Abstract 15275: Cardiac Magnetic Resonance Ventricular Function and Hemodynamic Multivariable Model Predictors of Exercise Performance in Tetralogy of Fallot Using Machine Learning and Multivariable Modelling: A Substudy of the Single Center Cardiac Magnetic Resonance Outcomes Registry-Tetralogy of Fallot

Abstract only Background: There are no robust predictors of exercise performance in repaired tetralogy of Fallot (rTOF) using both ventricular function and hemodynamics. Aim: To utilize machine learning and multivariable modeling to determine cardiac magnetic resonance (CMR) and other metrics predic...

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Bibliographic Details
Published in:Circulation (New York, N.Y.) N.Y.), 2023-11, Vol.148 (Suppl_1)
Main Authors: Fogel, Mark A, Reddy, Keerthi, Mahmood, Abdullah, Zhang, Xuemi, Ampah, Steve, Faerber, Jen, Goldmuntz, Elizabeth, Harris, Matthew, Biko, David, Partington, Sara, Paridon, Stephen, Mcbride, Michael, Ferrari, Victor, Whitehead, Kevin K, Mercer-Rosa, Laura M
Format: Article
Language:English
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Summary:Abstract only Background: There are no robust predictors of exercise performance in repaired tetralogy of Fallot (rTOF) using both ventricular function and hemodynamics. Aim: To utilize machine learning and multivariable modeling to determine cardiac magnetic resonance (CMR) and other metrics predictive of exercise performance. Methods: We retrospectively reviewed data of all rTOF pts undergoing CMR from 2005-2020. Those who had cardiopulmonary exercising testing (CPET) within 6 months of CMR were analyzed using the machine learning technique LASSO to choose candidate variables for subsequent testing with multivariable modeling. CMR parameters included right (RV) and left ventricular (LV) function and hemodynamics. Significance P
ISSN:0009-7322
1524-4539
DOI:10.1161/circ.148.suppl_1.15275