Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease
This temporally validated model shows promise to inexpensively risk-stratify individuals with CHD across the lifespan, which may inform the timing of imaging/interventions and facilitate improved access to care.
Authors
- William La Cava (ORCID: https://orcid.org/0000-0002-1332-2960)
- Tal Geva (ORCID: https://orcid.org/0000-0002-3538-1865)
- John K. Triedman (ORCID: https://orcid.org/0000-0002-7456-9596)
- Anne Marie Valente (ORCID: https://orcid.org/0000-0002-8357-694X)
- Joshua Mayourian (ORCID: https://orcid.org/0000-0002-2364-1260)
- Sunil J. Ghelani (ORCID: https://orcid.org/0000-0002-4227-8823)
- Amr El‐Bokl (ORCID: https://orcid.org/0000-0001-8049-2233)
- Platon Lukyanenko
Institutions
- Boston Children's Hospital (US)
- Harvard University (US)
Publication Details
- Journal
- European Heart Journal
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1093/eurheartj/ehae651
- Citations
- 23
- Primary Topic
- Congenital Heart Disease Studies
- Type
- article
- Field-Weighted Citation Impact
- 8.89
Funders
- Thrasher Research Fund
- Boston Children's Hospital
- National Institutes of Health
- U.S. National Library of Medicine