Evidence map›Paper›PMID 26934736›Full record

ArticlePloS one2016

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

Yolanda R Hill, Nick Child, Ben Hanson, Mikael Wallman, Ruben Coronel, Gernot Plank, Christopher A Rinaldi, Jaswinder Gill, Nicolas P Smith, Peter Taggart and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yolanda R HillDepartment of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
Nick ChildDepartment of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
Ben HansonDepartment of Mechanical Engineering, University College London, London, United Kingdom.
Mikael WallmanDepartment of Systems and Data Analysis, Franhofer-Chalmers Centre, Gothenburg, Sweden.
Ruben CoronelAcademic Medical Center, Amsterdam, Netherlands.
Gernot PlankInstitute of Biophysics, Medical University of Graz, Graz, Austria.
Christopher A RinaldiDepartment of Cardiology, Guys and St Thomas' Hospital, London, United Kingdom.
Jaswinder GillDepartment of Cardiology, Guys and St Thomas' Hospital, London, United Kingdom.
Nicolas P SmithDepartment of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.
Peter TaggartDepartment of Cardiovascular Sciences, University College London, London, United Kingdom.
Martin J BishopDepartment of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.

Funding

British Heart Foundation PG/ 14/66/30927British Heart Foundation PG/14/66/30927Department of HealthMedical Research Council G0901819Medical Research Council MC_PC_15031Medical Research Council MR/N011007/1Wellcome TrustWellcome Trust WT088641/ Z/09/Z
6 · The paper itself

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.

Indexed as

AnimalsCatheter AblationComputer SimulationHeart VentriclesHumansModels, AnatomicRabbitsSurgery, Computer-AssistedTachycardia, Ventricular

Identifiers

PMID26934736
PMCPMC4775046

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.