Key result
Precision health and multi-omic data integration offer a lifespan framework to advance congenital heart disease research.
Highlights the need for a lifespan perspective and precision health approach in congenital heart disease research to address the growing adult CHD population.
More than 90% of patients with congenital heart disease (CHD) are nowadays surviving to adulthood and adults account for over two-thirds of the contemporary CHD population in Western countries. Although outcomes are improved, surgery does not cure CHD. Decades of longitudinal observational data are currently motivating a paradigm shift toward a lifespan perspective and proactive approach to CHD care. The aim of this review is to operationalize these emerging concepts by presenting new constructs in CHD research. These concepts include long-term trajectories and a life course epidemiology framework. Focusing on a precision health, we propose to integrate our current knowledge on the genome, phenome, and environome across the CHD lifespan. We also summarize the potential of technology, especially machine learning, to facilitate longitudinal research by embracing big data and multicenter lifelong data collection.
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Diller et al. (2021) conducted a review in Congenital heart disease. Lifespan perspective and precision health approach was evaluated. A lifespan perspective and precision health approach, integrating genome, phenome, and environome data alongside machine learning, are proposed to advance congenital heart disease research.
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