Validation of the vessel-specific leads (VSLs) for detection of acute ischemia on a dataset with non-ischemic ST-segment deviation

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

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.

Diagnostic Accuracy Of The Electrocardiographic Decision Support – Myocardial Ischaemia (EDS-MI) Algorithm In Detection Of Acute Coronary Occlusion

Electrocardiographic Decision Support – Myocardial Ischaemia (EDS-MI) is a graphical decision support for detection and localization of acute transmural ischaemia. A recent study indicated that EDS-MI performs well for detection of acute transmural ischaemia. However, its performance has not been tested in patients with non-ischaemic ST-deviation. We aimed to optimize the diagnostic accuracy of EDS-MI in patients with verified acute coronary occlusion as well as patients with non-ischaemic ST deviation and compare its performance with STEMI criteria. We studied 135 patients with non-ischaemic ST deviation (perimyocarditis, left ventricular hypertrophy, takotsubo cardiomyopathy and early repolarization) and 117 patients with acute coronary occlusion. In 63 ischaemic patients, the extent and location of the ischaemic area (myocardium at risk) was assessed by both cardiovascular magnetic resonance imaging and EDS-MI. Sensitivity and specificity of ST elevation myocardial infarction criteria were 85% (95% confidence interval (CI) 77, 90) and 44% (95% CI 36, 53) respectively. Using EDS-MI, sensitivity and specificity increased to 92% (95% CI 85, 95) and 81% (95% CI 74, 87) respectively (p=0.035 and p<0.001). Agreement was strong (83%) between cardiovascular magnetic resonance imaging and EDS-MI in localization of ischaemia. Mean myocardium at risk was 32% (± 10) by cardiovascular magnetic resonance imaging and 33% (± 11) by EDS-MI when the estimated infarcted area according to Selvester QRS scoring was included in myocardium at risk estimation. In conclusion, EDS-MI increases diagnostic accuracy and may serve as an automatic decision support in the early management of patients with suspected acute coronary syndrome. The added clinical benefit in a non-selected clinical chest pain population needs to be assessed.