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

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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.