Spectrum of clinical applications of interlead ECG heterogeneity assessment: From myocardial ischemia detection to sudden cardiac death risk stratification

Abstract

Heterogeneity in depolarization and repolarization among regions of cardiac cells has long been recognized as a major factor in cardiac arrhythmogenesis. This fundamental principle has motivated development of noninvasive techniques for quantification of heterogeneity using the surface electrocardiogram (ECG). The initial approaches focused on interval analysis such as interlead QT dispersion and Tpeak–Tend difference. However, because of inherent difficulties in measuring the termination point of the T wave and commonly encountered irregularities in the apex of the T wave, additional techniques have been pursued. The newer methods incorporate assessment of the entire morphology of the T wave and in some cases of the R wave as well. This goal has been accomplished using a number of promising vectorial approaches with the resting 12‐lead ECG. An important limitation of vectorcardiographic analyses is that they require exquisite stability of the recordings and are not inherently suitable for use in exercise tolerance testing (ETT) and/or ambulatory ECG monitoring for provocative stress testing or evaluation of the influence of daily activities on cardiac electrical instability. The objectives of the present review are to describe a technique that has been under clinical evaluation for nearly a decade, termed “interlead ECG heterogeneity.” Preclinical testing data will be briefly reviewed. We will discuss the main clinical findings with regard to sudden cardiac death risk stratification, heart failure evaluation, and myocardial ischemia detection using standard recording platforms including resting 12‐lead ECG, ambulatory ECG monitoring, ETT, and pharmacologic stress testing in conjunction with single‐photon emission computed tomography myocardial perfusion imaging.

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

“…ECG features have been used to diagnose or predict cardiovascular events. For instance, heterogeneity of R-wave and T-wave morphology and ST-segment elevation have been used to diagnose ventricular arrhythmias ( 25 , 26 ). ST-segment elevation myocardial infarction (STEMI) has also been used for chronic HF prediction ( 27 ).…”

Continuous multi‐day tracking of post‐myocardial infarction recovery of cardiac electrical stability and autonomic tone using electrocardiogram patch monitors

“…High levels of TWA also predicted the occurrence of nonsustained ventricular tachycardia during PCI in post‐MI patients (Takasugi et al, 2011; Verrier et al, 2013). In the Finnish Health Survey 2000, TWH analyzed from resting 12‐lead ECGs identified subjects who died suddenly with 2.8 (1.8‐4.5) hazard ratios ( p < .001) (Kenttä et al, 2016; Verrier & Huikuri, 2017; Verrier et al, 2021). The results of the ATRAMI study also demonstrated the predictive capacity of altered autonomic tone (HRV) and baroreceptor reflex sensitivity (BRS) in post‐MI patients (La Rovere et al, 1998).…”

“…This analytical approach was used to calculate TWH from 12‐lead ECG recordings at rest, as described in detail (Nearing & Verrier, 2003; Verrier et al, 2021; Verrier & Huikuri, 2017). Basically, the approach superimposes simultaneous waveforms from three precordial leads, typically V 4 , V 5 , and V 6 , and calculates the variance or splay about the mean waveform.…”

“…T‐wave heterogeneity (TWH), a closely related electrophysiologic phenomenon and SCD predictor that can be measured on a 12‐lead ECG, was also determined. TWA (Verrier et al, 2011; Verrier & Ikeda, 2013) and TWH (Kenttä et al, 2016; Verrier & Huikuri, 2017; Verrier et al, 2021) have both been proven useful in predicting SCD, with significant hazard ratios of 4.2–22.6 and 2.8 (95% CI: 1.8–4.5), respectively. Since autonomic factors have been shown to play a major role in triggering arrhythmias post‐MI (Huikuri et al, 2001; La Rovere et al, 1998; Verrier & Antzelevitch, 2004), we also measured heart rate variability (HRV) using square root of the mean of the sum of squares of differences between adjacent normal RR intervals (rMSSD).…”

Biomechanical Sensing Systems for Cardiac Activity Monitoring

“…Electrocardiograms (ECG), Doppler ultrasounds, arteriography, computed tomography (CT), and other imaging techniques, among others, are commonly used in clinical settings to evaluate cardiovascular disease (CVD), all of which are able to identify the disease accurately [7][8][9]. In addition, patients who have arrhythmia and myocardial ischemia are required to have real-time electrocardiogram monitoring [10]. Conventional cardiovascular monitoring, on the other hand, frequently has its limitations due to the paucity of medical resources, which can be inconvenient, as well as the utilization of cumbersome and pricey technology for episodic diagnosis in hospital settings.…”