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Peer-reviewedCC BY-NC-ND 4.0NPJ Digital Medicine

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease

  • Joshua Mayourian1
  • Ivor B. Asztalos2
  • William La Cava3
  • Ryan L. Kobayashi1
  • Sunil J. Ghelani1
  • Victoria L. Vetter2
  • Rachel M. Wald4
  • Anne Marie Valente1
  • Tal Geva1
  • John K. Triedman1
4 affiliations
  1. 1Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA USA
  2. 2Division of Pediatric Cardiology, Perelman School of Medicine at the University of Pennsylvania, Children’s Hospital of Philadelphia, Philadelphia, PA USA
  3. 3Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA USA
  4. 4Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, ON Canada
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Abstract

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification strategy in the general adult population. The challenges of lifelong care in congenital heart disease (CHD)—inclusive of regional and socioeconomic disparities worldwide—underscore the need for similar solutions tailored to this population. Herein, we present the first noise-adapted single-lead ECG model for predicting left ventricular systolic dysfunction (left ventricular ejection fraction [LVEF] ≤ 40%) in pediatric and CHD patients. The internal cohort was comprised of 70,226 patients. External test groups included Children’s Hospital of Philadelphia (CHOP; 42,984 patients) and Toronto General Hospital (TGH; 284 repaired tetralogy of Fallot patients). Our model had strong performance across a broad range of CHD lesions, races, ages, and healthcare systems. Our findings support the potential of AI-enabled wearables to expand global access to CHD care. Prospective studies utilizing wearable ECG devices in pediatric and CHD patients are warranted.

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Cite

BibTeXRIS
BibTeX
@article{mayourian2026single,
  title = {{Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease}},
  author = {Joshua Mayourian and Ivor B. Asztalos and William La Cava and Ryan L. Kobayashi and Sunil J. Ghelani and Victoria L. Vetter and Rachel M. Wald and Anne Marie Valente and Tal Geva and John K. Triedman},
  journal = {NPJ Digital Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s41746-026-02646-x},
  url = {https://doi.org/10.1038/s41746-026-02646-x},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease
AU  - Joshua Mayourian
AU  - Ivor B. Asztalos
AU  - William La Cava
AU  - Ryan L. Kobayashi
AU  - Sunil J. Ghelani
AU  - Victoria L. Vetter
AU  - Rachel M. Wald
AU  - Anne Marie Valente
AU  - Tal Geva
AU  - John K. Triedman
PY  - 2026
DA  - 2026/07/11
JO  - NPJ Digital Medicine
DO  - 10.1038/s41746-026-02646-x
UR  - https://doi.org/10.1038/s41746-026-02646-x
AB  - Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification strategy in the general adult population. The challenges of lifelong care in congenital heart disease (CHD)—inclusive of regional and socioeconomic disparities worldwide—underscore the need for similar solutions tailored to this population. Herein, we present the first noise-adapted single-lead ECG model for predicting left ventricular systolic dysfunction (left ventricular ejection fraction [LVEF] ≤ 40%) in pediatric and CHD patients. The internal cohort was comprised of 70,226 patients. External test groups included Children’s Hospital of Philadelphia (CHOP; 42,984 patients) and Toronto General Hospital (TGH; 284 repaired tetralogy of Fallot patients). Our model had strong performance across a broad range of CHD lesions, races, ages, and healthcare systems. Our findings support the potential of AI-enabled wearables to expand global access to CHD care. Prospective studies utilizing wearable ECG devices in pediatric and CHD patients are warranted.
SN  - 2398-6352
ER  -