Is There a Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?


Journal

The Journal of surgical research
ISSN: 1095-8673
Titre abrégé: J Surg Res
Pays: United States
ID NLM: 0376340

Informations de publication

Date de publication:
08 2020
Historique:
received: 03 08 2019
revised: 17 01 2020
accepted: 31 01 2020
pubmed: 31 3 2020
medline: 10 9 2020
entrez: 31 3 2020
Statut: ppublish

Résumé

Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is typically an asymptomatic condition that if left untreated can expand to the point of rupture. In simple mechanical terms, rupture of an artery occurs when the local wall stress exceeds the local wall strength. It is therefore understandable that numerous studies have attempted to estimate the AAA wall stress and investigate the relationship between the AAA wall stress and AAA symptoms. We conducted computational biomechanics analysis for 19 patients with AAA (a proportion of these patients were classified as symptomatic) to investigate whether the AAA wall stress fields (both the patterns and magnitude) correlate with the clinical definition of symptomatic and asymptomatic AAAs. For computation of AAA wall stress, we used a very efficient method recently presented by the Intelligent Systems for Medicine Laboratory. The Intelligent Systems for Medicine Laboratory's method uses geometry from computed tomography images and mean arterial pressure as the applied load. The method is embedded in the software platform BioPARR-Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from http://bioparr.mech.uwa.edu.au/. The uniqueness of our stress computation approach is three-fold: i) the results are insensitive to unknown patient-specific mechanical properties of arterial wall tissue; ii) the residual stress is accounted for, according to Y.C. Fung's Uniform Stress Hypothesis; and iii) the analysis is automated and quick, making our approach compatible with clinical workflows. Symptomatic patients could not be identified from the plots (pattern) of AAA wall stress and stress magnitude. Although the largest stress was predicted for a patient who suffered from AAA symptoms, the three patients with the smallest stress were also symptomatic. The results demonstrate, contrary to the common view, that neither the wall stress magnitude nor the stress distribution appears to be associated with the presence of clinical symptoms.

Sections du résumé

BACKGROUND
Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is typically an asymptomatic condition that if left untreated can expand to the point of rupture. In simple mechanical terms, rupture of an artery occurs when the local wall stress exceeds the local wall strength. It is therefore understandable that numerous studies have attempted to estimate the AAA wall stress and investigate the relationship between the AAA wall stress and AAA symptoms.
MATERIALS AND METHODS
We conducted computational biomechanics analysis for 19 patients with AAA (a proportion of these patients were classified as symptomatic) to investigate whether the AAA wall stress fields (both the patterns and magnitude) correlate with the clinical definition of symptomatic and asymptomatic AAAs. For computation of AAA wall stress, we used a very efficient method recently presented by the Intelligent Systems for Medicine Laboratory. The Intelligent Systems for Medicine Laboratory's method uses geometry from computed tomography images and mean arterial pressure as the applied load. The method is embedded in the software platform BioPARR-Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from http://bioparr.mech.uwa.edu.au/. The uniqueness of our stress computation approach is three-fold: i) the results are insensitive to unknown patient-specific mechanical properties of arterial wall tissue; ii) the residual stress is accounted for, according to Y.C. Fung's Uniform Stress Hypothesis; and iii) the analysis is automated and quick, making our approach compatible with clinical workflows.
RESULTS
Symptomatic patients could not be identified from the plots (pattern) of AAA wall stress and stress magnitude. Although the largest stress was predicted for a patient who suffered from AAA symptoms, the three patients with the smallest stress were also symptomatic.
CONCLUSIONS
The results demonstrate, contrary to the common view, that neither the wall stress magnitude nor the stress distribution appears to be associated with the presence of clinical symptoms.

Identifiants

pubmed: 32222592
pii: S0022-4804(20)30082-2
doi: 10.1016/j.jss.2020.01.025
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

37-46

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Karol Miller (K)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Hozan Mufty (H)

Department of Vascular Surgery, University Hospitals Leuven, Leuven, Belgium.

Alastair Catlin (A)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Christopher Rogers (C)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Bradley Saunders (B)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Ross Sciarrone (R)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Inge Fourneau (I)

Department of Vascular Surgery, University Hospitals Leuven, Leuven, Belgium.

Bart Meuris (B)

Department of Cardiac Surgery, University Hospitals Leuven, Leuven, Belgium.

Angus Tavner (A)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Grand R Joldes (GR)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia.

Adam Wittek (A)

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Western Australia, Australia. Electronic address: adam.wittek@uwa.edu.au.

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