 12.5 mm). The bootstrap method with 1,000 trials confirmed that the SVR and RFR models had significantly higher predictive accurate than the MLR in the bootstrap assessment with the left-out sample (SVR vs. MLR (\(p<0.01\)), RFR vs. MLR (\(p < 0.01\))). The performance comparison of regression models showed that a modest improvement in localization accuracy can be achieved by SVR and RFR models, in comparison with MLR. The “population” coefficients generated by the optimized SVR model from our dataset are superior to the previously-derived “population” coefficients generated by the MLR model and can supersede them to improve the localization of ventricular activation on the generic LV endocardial surface.

OBJECTIVES:The aim of this study was to develop rapid computational methods for identifying the site of origin of ventricular activation from the 12-lead electrocardiogram.BACKGROUND:Catheter ablation of ventricular tachycardia in patients with structural heart disease frequently relies on a substrate-based approach, which may use pace mapping guided by body-surface electrocardiography to identify culprit exit sites.METHODS:Patients undergoing ablation of scar-related VT (n = 38) had 12-lead electrocardiograms recorded during pacing at left ventricular endocardial sites (n = 1,012) identified on 3-dimensional electroanatomic maps and registered to a generic left ventricular endocardial surface divided into 16 segments and tessellated into 238 triangles; electrocardiographic data were reduced for each lead to 1 variable, consisting of QRS time integral. Two methods for estimating the origin of activation were developed: 1) a discrete method, estimating segment of activation origin using template matching; and 2) a continuous method, using population-based multiple linear regression to estimate triangle of activation origin. A variant of the latter method was derived, using patient-specific multiple linear regression.RESULTS:The optimal QRS time integral included the first 120 ms of the QRS interval. The mean localization error of population-based regressions was 12 ± 8 mm. Patient-specific regressions can achieve localization accuracy better than 5 mm when at least 10 training-set pacing sites are used; this accuracy further increases with each added pacing site.CONCLUSIONS:Computational intraprocedure methods can automatically identify the segment and site of left ventricular activation using novel algorithms, with accuracy within <10 mm.

Existing criteria recommended by ACC/ESC for identifying patients with ST-elevation myocardial infarction (STEMI) from the 12-lead ECG perform with high specificity (SP), but low sensitivity (SE). In our previous study, we found that the SE of acute ischemia detection can be markedly improved without any loss of SP by calculating, from the 12-lead ECG, ST elevation in 3 vessel specific leads (VSLs). To further validate the method, we evaluated the SP using a dataset with non-ischemic ST segment changes, consisting of 12-lead ECGs of 100 patients. These ECGs were chosen to represent five causes of pathological ST deviation, other than acute coronary occlusion: ventricular pre-excitation, acute pericarditis, early-repolarization syndrome, left ventricular hypertrophy. and left bundle branch block. Both STEMI and VSL criteria were tested by calculating SP as the performance measure. We found that SP of the STEMI criteria was 100%, 4%, 29%, 100%, and 64%, respectively, for the five subgroups. The corresponding values of SP for the VSLs were 92%. 88%, 100%, 77%, and 68%. For the entire group, SP was 57% for the STEMI criteria and significantly higher for the VSLs at 83%. Thus, the VSLs not only are more sensitive in detecting acute ischemia, but also significantly more specific in rejecting patients with non-ischemic ST deviation than the existing STEMI criteria.

Background: Existing criteria recommended by ACC/ESC for identifying patients with ST elevation myocardial infarction (STEMI) from the 12-lead ECG perform with high specificity (SP), but low sensitivity (SE). In our previous studies, we found that the SE of ischemia detection can be markedly improved without any loss of SP by calculating, from the 12-lead ECG, ST deviation in 3 "optimal" vessel-specific leads (VSLs). Our original VSLs, based on Delta ST body-surface potential maps (BSPMs), have been modified by using the more appropriate J-point BSPMs at peak ischemia (without subtraction of pre-occlusion distributions). The aim of the present study was to compare the performance of these new VSLs with that achieved by the STEMI criteria used in current practice.Methods: Two independent datasets of 12-lead ECGs were used: the STAFF DJ dataset acquired during ischemic episodes caused by balloon inflation in LAD (n = 35), RCA (n = 47), and LCx = 17) coronary arteries, and the Glasgow dataset comprising admission 12-lead ECGs of 116 patients who were hospitalized for chest pain and underwent contrast-enhanced cardiac MRI that confirmed AMI in 58 patients (50%).Results: We found that, in the STAFF III dataset, the detection of ischemic state by the STEMI criteria attained SE/SP of 60/97%, whereas SE/SP values of VSLs were 72/98%. In the Glasgow dataset, STEMI criteria yielded SE/SP of 43/98%, whereas the VSLs improved SE/SP to 60/98%. The most significant increase in diagnostic performance appeared in patients with LCx coronary artery occlusion: in STAFF. HI data (n = 17) SE achieved by STEMI criteria was improved by the VSLs from 35% to 71%; in Glasgow data (n = 12) SE of 31% achieved by STEMI criteria was improved by the VSLs to 69%.Conclusion: In our study population, existing ACC/ESC STEMI criteria complemented by the new VSLs yielded much improved sensitivity of ischemia detection without any detrimental effect on specificity. This finding needs to be corroborated on a larger chest-pain patient population with typical prevalence of acute ischemia presented to the emergency rooms. (C) 2015 Elsevier Inc. All rights reserved.

