ArticleHeart rhythm2020
Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models.
Article in Heart rhythm, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 53 citations in OpenAlex.
- Repaired tetralogy of Fallot across adulthood: from right ventricular remodelling to targeted intervention.Heart failure reviews · 2026Review
- The Hodgkin-Huxley-Katz Prize Lecture: Heart digital twins: A new paradigm in arrhythmia medicine.The Journal of physiology · 2026Article
- Genotype-specific digital twins for arrhythmia ablation targeting in arrhythmogenic right ventricular cardiomyopathy.Communications medicine · 2026Article
- Discrimination of sustained VT in structural heart disease using LGE-CMR and computational simulation: moving beyond LVEF.Frontiers in cardiovascular medicine · 2026Article
- Genotype-specific Digital Twins for Arrhythmia Ablation Targeting in Arrhythmogenic Right Ventricular Cardiomyopathy.Research square · 2025Article
- Heart Digital Twins Predict Features of Invasive Reentrant Circuits and Ablation Lesions in Scar-Dependent Ventricular Tachycardia.Circulation. Arrhythmia and electrophysiology · 2025Article
- SCN10A-short gene therapy to restore conduction and protect against malignant cardiac arrhythmias.European heart journal · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Personalized Heart Digital Twins Detect Substrate Abnormalities in Scar-Dependent Ventricular Tachycardia.Circulation · 2025Observational
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- Medical Digital Twin: A Review on Technical Principles and Clinical Applications.Journal of clinical medicine · 2025Review
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient.Computer methods in biomechanics and biomedical engineering · 2024Article
- Digital twins in medicine.Nature computational science · 2024Review
- Fibrosis modeling choice affects morphology of ventricular arrhythmia in non-ischemic cardiomyopathy.Frontiers in physiology · 2024Article
- Up digital and personal: How heart digital twins can transform heart patient care.Heart rhythm · 2024Review
- Use of Cardiovascular Magnetic Resonance for Risk Stratification in Repaired Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- Article
- Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAdults 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.
objectiveThe 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.
methodsThis 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.
resultsTwo 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.
conclusionThis 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.
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