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

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.

Genome-Wide Associations of Global Electrical Heterogeneity ECG Phenotype: The ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study)

BACKGROUND

ECG global electrical heterogeneity (GEH) is associated with sudden cardiac death. We hypothesized that a genome-wide association study would identify genetic loci related to GEH.

METHODS AND RESULTS

We tested genotyped and imputed variants in black (N=3057) and white (N=10 769) participants in the ARIC (Atherosclerosis Risk in Communities) study and CHS (Cardiovascular Health Study). GEH (QRS-T angle, sum absolute QRST integral, spatial ventricular gradient magnitude, elevation, azimuth) was measured on 12-lead ECGs. Linear regression models were constructed with each GEH variable as an outcome, adjusted for age, sex, height, body mass index, study site, and principal components to account for ancestry. GWAS identified 10 loci that showed genome-wide significant association with GEH in whites or joint ancestry. The strongest signal (rs7301677, near TBX3) was associated with QRS-T angle (white standardized β+0.16 [95% CI 0.13-0.19]; P=1.5×10-26), spatial ventricular gradient elevation (+0.11 [0.08-0.14]; P=2.1×10-12), and spatial ventricular gradient magnitude (-0.12 [95% CI -0.15 to -0.09]; P=5.9×10-15). Altogether, GEH-SNPs explained 1.1% to 1.6% of GEH variance. Loci on chromosomes 4 (near HMCN2), 5 (IGF1R), 11 (11p11.2 region cluster), and 7 (near ACTB) are novel ECG phenotype-associated loci. Several loci significantly associated with gene expression in the left ventricle (HMCN2 locus-with HMCN2; IGF1R locus-with IGF1R), and atria (RP11-481J2.2 locus-with expression of a long non-coding RNA and NDRG4).

CONCLUSIONS

We identified 10 genetic loci associated with ECG GEH. Replication of GEH GWAS findings in independent cohorts is warranted. Further studies of GEH-loci may uncover mechanisms of arrhythmogenic remodeling in response to cardiovascular risk factors.

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

BACKGROUND

Cardiac resynchronization therapy (CRT) reduces mortality and morbidity in selected heart failure patients. However, not all patients respond to CRT.

OBJECTIVE

We hypothesized that a novel measure of electrical dyssynchrony, sum absolute QRST integral (SAI QRST), predicts CRT response independent of QRS duration and morphology.

METHODS

We retrospectively analyzed baseline 12-lead electrocardiograms of SmartDelay Determined AV Optimization: A comparison to other AV delay methods used in cardiac resynchronization therapy (SMART-AV) trial study participants (N = 234; mean age 67 years; 163 (70%) men; 140 (60%) ischemic cardiomyopathy; mean left ventricular ejection fraction 25%; mean QRS duration 152 ms; 179 (77%) had left bundle branch block). Baseline pre-implant electrocardiograms were digitized, transformed into orthogonal XYZ, and analyzed automatically by customized MATLAB software. SAI QRST was measured as an averaged arithmetic sum of absolute areas under the QRST curve. Patients were followed prospectively 6 months after CRT-defibrillator implantation. Patients with a decrease in left ventricular end-systolic volume ≥15 mL after 6 months of CRT were considered responders. The logistic regression model was adjusted for age, sex, bundle branch block morphology, left ventricular ejection fraction, cardiomyopathy type, and QRS duration.

RESULTS

Patients with the high mean SAI QRST (third tertile) had 2.5 times greater odds of response than those with the low mean SAI QRST (first tertile: odds ratio [OR] 2.5; 95% confidence interval [CI] 1.3-5.0; P = .010) and 1.9 times greater than the lower 2 tertiles combined (OR 1.9; 95% CI 1.1-3.5; P = .03). Adjustment for renal function (OR 2.33; 95% CI 1.32-4.11; P = .003) and left ventricular lead position in right anterior oblique and left anterior oblique views (OR 1.7; 95% CI 0.9-3.2; P = .087) did not attenuate association of SAI QRST with outcome.

CONCLUSION

High SAI QRST independently predicts CRT response in the SMART-AV study.