Background We evaluated predictors of coronary heart disease (CHD) death and sudden cardiac death (SCD) in the Atherosclerosis Risk in Communities (ARIC) study. Methods and Results The study population included 13 621 men and women 45 to 65 years of age free from manifest cardiovascular disease at entry. Hazard ratios from Cox regression with 95% confidence intervals were computed for 18 dichotomized repolarization‐related ECG variables. The average follow‐up was 14 years. Independent predictors of CHD death in men were TaVR‐ and rate‐adjusted QTend (QTea), with a 2‐fold increased risk for both, and spatial angles between mean QRS and T vectors and between Tpeak (Tp) and normal R reference vectors [θ(Rm|Tm) and θ(Tp|Tref), respectively], with a >1.5‐fold increased risk for both. In women, independent predictors of the risk of CHD death were θ(Rm|Tm), with a 2‐fold increased risk for θ(Rm|Tm), and θ(Tp|Tref), with a 1.7‐fold increased risk. Independent predictors of SCD in men were θ(Tp|Tref) and QTea, with a 2‐fold increased risk, and θ(Tinit|Tterm), with a 1.6‐fold increased risk. In women, θ(Tinit|Tterm) was an independent predictor of SCD, with a >3‐fold increased risk, and θ(Rm|Tm) and TV1 were >2‐fold for both. Conclusions θ(Rm|Tm) and θ(Tp|Tref), reflecting different aspects of ventricular repolarization, were independent predictors of CHD death and SCD, and TaVR and TV1 were also independent predictors. The risk levels for independent predictors for both CHD death and SCD were stronger in women than in men, and QTea was a significant predictor in men but not in women.

BACKGROUND:Data are limited about race-and sex-associated differences in prognostically important ECG measures of regional repolarization. METHODS AND RESULTS:The normal reference group from the Atherosclerosis Risk in Communities (ARIC) study included 8,676 white and African-American men and women aged 40-65 years. Exclusion criteria included cardiovascular disease, hypertension, diabetes and major ECG abnormalities. Notable sex differences (p<0.001) were observed in the upper 98% limits for rate-adjusted QTend (QTea) which was 435 ms in white and African-American men and 445 ms in white and African-American women, and for left ventricular epicardial repolarization time (RTepi) which was 345 ms in white and African-American men and 465 ms in white and African-American women. These sex differences reflect earlier onset and end of repolarization in men than in women. Upper normal limits for STJ amplitude in V2-V3 were 100 μV in white and African-American women, 150 μV in white men and 200 μV in African-American men (p<0.001 for sex differences), and for other chest leads, aVL and aVF 50 μV in white women, 100 μV in African-American women, 100 μV in white men and 150 μV in African-American men (p<0.001 for sex and race differences). CONCLUSIONS:Shorter QTea and RTepi in men than in women reflect earlier onset and end of repolarization in men. STJ amplitudes in African-American men were higher than in other subgroups by race and sex. These sex and race differences need to be considered in clinical and epidemiological applications of normal standards.

Background: Substantial new information has emerged recently about the prognostic value for a variety of new ECG variables. The objective of the present study was to establish reference standards for these novel risk predictors in a large, ethnically diverse cohort of healthy women from the Women's Health Initiative (WHI) study.Methods and Results: The study population consisted of 36,299 healthy women. Racial differences in rate-adjusted QT end (QT(ea)) and QT peak (QT(pa)) intervals as linear functions of RR were small, leading to the conclusion that 450 and 390 ms are applicable as thresholds for prolonged and shortened QT(ea) and similarly, 365 and 295 ms for prolonged and shortened QT(pa), respectively. As a threshold for increased dispersion of global repolarization (TpeakTend interval), 110 ms was established for white and Hispanic women and 120 ms for African-American and Asian women. ST elevation and depression values for the monitoring leads of each person with limb electrodes at Mason-Likar positions and chest leads at level of V1 and V2 were first computed from standard leads using lead transformation coefficients derived from 892 body surface maps, and subsequently normal standards were determined for the monitoring leads, including vessel-specific bipolar left anterior descending, left circumflex artery and right coronary artery leads. The results support the choice 150 mu V as a tentative threshold for abnormal ST-onset elevation for all monitoring leads. Body mass index (HMI) had a profound effect on Cornell voltage and Sokolow-Lyon voltage in all racial groups and their utility for left ventricular hypertrophy classification remains open.Conclusions: Common thresholds for all racial groups are applicable for QT(ea), and QT(pa) intervals and ST elevation. Race-specific normal standards are required for many other ECG parameters. (C) 2013 Elsevier Inc. All rights reserved.