Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse

项目总结 二尖瓣脱垂(MVP)是一种常见的瓣膜病，全世界约有1.7亿人。每年，0.4- 1.9%的MVP患者会出现心脏骤停(SCA)或心源性猝死(SCD)，7% 年轻人的SCD是由MVP引起的。然而，对这一毁灭性结果的预测并不容易。 MVP的一级预防植入型心律转复除颤器(ICD)尚无适应症。 重度二尖瓣返流仅能解释50%的SCA病例的MVP。SCD/SCA风险还与 伴有轻度MR的双叶表型、二尖瓣环分离(MAD)和左室局灶性纤维化 磁共振(CMR)-晚期Gd增强(LGE)图像。这样的成像参数(包括 LGE)尚未进行前瞻性评估。此外，它们在SCA幸存者中并不总是存在，而且 弥漫性纤维化已经被我们的小组和其他人提出作为一种替代的心律失常底物，基于 CMR/T1标测、应变超声心动图和尸检数据。总体而言，要准确地确定唯一的 影像表型，不确定哪些MVP患者应该接受CMR。不管 心律失常表型，复杂性室性异位(COVE-定义为频繁的多形性室性心动过速，双相 或非持续性室性心动过速)在80-100%的MVP病例中在SCA或SCD之前被检测到。ComVE， 在CMR上通常与左心室纤维化有关，与更高的全原因死亡率和SCA发生率有关 (20%与12%，如果没有ComVE，p&lt；0.05)基于初步横断面数据。我们的中心假设是 合并VE的MVP患者，由于LGE或异常T1映射的发生率较高， 代表理想的CMR候选对象，无论是否涉及传单或MAD，并且可以通过 大型超声心动图数据库中的自动化“监视”工具。此外，我们假设纤维化 是史无前例的多中心纵向评估临床的SCD/SCA的最强预测者 MVP发生心律失常风险的CMR参数。具体地说，我们的目标是1)评估CMR作为筛查的作用 在未选择的MVP样本中，除LGE外，使用ComVE合并T1标测的MVP纤维化工具； 2)开发了一种基于回声的机器学习算法，利用ComVE检测MVP，并测试其与 心肌纤维化对CMR和SCD/SCA纵向风险的影响；以及3)建立一个新的前瞻性SCD/SCA风险 MVP中的预测模型。更好地选择CMR候选者并开发SCD/SCA风险预测 CMR包含纤维化工具有望显著改善MVP的风险分层，并建立 初级预防ICD试验的未来标准。

PROJECT SUMMARY Mitral valve prolapse (MVP) is a common valvulopathy affecting over 170 million worldwide. Every year, 0.4- 1.9% of individuals with MVP will develop sudden cardiac arrest (SCA) or sudden cardiac death (SCD), and 7% of SCDs in the young are caused by MVP. However, predictors of this devastating outcome are not readily available, and indications for a primary prevention implantable cardioverter defibrillator (ICD) in MVP are lacking. Severe mitral regurgitation explains only 50% of SCA cases in MVP. SCD/SCA risk has also been linked to a bileaflet phenotype with mild MR, mitral annular disjunction (MAD), and left ventricular focal fibrosis on cardiac magnetic resonance (CMR)-late gadolinium enhancement (LGE) images. Such imaging parameters (including LGE) have not been evaluated prospectively. Moreover, they are not consistently found in SCA survivors, and diffuse fibrosis has been proposed as an alternative arrhythmic substrate by our group and others based on CMR/T1 mapping, strain echocardiography, and post-mortem data. Overall, it is challenging to pinpoint a unique imaging phenotype, and uncertainty exists about which MVP patients should undergo CMR. Regardless of arrhythmic phenotype, complex ventricular ectopy (ComVE - defined as frequent polymorphic PVCs, bigeminy or non-sustained ventricular tachycardia) is detected in 80-100% of MVP cases prior to SCA or SCD. ComVE, commonly associated with left ventricular fibrosis on CMR, is linked to higher all-cause mortality and SCA rates (20% versus 12% if no ComVE, p < 0.05) based on preliminary cross-sectional data. Our central hypothesis is that MVP patients with ComVE, because of the higher prevalence of either LGE or abnormal T1 mapping, represent ideal CMR candidates regardless of leaflet involvement or MAD, and can be rapidly identified by an automated “surveillance” tool within a large echocardiographic database. Moreover, we hypothesize that fibrosis is the strongest predictor of SCD/SCA in an unprecedented, multi-center effort to longitudinally assess clinical and CMR parameters of arrhythmic risk in MVP. Specifically, we aim to 1) Assess the role of CMR as a screening tool for fibrosis in MVP with ComVE incorporating T1 mapping in addition to LGE in an unselected MVP sample; 2) Develop an echo-based machine-learning algorithm to detect MVP with ComVE, test its association with myocardial fibrosis on CMR and longitudinal SCD/SCA risk; and 3) Build a novel prospective SCD/SCA risk prediction model in MVP. Better selection of CMR candidates and development of a SCD/SCA risk prediction tool inclusive of fibrosis by CMR are expected to dramatically improve risk stratification in MVP and establish future criteria for primary prevention ICD trials.