Computer modelling of beat-to-beat repolarization heterogeneity in human cardiac ventricles

Kozmann, György; Tuboly, Gergely; Szathmary, Vavrinec; Svehlikova, Jana; Tyšler, Milan

doi:10.1016/j.bspc.2014.08.008

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…Notably, SAI QRST correlated with the time after MI 11 and many clinical characteristics, which suggested that SAI QRST is a measure of electrical remodeling. Consistently with clinical findings, Kozmann et al 14 in silico showed that SAI QRST is a measure of non-dipolarity of activation and heterogeneity of action potential morphology distribution. Combining together SAI QRST, QRS duration, and QRS morphology into an “electrical dyssynchrony score” could conceivably improve selection of CRT candidates.…”

Section: Discussionsupporting

confidence: 67%

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Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: Results from the SMART-AV Trial

et al. 2015

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Background Cardiac resynchronization therapy (CRT) reduces mortality and morbidity in selected heart failure (HF) patients. However, not all patients respond to CRT. Objective We hypothesized that a novel measure of electrical dyssynchrony, sum absolute QRST integral (SAI), predicts CRT response independent of QRS duration and morphology. Methods We retrospectively analyzed baseline 12-lead electrocardiograms (ECGs) of SMART-AV study participants [N=234, mean age 67 y, 70% male, 60% ischemic cardiomyopathy (ICM), mean left ventricular ejection fraction (LVEF) 25%, mean QRS duration 152ms, 77% had left bundle branch block (LBBB)]. Baseline pre-implant ECGs were digitized, transformed into orthogonal XYZ, and analyzed automatically by customized Matlab software. SAI was measured as an averaged arithmetic sum of absolute areas under the QRST curve. Patients were followed prospectively 6 months after CRT-D implantation. Patients with a decrease in left ventricular end-systolic volume ≥ 15mls after 6 months of CRT were considered responders. Logistic regression model was adjusted for age, gender, BBB morphology, LVEF, type of cardiomyopathy and QRS duration. Results Patients with the high mean SAI (3rd tertile) had 2.5 times greater odds of response than those with low mean SAI (1st tertile; OR 2.5, 95% CI 1.3–5.0, p=0.010), and 1.9 times greater than the lower two tertiles combined (OR 1.9, 95%CI 1.1–3.5; P=0.03). Adjustment for renal function (OR 2.33 (95%CI 1.32, 4.11); P=0.003) and LV lead position in RAO/LAO (OR 1.7 (95%CI 0.9, 3.2); P=0.087) did not attenuate association of SAI with outcome. Conclusion High SAI QRST independently predicts CRT response in the SMART-AV study.

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Section: Discussionsupporting

confidence: 67%

“…Theoretically multipolarity of electrical activation means electrical dyssynchrony. SAI QRST reflects multipolarity of electrical activation, consistent with electrical dyssynchrony, and global electrical heterogeneity in the heart 14 . We hypothesized that SAI QRST is independently associated with CRT response.…”

Section: Introductionmentioning

confidence: 92%

Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: Results from the SMART-AV Trial

et al. 2015

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“…The spatial QRS-T angle [28] is a well-known marker of the risk of SCD and cardiovascular mortality. SAI QRST is a scalar analog of the SVG [15,[29][30][31], associated with ventricular tachyarrhythmias in heart failure. [29,30,32] We previously showed that GEH is associated with SCD after adjustment for traditional ECG metrics and a comprehensive list of cardiovascular risk factors.…”

Section: Global Electrical Heterogeneitymentioning

confidence: 99%

Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

Alday

Bender

German

et al. 2019

Preprint

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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 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 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)/1,000 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 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% SCD events were reclassified from low or intermediate risk to a high-risk category. Long-term, QRS-T angle was the strongest 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.

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“…The sum absolute QRST integral (SAI QRST) is a scalar analog of the SVG [3][4][5][6], associated with ventricular tachyarrhythmias in heart failure. [3,4,7] SAI QRST is associated with electrical dyssynchrony [8], mechanical response to cardiac resynchronization therapy [9], and mortality in cardiac resynchronization therapy patients [10].…”

Section: History Of Spatial Ventricular Gradientmentioning

confidence: 99%

Global Electrical Heterogeneity: Mechanisms and Clinical Significance

Tereshchenko

2018

2018 Computing in Cardiology Conference (CinC)

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This review summarizes recent findings and discusses a clinical significance of a vectorcardiographic (VCG) Global electrical heterogeneity (GEH). GEH concept is based on the concept of the spatial ventricular gradient (SVG), which is a global measure of the dispersion of total recovery time. We quantify GEH by measuring five features of the SVG vector (SVG magnitude, direction (azimuth and elevation), a scalar value, and spatial QRS-T angle) on orthogonal XYZ ECG. In analysis of more than 20,000 adults we showed that GEH is independently associated with sudden cardiac death (SCD) after adjustment for demographics, cardiovascular disease (time-updated incident non-fatal cardiovascular events [coronary heart disease, heart failure, stroke, atrial fibrillation, use of beta-blockers], and known risk factors [cholesterol, triglycerides, physical activity index, smoking, diabetes, obesity, hypertension, antihypertensive medications, creatinine, alcohol intake, left ventricular ejection fraction, and time-updated ECG metrics (heart rate, QTc, QRS duration, ECG-left ventricular hypertrophy, bundle branch block or interventricular conduction delay)]. This finding suggests that GEH represents an independent electrophysiological substrate of SCD.

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Computer modelling of beat-to-beat repolarization heterogeneity in human cardiac ventricles

Cited by 7 publications

References 23 publications

Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: Results from the SMART-AV Trial

Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: Results from the SMART-AV Trial

Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

Global Electrical Heterogeneity: Mechanisms and Clinical Significance

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