Cargando…

Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling

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 Vulnera...

Descripción completa

Detalles Bibliográficos
Autores principales: Hill, Yolanda R., Child, Nick, Hanson, Ben, Wallman, Mikael, Coronel, Ruben, Plank, Gernot, Rinaldi, Christopher A., Gill, Jaswinder, Smith, Nicolas P., Taggart, Peter, Bishop, Martin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775046/
https://www.ncbi.nlm.nih.gov/pubmed/26934736
http://dx.doi.org/10.1371/journal.pone.0149342
_version_ 1782419014888194048
author Hill, Yolanda R.
Child, Nick
Hanson, Ben
Wallman, Mikael
Coronel, Ruben
Plank, Gernot
Rinaldi, Christopher A.
Gill, Jaswinder
Smith, Nicolas P.
Taggart, Peter
Bishop, Martin J.
author_facet Hill, Yolanda R.
Child, Nick
Hanson, Ben
Wallman, Mikael
Coronel, Ruben
Plank, Gernot
Rinaldi, Christopher A.
Gill, Jaswinder
Smith, Nicolas P.
Taggart, Peter
Bishop, Martin J.
author_sort Hill, Yolanda R.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4775046
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-47750462016-03-10 Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling Hill, Yolanda R. Child, Nick Hanson, Ben Wallman, Mikael Coronel, Ruben Plank, Gernot Rinaldi, Christopher A. Gill, Jaswinder Smith, Nicolas P. Taggart, Peter Bishop, Martin J. PLoS One Research Article 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. Public Library of Science 2016-03-02 /pmc/articles/PMC4775046/ /pubmed/26934736 http://dx.doi.org/10.1371/journal.pone.0149342 Text en © 2016 Hill et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hill, Yolanda R.
Child, Nick
Hanson, Ben
Wallman, Mikael
Coronel, Ruben
Plank, Gernot
Rinaldi, Christopher A.
Gill, Jaswinder
Smith, Nicolas P.
Taggart, Peter
Bishop, Martin J.
Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title_full Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title_fullStr Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title_full_unstemmed Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title_short Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling
title_sort investigating a novel activation-repolarisation time metric to predict localised vulnerability to reentry using computational modelling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775046/
https://www.ncbi.nlm.nih.gov/pubmed/26934736
http://dx.doi.org/10.1371/journal.pone.0149342
work_keys_str_mv AT hillyolandar investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT childnick investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT hansonben investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT wallmanmikael investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT coronelruben investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT plankgernot investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT rinaldichristophera investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT gilljaswinder investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT smithnicolasp investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT taggartpeter investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling
AT bishopmartinj investigatinganovelactivationrepolarisationtimemetrictopredictlocalisedvulnerabilitytoreentryusingcomputationalmodelling