| Literature DB >> 33926659 |
Gerhard-Paul Diller1, Alexandra Arvanitaki2, Alexander R Opotowsky3, Kathy Jenkins4, Philip Moons5, Alexander Kempny6, Animesh Tandon7, Andrew Redington8, Paul Khairy9, Seema Mital10, Michael Α Gatzoulis6, Yue Li11, Ariane Marelli12.
Abstract
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.Entities:
Keywords: artificial intelligence; congenital heart disease; disease trajectories; lifespan; precision medicine; research
Year: 2021 PMID: 33926659 DOI: 10.1016/j.jacc.2021.03.012
Source DB: PubMed Journal: J Am Coll Cardiol ISSN: 0735-1097 Impact factor: 24.094