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

Abstract

Aim: Our goal was to investigate the effect of a global XYZ median beat construction and the heart vector origin point definition on predictive accuracy of ECG biomarkers of sudden cardiac death (SCD).

Methods: Atherosclerosis Risk In Community study participants with analyzable digital ECGs were included (n=15,768; 55% female, 73% white, mean age 54.2±5.8y). We developed an algorithm to automatically detect the heart vector origin point on a median beat. Three different approaches to construct a global XYZ beat and two methods to locate origin point were compared. Global electrical heterogeneity was measured by sum absolute QRST integral (SAI QRST), spatial QRS-T angle, and spatial ventricular gradient (SVG) magnitude, azimuth, and elevation. Adjudicated SCD served as the primary outcome.

Results: There was high intra-observer (kappa 0.972) and inter-observer (kappa 0.984) agreement in a heart vector origin definition between an automated algorithm and a human. QRS was wider in a median beat that was constructed using R-peak alignment than in time-coherent beat (88.1±16.7 vs. 83.7±15.9ms; P<0.0001), and on a median beat constructed using QRS-onset as a zeroed baseline, vs. isoelectric origin point (86.7±15.9 vs. 83.7±15.9ms; P<0.0001). ROC AUC was significantly larger for QRS, QT, peak QRS-T angle, SVG elevation, and SAI QRST if measured on a time-coherent median beat, and for SAI QRST and SVG magnitude if measured on a median beat using isoelectric origin point.

Conclusion: Time-coherent global XYZ median beat with physiologically meaningful definition of the heart vector’s origin point improved predictive accuracy of SCD biomarkers.

Jason A. Thomas

PhD
Medical Data & AI Scientist | Strategist | Informatician | Tech lead - Senior Data & AI Scientist - Philips

My research interests include 1) building foundational layers (data, infrastructure, knowledge representation, talent, culture) to support biomedical data science and 2) applying data science & AI methods on data to drive business value and improve patient outcomes.