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Algorithm for the analysis of pre-extraction computed tomographic images to evaluate implanted lead-lead interactions and lead-vascular attachments.

Authors :
Holm, Mikayle A.
Vatterott, Pierce J.
Gaasedelen, Erik N.
Syed, Imran
Khan, Akbar
Iles, Tinen L.
Iaizzo, Paul A.
Source :
Heart Rhythm; Jun2020, Vol. 17 Issue 6, p1009-1016, 8p
Publication Year :
2020

Abstract

<bold>Background: </bold>The number of lead extractions is growing because of the greater population and increasing age of individuals with a cardiac implantable electronic device. Lead extraction procedures can be complex undertakings with risk of significant mortality, and vascular tears in the superior vena cava are of greatest concern.<bold>Objective: </bold>The purpose of this study was to study whether a novel algorithm that analyzes pre-extraction computed tomographic (CT) images can determine the likelihood and location of lead-lead interactions and lead-vessel attachment within patients' venous vasculatures. This information can be used to identify potential case challenges in the planning stages.<bold>Methods: </bold>We developed an algorithm to estimate the presence and position of lead-lead interactions and lead-vessel adherences by tracking distance between the leads and distance between the lead and superior vena cava in a sample of 12 patients referred to the United Heart and Vascular Clinic for lead extractions due to infection (n = 5), lead failure (n = 5), and tricuspid regurgitation (n = 2).<bold>Results: </bold>Preliminary results indicate that the developed algorithm successfully identified lead-lead and lead-vascular attachments compared to review of CT images by medical experts.<bold>Conclusion: </bold>With future validation and clinical implementation, this algorithm could aid physician preparedness by minimizing intraprocedural emergencies and may improve patient outcomes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15475271
Volume :
17
Issue :
6
Database :
Supplemental Index
Journal :
Heart Rhythm
Publication Type :
Academic Journal
Accession number :
143310364
Full Text :
https://doi.org/10.1016/j.hrthm.2020.01.003