Global Electrocardiographic Measures and Cardiac Structure and Function: The Atherosclerosis Risk in Communities (ARIC) Study

Abstract

Background: Electrical excitation initiates myocardial mechanical contraction and coordinates myocardial pumping. We hypothesized that ECG global electrical heterogeneity (GEH) and its longitudinal changes are associated with cardiac structure and function.

Methods and Results: Participants from the Atherosclerosis Risk in Communities (ARIC) study (N=5,114; 58% female; 22% African Americans) with resting 12-lead ECGs (visits 1–5) and echocardiographic assessment of left ventricular ejection fraction (LVEF), LV global longitudinal strain, LV mass index (LVMi), LV end-diastolic (LVEDVi), and end-systolic volume index (LVESVi) at visit 5 were included. Longitudinal analysis included ARIC participants (N=14,609) with measured GEH at visits 1–4. GEH was quantified by spatial ventricular gradient, QRS-T angle, and sum absolute QRST integral (SAI QRST). Cross-sectional and longitudinal regressions were adjusted for manifest and subclinical cardiovascular disease (CVD). Having 4 abnormal GEH parameters was associated with a 6.4%(95% CI 5.5–7.3%) LVEF decline, a 24.2 (95%CI 21.5–26.9) g/m2 increase in LVMi, a 10.3 (95%CI 8.9–11.7) mL/m2 increase in LVEDVi, and a 7.8 (95%CI 6.9–8.6) mL/m2 increase in LVESVi. Altogether, clinical and ECG parameters accounted for approximately 1/3rd of LV volume and 20% of systolic function variability. The associations were significantly stronger in CVD. SAI QRST increased by 20 mV*ms for each 3-year period in participants who demonstrated LV dilatation at visit 5. Sudden cardiac death victims demonstrated rapid GEH worsening, while those with LV dysfunction demonstrated slow GEH worsening. Healthy aging was associated with a distinct pattern of SVG azimuth decrement.

Conclusion: GEH is a marker of subclinical abnormalities in cardiac structure and function.

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.