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: 10390482
• 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/10390482
• 关键词: Affect; 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; Lead; Left; Link; Machine Learning; Magnetic Resonance; 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; base; cardiovascular risk factor; coronary fibrosis; cost; extracellular; hemodynamics; high risk; improved; machine learning algorithm; mortality; neural network architecture; novel; 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.

项目概要 二尖瓣脱垂 (MVP) 是一种常见的瓣膜病，每年影响全球超过 1.7 亿人。 1.9% 的 MVP 患者会出现心脏骤停 (SCA) 或心源性猝死 (SCD)，7% 年轻人中的 SCD 是由 MVP 引起的，然而，这种破坏性结果的预测因素并不容易。 MVP 中尚缺乏初级预防植入式心律转复除颤器 (ICD) 的指征。 严重二尖瓣反流解释了 MVP 中只有 50% 的 SCA 病例也与二尖瓣关闭不全有关。 双叶表型伴有轻度 MR、二尖瓣环分离 (MAD) 和左心室局灶性纤维化 磁共振（CMR）-晚期钆增强（LGE）图像此类成像参数（包括）。 LGE）尚未进行前瞻性评估，而且在 SCA 幸存者中也没有一致地发现它们。 我们小组和其他人基于以下观点提出弥漫性纤维化作为替代性心律失常基质 总体而言，确定独特的 CMR/T1 映射、应变超声心动图和尸检数据具有挑战性。 影像表型，并且无论哪种情况，哪些 MVP 患者应该接受 CMR 都存在不确定性。 心律失常表型、复杂心室异位（ComVE - 定义为频繁的多态性室性早搏、二联律 80-100% 的 MVP 病例在 SCA 或 SCD 之前检测到（或非持续性室性心动过速）。 通常与 CMR 上的左心室纤维化相关，并与较高的全因死亡率和 SCA 发生率相关 （如果没有 ComVE，则为 20% 对比 12%，p < 0.05）基于初步横截面数据 我们的中心假设是。 患有 ComVE 的 MVP 患者，由于 LGE 或异常 T1 映射的患病率较高， 理想的 CMR 候选者，无论是否涉及传单或 MAD，都可以通过 大型超声心动图数据库中的自动化“监视”工具。 是史无前例的、多中心纵向评估临床研究中 SCD/SCA 最强的预测因子 MVP 中心律失常风险的 CMR 参数具体而言，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，除了 LGE 之外，ComVE 还结合了 T1 映射，用于 MVP 纤维化工具； 2) 开发基于回声的机器学习算法，用 ComVE 检测 MVP，测试其与 心肌纤维化对 CMR 和纵向 SCD/SCA 风险的影响；3) 建立新的前瞻性 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。 通过 CMR 包含纤维化的工具预计将显着改善 MVP 的风险分层并建立 初级预防 ICD 试验的未来标准。