A baby’s heart defect is often spotted on ultrasound, but the scan wrongly suggests a severe narrowing of the aorta in up to 90% of cases. This project uses advanced fetal MRI to distinguish true coarctation of the aorta from a similar-looking but different condition called uteroplacental insufficiency, where the placenta fails to supply enough blood. Both problems can stunt heart development and raise the risk of later cardiovascular disease, but doctors currently struggle to tell them apart before birth. The researchers will combine 3D/4D fetal MRI with a maternal oxygen challenge and machine learning to build a detailed profile of blood flow in the placenta and fetal heart. They will then follow the babies for a year after birth to link the womb environment with early markers of heart health. If successful, this work could slash the false-positive rate for coarctation diagnosis, sparing families unnecessary anxiety and invasive procedures, while also identifying which infants need close monitoring for future cardiovascular problems.
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Neonatal coarcation of the aorta (CoA) accounts for 7% of congenital heart disease (CHD), but with false positive rates from fetal ultrasound of up to 50-90%. There is however significant crossover between the cardiac phenotype observed in suspected CoA and that described in uteroplacental insufficiency (UPI), which may also be difficult to detect. Both conditions have been independently linked with structural placental abnormalities, fetal growth restriction, abnormal vascular function, and an increased risk of cardiovascular events in later life. This project aims to understand the relationship between placental function, development of the left heart and early cardiovascular health, using a unique combination of novel 3D/4D fetal MRI methods to generate a comprehensive, non-invasive profile of the fetoplacental circulation in a spectrum of left-sided CHD (CoA and hypoplastic left heart syndrome). Casual inference methods will be used characterise the baseline fetoplacental conditions associated with underdevelopment of the left heart, including the fetoplacental response to maternal hyperoxygenation. Machine learning methods will then be used to accurately model the severe neonatal CoA phenotype and improve prenatal risk stratification. Finally, postnatal follow-up at one-year of age will provide longitudinal insights into the relationship between intrauterine environment and early markers of cardiovascular health.
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