TY - JOUR
T1 - Lifespan Perspective on Congenital Heart Disease Research
T2 - JACC State-of-the-Art Review
AU - Diller, Gerhard Paul
AU - Arvanitaki, Alexandra
AU - Opotowsky, Alexander R.
AU - Jenkins, Kathy
AU - Moons, Philip
AU - Kempny, Alexander
AU - Tandon, Animesh
AU - Redington, Andrew
AU - Khairy, Paul
AU - Mital, Seema
AU - Gatzoulis, Michael
AU - Li, Yue
AU - Marelli, Ariane
N1 - Publisher Copyright:
© 2021
PY - 2021/5/4
Y1 - 2021/5/4
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - congenital heart disease
KW - disease trajectories
KW - lifespan
KW - precision medicine
KW - research
UR - http://www.scopus.com/inward/record.url?scp=85104461635&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104461635&partnerID=8YFLogxK
U2 - 10.1016/j.jacc.2021.03.012
DO - 10.1016/j.jacc.2021.03.012
M3 - Review article
C2 - 33926659
AN - SCOPUS:85104461635
SN - 0735-1097
VL - 77
SP - 2219
EP - 2235
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
IS - 17
ER -