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: 10171903
• 负责人: Francesca N Delling
• 金额: $ 77.98万
• 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/10171903
• 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.

项目总结