Xie Eric, Sung Eric, Saad Elie, Trayanova Natalia, Wu Katherine C, Chrispin Jonathan
Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
Front Cardiovasc Med. 2022 Aug 22;9:884767. doi: 10.3389/fcvm.2022.884767. eCollection 2022.
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
心脏性猝死（SCD）是主要的死亡原因之一，约占所有心血管疾病死亡人数的一半。在美国，大多数SCD（85%）发生在缺血性心肌病（ICM）患者中，一小部分发生在非缺血性心肌病（NICM）患者中，后者往往更年轻，其死亡风险比缺血性心肌病患者更不明确。SCD风险分层的传统方法是测定射血分数（EF），通常采用超声心动图，目前这是确定植入式心脏除颤器（ICD）形式的一级预防候选资格的一种方法。先进的心脏成像方法，如心脏磁共振成像（CMR）、单光子发射计算机断层扫描（SPECT）、正电子发射断层扫描（PET）和计算机断层扫描（CT），已成为有前景的非侵入性风险分层方法，可通过对易导致SCD的潜在心肌基质进行表征来实现。CMR上的延迟钆增强（LGE）可检测心肌瘢痕，这可为ICD决策提供依据。在风险分层中，已对总体瘢痕负荷、区域特异性瘢痕负荷和瘢痕异质性进行了研究。PET和SPECT是确定心肌活力、神经支配以及炎症的核医学方法。CT可用于评估心肌脂肪及其与折返环路的关联。新兴方法包括利用从CMR衍生的复杂电生理模型开发“虚拟心脏”，以尝试预测心律失常易感性。最近的进展将新颖的机器学习（ML）算法与成熟的成像技术相结合，以提高预测性能。使用先进成像技术增强SCD风险分层的做法越来越成熟，可能很快在临床决策中发挥更大作用。ML可通过推进变量发现和数据分析进一步推动这一范式转变。
