Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

Autor:	Mayourian J; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., La Cava WG; Computational Health Informatics Program (W.G.L.C.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., Vaid A; The Charles Bronfman Institute of Personalized Medicine (A.V., G.N.N., S.Q.D.), Icahn School of Medicine at Mount Sinai, New York, NY., Nadkarni GN; The Charles Bronfman Institute of Personalized Medicine (A.V., G.N.N., S.Q.D.), Icahn School of Medicine at Mount Sinai, New York, NY., Ghelani SJ; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., Mannix R; Department of Medicine, Division of Emergency Medicine (R.M.), Boston Children's Hospital, MA.; Harvard Medical School (R.M.), Boston, MA., Geva T; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., Dionne A; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., Alexander ME; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA., Duong SQ; The Charles Bronfman Institute of Personalized Medicine (A.V., G.N.N., S.Q.D.), Icahn School of Medicine at Mount Sinai, New York, NY.; Division of Pediatric Cardiology, Department of Pediatrics (S.Q.D.), Icahn School of Medicine at Mount Sinai, New York, NY., Triedman JK; Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.; Department of Pediatrics (J.M., W.G.L.C., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston, MA.
Jazyk:	angličtina
Zdroj:	Circulation [Circulation] 2024 Mar 19; Vol. 149 (12), pp. 917-931. Date of Electronic Publication: 2024 Feb 05.
DOI:	10.1161/CIRCULATIONAHA.123.067750
Abstrakt:	Background: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. Methods: A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). Results: The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). Conclusions: This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists. Competing Interests: Dr Nadkarni reports consultancy agreements with AstraZeneca, BioVie, GLG Consulting, Pensieve Health, Reata, Renalytix, Siemens Healthineers, and Variant Bio; research funding from Goldfinch Bio and Renalytix; honoraria from AstraZeneca, BioVie, Lexicon, Daiichi Sankyo, Menarini Health, and Reata; patents or royalties with Renalytix; equity and stock options in Pensieve Health and Renalytix as a scientific cofounder; equity in Verici Dx; financial compensation as a scientific board member and advisor to Renalytix; advisory board of Neurona Health; and advisory or leadership role for Pensieve Health and Renalytix, none of which played a role in the design or conduct of this study. The other authors report no disclosures.
Databáze:	MEDLINE
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