Al-Zaiti Salah, Macleod Robert, Dam Peter Van, Smith Stephen W, Birnbaum Yochai
Department of Acute & Tertiary Care, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Biomedical Engineering, University of Utah, Salt Lake, UT, USA.
J Electrocardiol. 2022 Sep-Oct;74:65-72. doi: 10.1016/j.jelectrocard.2022.08.003. Epub 2022 Aug 18.
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.
尽管12导联心电图是对有症状冠状动脉疾病患者进行初始无创评估的主要手段，但它仍然是检测心肌缺血的次优诊断工具，其敏感性和特异性评分仅处于可接受水平。虽然心肌缺血会影响QRS波群和ST-T波形的形态，但当前指南主要关注ST段幅度，这是一个错失的机会，可能解释了心电图诊断性能欠佳的原因。这种潜在机会以及心电图的低成本和易用性，为提高心电图对缺血检测的诊断准确性提供了有力动机。本文描述了众多经证实能显著提高心电图对缺血检测敏感性的计算心电图方法和途径。简而言之，这些新兴方法在概念上可分为以下四种方法之一：（1）利用除经典ST-T幅度测量之外的、指示缺血损伤的新型心电图波形特征和信号；（2）应用基于体表电位标测（BSPM）的方法来扩大体表心电图对缺血检测的空间覆盖范围；（3）开发反向心电图解法以重建激活和恢复途径的解剖模型，从而检测和定位损伤电流；（4）探索基于人工智能（AI）的技术来获取缺血的心电图波形信号。我们介绍了这些新兴心电图方法各自的最新进展、缺点和未来机遇。未来研究应聚焦于这些方法的前瞻性临床试验，以确立其临床实用性，并加速其向临床实践的潜在转化。
