Modeling Mortality in Duchenne Muscular Dystrophy Cardiomyopathy: Identification of Surrogate Outcome Measures for DMD Drug Trials

Overview

Dystrophin associated heart dysfunction is a leading cause of death in patients with Duchenne and Becker Muscular dystrophy (DMD/BMD) and Duchenne and Becker muscular dystrophy carriers (MDC); however, the evolution of heart dysfunction is not well-understood. The central objectives of this proposal are to elucidate this evolution of heart dysfunction and identify measures from cardiac MRI images that can predict death or significant heart disease in patients with DMD/BMD/MDC. This study will create a large clinical and cardiac MRI registry of dystrophin associated heart dysfunction, will utilize advanced image analysis techniques, including deep learning neural networks, to comprehensively evaluate every patient, and will create a risk toolkit accessible to clinicians around the world; this proposal has the potential to improve the quality of life in patients with dystrophin associated heart dysfunction by allowing for earlier and more intensive therapy in patients with severe disease and by identifying surrogate outcome measures for use in therapeutic trials.

Duchenne and Becker muscular dystrophy (DMD/BMD) are devastating diseases with no cure resulting in loss of ambulation, respiratory failure, cardiomyopathy, and premature death. Dystrophin associated cardiomyopathy (defined here as CM) is the leading cause of death in DMD/BMD, and an under-studied concern in DMD and BMD mutation carriers (MDC). CM progression is variable and poorly described in the current era. There are no blood or imaging biomarkers that can predict the pace of progression or the risk of early mortality. More importantly, there are no established cardiac outcome measures. Novel, targeted therapeutics are necessary to treat CM, but these significant knowledge gaps make clinical trials challenging. A better understanding of DMD/BMD/MDC cardiovascular disease progression and the identification of surrogate outcome measures are critical for the field to advance. To address these obstacles, the investigators propose to leverage the Duchenne muscular dystrophy cardiac care consortium (DMDCCC). Created with grants from the NHLBI and the FDA, this consortium consists of twelve high-volume sites with similar DMD/BMD/MDC cardiovascular treatment and diagnostic protocols, including surveillance CMR imaging every 1-2 years. This proposal will create a comprehensive prospective registry of DMD/BMD/MDC patients with meticulously collected clinical data and cardiac magnetic resonance (CMR) images; we anticipate enrollment of 950 patients with over 4000 CMR studies. This cohort will be used to better define the progression of CM and to determine associations with mortality. The central hypothesis of this proposal is that integrated statistical modeling based on advanced imaging can improve prediction of CM progression and mortality. Aim 1 will create a comprehensive cohort of DMD/BMD/MDC patients and model the progression of CM. Aim 2 will determine cardiovascular measures that are associated with CM mortality or rapid progression using novel, data-driven, personalized machine learning models. Aim 3 will create a portal for DMD/BMD/MDC centers to determine patient risk. This multi-PI proposal leverages expertise in clinical care, cardiac imaging, biomedical engineering, complex image analysis, and neural networks. This study will create the largest cohort of DMD/BMD/MDC patients with CMR images, allowing for a better understanding of CM progression and identifying biomarkers that associate with poor outcomes. The resulting risk portal will provide clinicians all over the world with a method to assess their patient's risk in real time, allowing intensification of therapy for those deemed high risk. By building on prior productive collaborations, particularly that of the DMDCCC, this proposal will expand our understanding of CM, improving clinical care and future cardiac-specific therapeutic trials.