Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities

Abstract

Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.

Keywords: Acute coronary syndrome; ECG; Machine learning; Myocardial ischemia; Novel markers.

Fig. 1.. Classification systems across the spectrum of coronary artery disease.

This figure shows the spectrum of coronary artery disease (CAD) as a function of severity and extent of atherosclerosis plaque progression, ranging from patent coronary artery (far left) to total coronary occlusion (far right). Among patients who develop symptomatic CAD, including those evaluated for chest pain or angina-like symptoms, a subset is diagnosed with acute coronary syndrome (ACS). This group is subclassified, based on biomarker-evidence of myocardial necrosis, as either acute myocardial infarction (MI) or unstable angina (UA). Those with acute MI can be further subclassified, based on the presence of ST elevation on the ECG, as either ST elevation myocardial infarction (STEMI) or without ST elevation (NSTEMI). The STEMI and NSTEMI patients overlap in terms of presence or absence of total occlusion (depicted as triangles across the continuum in the figure). Alternatively, the same group with acute MI can be subclassified, based on angiographic TIMI flow criteria, as either occlusion (OMI) or non-occlusion (non-OMI) myocardial infarction. Unlike STEMI, OMI classification better aligns with focal angiographic findings since this group exclusive contains patients with total coronary occlusion. Color gradient indicates the severity of disease. This Figure was created with http://BioRender.com.

Fig. 2.. Limitations of ST amplitude on surface ECG as a sole marker of myocardial ischemia.

(A) cardiac model of anterior wall epicardial ischemia with corresponding ST elevation on V3 to V5 of the 12-lead ECG. (B) cardiac model of anterolateral and inferior-apical epicardial ischemia with corresponding attenuation of ST changes on the 12-lead ECG. This figure was generated using ECGSIM (http://www.ecgsim.org) [23].

Fig. 3.. Selected example of a 12-lead ECG with VSEL display.

This 12-lead ECG was obtained on a 74-year-old male evaluated at the emergency department for chest pain of 2 h duration. There is subtle STE in lateral leads and reciprocal changes in inferior leads with borderline ST depression and T wave inversion in anterior leads, collectively indicating a pattern associated with severe infarct. However, the automated computer interpretation failed in capturing these patterns and indicated no ischemia or infarct. The derived VSEL shows clear STEMI of RCA with abnormal ischemic patterns in LAD and LCX. The patient experienced cardiac arrest at the emergency department, and after successful resuscitation he was referred for urgent angiography that revealed 90% RCA occlusion, 60% LCX occlusion, and 50% LAD occlusion. RCA: right coronary artery; LAD: left anterior descending; LCX: left circumflex.

Fig. 4.. Selected example of cine-ECG display derived from a standard 12-lead ECG.

This Cine-ECG shows the anatomical location of the average activation sequence from the same 12-lead ECG displayed in Fig. 3. The upper panel compares the average electrical pathway (green line) to normal limits in the general population (orange line). It indicates abnormal deviation toward left lateral and posterior myocardial walls. The middle and lower panels visualize this sequence to the anatomic location (red being outside the normal position boundaries in the middle panels. In the lower panels the colors indicate the time in ms (see bottom color bar). These findings are compatible with the angiographic findings of LCX coronary involvement affecting the posterolateral myocardial wall.