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Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.
Mayourian, Joshua; Geggel, Robert; La Cava, William G; Ghelani, Sunil J; Triedman, John K.
Afiliación
  • Mayourian J; Department of Cardiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
  • Geggel R; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • La Cava WG; Department of Cardiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
  • Ghelani SJ; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • Triedman JK; Department of Cardiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, USA.
Pediatr Cardiol ; 2024 Jul 02.
Article en En | MEDLINE | ID: mdl-38953953
ABSTRACT
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its application to pediatric populations remains underexplored. In this study, we trained a convolutional neural network (AI-pECG) on paired ECG-echocardiograms (≤ 2 days apart) to detect ASD2 from patients ≤ 18 years old without major congenital heart disease. Model performance was evaluated on the first ECG-echocardiogram pair per patient for Boston Children's Hospital internal testing and emergency department cohorts using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. The training cohort comprised of 92,377 ECG-echocardiogram pairs (46,261 patients; median age 8.2 years) with an ASD2 prevalence of 6.7%. Test groups included internal testing (12,631 patients; median age 7.4 years; 6.9% prevalence) and emergency department (2,830 patients; median age 7.5 years; 4.9% prevalence) cohorts. Model performance was higher in the internal test (AUROC 0.84, AUPRC 0.46) cohort than the emergency department cohort (AUROC 0.80, AUPRC 0.30). In both cohorts, AI-pECG outperformed ECG findings of incomplete right bundle branch block. Model explainability analyses suggest high-risk limb lead features include greater amplitude P waves (suggestive of right atrial enlargement) and V1 RSR' (suggestive of RBBB). Our findings demonstrate the promise of AI-pECG to inexpensively screen and/or detect ASD2 in pediatric patients. Future multicenter validation and prospective trials to inform clinical decision making are warranted.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Pediatr Cardiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Pediatr Cardiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos