Literature DB >> 26394813

Changes in Aortic Volumes Following Endovascular Sealing of Abdominal Aortic Aneurysms With the Nellix Endoprosthesis.

Usman Shaikh1, Tze Y Chan2, Olufemi Oshin1, Richard G McWilliams1, Robert K Fisher1, Andrew England3, Francesco Torella4.   

Abstract

PURPOSE: To investigate the effects on aortic volumes of endovascular aneurysm sealing (EVAS) with the Nellix device.
METHODS: Twenty-five consecutive patients (mean age 78±7 years; 17 men) with abdominal aortic aneurysms containing thrombus were treated with EVAS. Their pre- and post-EVAS computed tomography (CT) scans were reviewed to document volume changes in the entire aneurysmal aorta, the lumen, and the intraluminal thrombus. The changes are reported as the mean and 95% confidence interval (CI).
RESULTS: Total aortic volume was greater on postoperative scans by a mean 17 mL (95% CI 10.0 to 23.5, p<0.001). The volume occupied by the endobags was greater than the preoperative lumen volume by a mean 28 mL (95% CI 24.7 to 31.7, p=0.002). Postoperatively, the aortic volume occupied by thrombus had decreased by a mean 11 mL (95% CI 4.7 to 18.2, p<0.001). There were good correlations between changes in aneurysm and thrombus volumes (r=0.864, p<0.001), between the planning CT/EVAS time interval and the change in aneurysm volume (r=0.640, p=0.001), and between the planning CT/EVAS time interval and the change in thrombus volume (r=0.567, p=0.003).
CONCLUSION: There are significant changes in aortic volumes post EVAS. These changes may be a direct consequence of the technique and have implications for the planning and performance of EVAS.
© The Author(s) 2015.

Entities:  

Keywords:  abdominal aortic aneurysm; aortic lumen; computed tomography; endovascular aneurysm sealing; thrombus; volume change

Mesh:

Year:  2015        PMID: 26394813     DOI: 10.1177/1526602815607186

Source DB:  PubMed          Journal:  J Endovasc Ther        ISSN: 1526-6028            Impact factor:   3.487


  1 in total

1.  Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning.

Authors:  Liam Burrows; Ke Chen; Weihong Guo; Martin Hossack; Richard G McWilliams; Francesco Torella
Journal:  Sci Rep       Date:  2022-08-20       Impact factor: 4.996

  1 in total

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