The Present and Future
JACC State-of-the-Art Review
Lifespan Perspective on Congenital Heart Disease Research: JACC State-of-the-Art Review

https://doi.org/10.1016/j.jacc.2021.03.012Get rights and content
Under an Elsevier user license
open archive

Highlights

  • Advances in the care of patients with CHD have resulted in an older patient population with complex cardiovascular conditions, presenting challenges for health care delivery.

  • This requires a paradigm shift that considers long-term epidemiological trajectories and the interaction of congenital and acquired cardiovascular disorders.

  • The evolving framework must integrate genetic, phenotypical, and environmental factors that influence the natural history and clinical course of patients with CHD.

  • Innovative platforms for evaluation and management of patients with CHD at all stages of life should embrace concepts emerging from research, technology, and personalized medicine.

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.

Key Words

artificial intelligence
congenital heart disease
disease trajectories
lifespan
precision medicine
research

Abbreviations and Acronyms

3D
3-dimensional
ACHD
adult congenital heart disease
AI
artificial intelligence
CHD
congenital heart disease
CMR
cardiovascular magnetic resonance
DL
deep learning
EHRs
electronic health records
ICHOM
International Consortium for Health Outcome Measurement
ML
machine learning
RCTs
randomized, controlled clinical trials
RNNs
recurrent neural networks

Cited by (0)

Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.

The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.

Drs. Diller and Arvanitaki share first authorship.