ArticlePloS one2016
Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- In silico optimization of regenerative cell therapy in the infarcted human ventricles to mitigate arrhythmic burden.Stem cell reports · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- In silico evaluation of cell therapy in acute versus chronic infarction: role of automaticity, heterogeneity and Purkinje in human.Scientific reports · 2024Article
- Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning.International journal for numerical methods in biomedical engineering · 2021Article
- The 'Digital Twin' to enable the vision of precision cardiology.European heart journal · 2020Article
- In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy.Scientific reports · 2020Article
- Evaluation of the reentry vulnerability index to predict ventricular tachycardia circuits using high-density contact mapping.Heart rhythm · 2020Article
- Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts.Biophysical journal · 2019Article
- Characterizing the clinical implementation of a novel activation-repolarization metric to identify targets for catheter ablation of ventricular tachycardias using computational models.Computers in biology and medicine · 2019Article
- Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia.Frontiers in physiology · 2019Article
- Assessment of a conduction-repolarisation metric to predict Arrhythmogenesis in right ventricular disorders.International journal of cardiology · 2018Article
- Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models.Computers in biology and medicine · 2018Article
- Patient-Specific Identification of Atrial Flutter Vulnerability-A Computational Approach to Reveal Latent Reentry Pathways.Frontiers in physiology · 2018Article
- Computational rabbit models to investigate the initiation, perpetuation, and termination of ventricular arrhythmia.Progress in biophysics and molecular biology · 2016Review
- Computational Representations of Myocardial Infarct Scars and Implications for Arrhythmogenesis.Clinical Medicine Insights. Cardiology · 2016Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Exit sites associated with scar-related reentrant arrhythmias represent important targets for catheter ablation therapy. However, their accurate location in a safe and robust manner remains a significant clinical challenge. We recently proposed a novel quantitative metric (termed the Reentry Vulnerability Index, RVI) to determine the difference between activation and repolarisation intervals measured from pairs of spatial locations during premature stimulation to accurately locate the critical site of reentry formation. In the clinic, the method showed potential to identify regions of low RVI corresponding to areas vulnerable to reentry, subsequently identified as ventricular tachycardia (VT) circuit exit sites. Here, we perform an in silico investigation of the RVI metric in order to aid the acquisition and interpretation of RVI maps and optimise its future usage within the clinic. Within idealised 2D sheet models we show that the RVI produces lower values under correspondingly more arrhythmogenic conditions, with even low resolution (8 mm electrode separation) recordings still able to locate vulnerable regions. When applied to models of infarct scars, the surface RVI maps successfully identified exit sites of the reentrant circuit, even in scenarios where the scar was wholly intramural. Within highly complex infarct scar anatomies with multiple reentrant pathways, the identified exit sites were dependent upon the specific pacing location used to compute the endocardial RVI maps. However, simulated ablation of these sites successfully prevented the reentry re-initiation. We conclude that endocardial surface RVI maps are able to successfully locate regions vulnerable to reentry corresponding to critical exit sites during sustained scar-related VT. The method is robust against highly complex and intramural scar anatomies and low resolution clinical data acquisition. Optimal location of all relevant sites requires RVI maps to be computed from multiple pacing locations.
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