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Algorithm for the analysis of pre-extraction computed tomographic images to evaluate implanted lead-lead interactions and lead-vascular attachments.
- Source :
-
Heart rhythm [Heart Rhythm] 2020 Jun; Vol. 17 (6), pp. 1009-1016. Date of Electronic Publication: 2020 Jan 10. - Publication Year :
- 2020
-
Abstract
- Background: 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.<br />Objective: 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.<br />Methods: 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).<br />Results: Preliminary results indicate that the developed algorithm successfully identified lead-lead and lead-vascular attachments compared to review of CT images by medical experts.<br />Conclusion: With future validation and clinical implementation, this algorithm could aid physician preparedness by minimizing intraprocedural emergencies and may improve patient outcomes.<br /> (Copyright © 2020 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1556-3871
- Volume :
- 17
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Heart rhythm
- Publication Type :
- Academic Journal
- Accession number :
- 31931170
- Full Text :
- https://doi.org/10.1016/j.hrthm.2020.01.003