A child diagnosed with dilated cardiomyopathy today has no reliable way of knowing whether their heart will fail in two years or twenty. In adults, doctors can use genetic and clinical data to tailor treatments and predict outcomes, but for children the underlying cause is often unknown, the disease course is unpredictable, and there are no tools to guide therapy. This fellowship will build the world’s largest research cohort of children with dilated cardiomyopathy, combining clinical data, biomarkers, and proteomics to answer three questions: can we predict long-term outcomes, can we forecast disease progression and response to common drugs, and can biomarkers improve diagnosis? If successful, the work will produce the first paediatric risk-prediction tools for this condition, allowing clinicians to personalise care—deciding who needs aggressive treatment early and who can be managed more conservatively. That would shift paediatric cardiology from reactive crisis management to proactive, evidence-based care, reducing the number of children who progress to heart failure or need a transplant.
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Dilated cardiomyopathy (DCM) is the most common paediatric heart muscle disease and is associated with malignant arrhythmias, cardiac transplantation, and heart failure death. In adults, an understanding of the interplay between cause of disease, phenotype and outcomes has allowed patients to benefit from disease specific management and risk prediction tools leading to personalised care. In contrast, in children the underlying cause is often undetermined, the natural history is poorly understood, and we have no way of predicting response to therapies or outcomes. Together, these evidence gaps prevent personalised management and prognostication. During this fellowship, I will use international collaborations developed during my BHF clinical research training fellowship (CRTF), to develop the largest research cohort of children with DCM in the world and answer 3 questions; (1) Can we predict long-term outcomes and provide personalised risk estimates? (2) Can we predict disease progression and response to common therapies? (3) Can we use biomarkers to help diagnose and predict progression? Building on my substantial track record in risk modelling, I will take advantage of world leading expertise in data science and proteomic bioinformatics at UCL to develop the first paediatric tools for DCM allowing evidence-based personalisation of care for the first time.
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