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

Abstract

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.

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

“…First, each beat on a 10-second 12-lead ECG recording was manually labeled by the team of physicians, as recently described. [14] Non-analyzable beats due to noise and artifacts, and non-dominant ectopic beats were excluded from analysis. Only normal sinus beats in a 10-second recording were included in the construction of a normal sinus median beat.…”

“…Equality of the areas under the (receiver operating characteristic, ROC) curve (AUC) for studied ECG and VCG predictors of SCD was compared using a Wald test of the null hypothesis that all classifier AUC values are equal. [22] In addition, as we recently showed that the best predictive accuracy of ECG biomarkers is within 2 years after ECG recording,[14] time-dependent (2-year) ROC AUC analysis was performed, using survival analysis framework[23, 24].…”

Using ECG Machine Learning for Detection of Cardiovascular Disease in African American Men and Women: the Jackson Heart Study

“…23 Both area and peak QRS-T angles were measured. 14,23,24 Quality control of automated ECG analysis was performed by investigators included median beat measurements (PR, QRS, QT intervals, and frontal P, QRS, and T axes), as well as durations, amplitudes, and areas of all identified by the algorithm waves and segments on all 12 leads. We used the results of automated 12-lead ECG measurements as reported by the 12SL algorithm, without further quality control procedures.…”

“…Global electrical heterogeneity (GEH) 13 is a novel vectorcardiographic (VCG) phenotype providing additional predictive value beyond traditional ECG metrics. 14…”

Competing Risks in Patients with Primary Prevention Implantable Cardioverters Defibrillators: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study

“…3 It integrates static and dynamic alternations in ventricular repolarization and conduction, which have been linked to ventricular arrhythmias in studies from the bench to the bedside. 6 Abnormal GEH is associated with SCD in the general population 3, 7 where it is explicitly associated with SCD over non-arrhythmic modes of death 3 , and an increased risk of drug-induced torsades-de-pointes. 8 The utility of assessing GEH for SCD risk stratification in CM patients with reduced LVEF who meet primary prevention ICD indications, however, is unknown.…”