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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $759,534

## Abstract

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.

## Key facts

- **NIH application ID:** 10814950
- **Project number:** 5R01HL153447-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Francesca N Delling
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $759,534
- **Award type:** 5
- **Project period:** 2020-05-26 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10814950

## Citation

> US National Institutes of Health, RePORTER application 10814950, Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse (5R01HL153447-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10814950. Licensed CC0.

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