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Updated: Jan 6, 2026
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac
Julie K Shade1, Mark J Cartoski2, Plamen Nikolov3
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland.
Background:
Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate.
Objective:
The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria.
Methods:
This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome.
Results:
Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT.
Conclusion:
This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF.
