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

使用 CMR 和超声心动图机器学习对二尖瓣脱垂进行前瞻性心脏性猝死风险分层

• 批准号: 10600113
• 负责人: Francesca N Delling
• 金额: $ 77.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-26 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10600113
• 关键词: Affect; Arrhythmia; Artificial Intelligence; Autopsy; California; Cardiac; Clinical; Clinical Markers; Complex; Data; Databases; Defibrillators; Development; Diffuse; Echocardiography; Fibrosis; Future; Gadolinium; Goals; Heart Arrest; High Prevalence; Holter Electrocardiography; Hybrids; Image; Image Enhancement; Implantable Defibrillators; Individual; Left; Link; Machine Learning; Magnetic Resonance; Maps; Mission; Mitral Valve Insufficiency; Mitral Valve Prolapse; Myocardial; Outcome; Patients; Phenotype; Population; Prevalence; Primary Prevention; Registries; Retrospective Studies; Risk; Role; Sampling; San Francisco; Screening procedure; Survivors; Testing; Training; Uncertainty; United States National Institutes of Health; Universities; Validation; Ventricular; Ventricular Tachycardia; cardiovascular risk factor; convolutional neural network; coronary fibrosis; cost; extracellular; hemodynamics; high risk; improved; machine learning algorithm; mortality; neural network architecture; novel; predictive tools; prognostic significance; prospective; recurrent neural network; risk prediction; risk prediction model; risk stratification; secondary analysis; stem; sudden cardiac death; tool

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.

项目摘要 二尖瓣脱垂（MVP）是一种常见的瓣膜病，全球影响超过1.7亿。每年，0.4- 1.9％的MVP患者将发展心脏骤停（SCA）或心脏猝死（SCD），7％ 年轻人中的SCD是由MVP引起的。但是，这种毁灭性结果的预测因素不容易 缺乏可用的MVP中预防植入式心脏逆转除颤器（ICD）的指示。 严重的二尖瓣反流仅解释了MVP中SCA病例的50％。 SCD/SCA风险也已与 双叶型表型，具有轻度MR，二尖瓣环形分离（MAD）和心脏左心室局灶性纤维化 磁共振（CMR） - 层gadolinium增强（LGE）图像。这样的成像参数（包括 LGE）尚未预期评估。而且，在SCA表面上并没有始终发现它们，并且 我们的组和其他基于我们的组提出了弥漫性纤维化作为替代性心律失常底物 CMR/T1映射，应变超声心动图和验尸数据。总体而言，确定独特的 对于MVP患者应接受CMR的成像表型和不确定性存在。不管 心律不齐的表型，复杂的心室外缘（comve-定义为频繁的多态性PVC，Bigminy 在SCA或SCD之前，在80-100％的MVP病例中检测到非固定心室心动过速）。漫画， 通常与CMR上的左心室纤维化相关，与较高的全因死亡率和SCA率有关 （如果没有漫画，则为20％和12％，p <0.05）基于初步的横截面数据。我们的中心假设是 由于LGE或异常T1映射的患病率较高，因此具有COMVE的MVP患者， 无论传单参与或疯狂，都代表理想的CMR候选者 在大型超声心动图数据库中自动化的“监视”工具。此外，我们假设纤维化 是SCD/SCA的强大预测指标，以纵向评估临床的空前，多中心 MVP中心律失常风险的CMR参数。具体而言，我们的目的是1）评估CMR作为筛选的作用 除了未选择的MVP样品中的LGE之外，除了LGE之外，还与Comve合并的MVP纤维化工具； 2）开发一种基于回声的机器学习算法来检测Comve的MVP，测试其与 CMR和纵向SCD/SCA风险上的心肌纤维化； 3）建立一个新颖的潜在SCD/SCA风险 MVP中的预测模型。更好地选择CMR候选者和SCD/SCA风险预测的开发 预计CMR纤维化的工具将大大改善MVP的风险分层并建立 初级预防试验的未来标准。