Investigation of Type A Aortic Dissection Using Computational Modelling

Aortic dissection is a catastrophic failure of the endothelial wall that could lead to malperfusion or rupture. Computational modelling tools may help detect arterial damage. Technological advancements have led to more sophisticated forms of modelling being made available to low-grade computers. These devices can create 3D models with clinical data, where the clinical blood pressure waveforms' model can be used to form boundary conditions for assessing hemodynamic parameters, modelling blood flow propagation along the aorta to predict the development of cardiovascular disease. This study presents patient-specific data for a rare case of severe Type A aortic dissection. CT scan images were taken nine months apart, consisting of the artery both before and after dissection. The results for the pre-dissection CT showed that the pressure waveform at the ascending aorta was higher, and the systolic pressure was lagging at the descending aorta. For the post-dissection analysis, we observed the same outcome; however, the amplitude for the waveform (systolic pressure) at the ascending aorta increased in the false lumen by 25% compared to the true lumen by 3%. Also, the waveform peak (systolic) was leading by 0.01 s. The hemodynamic parameter of wall shear stress (WSS) predicted the aneurysm's existence at the ascending aorta, as well as potential aortic dissection. The high WSS contours were located at the tear location at the peak blood flow of 0.14 s, which shows the potential of this tool for earlier diagnosis of aortic dissection.

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Publication Details

Journal
Biomedicines
Published
2024-09-01
DOI
https://doi.org/10.3390/biomedicines12091973
Citations
2
Primary Topic
Aortic Disease and Treatment Approaches
Type
article
Field-Weighted Citation Impact
0.84
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article

Investigation of Type A Aortic Dissection Using Computational Modelling

Djelloul Belkacemi, Mohammad Al‐Rawi, Eric T.A. Lim, Manar Khashram
2 citations
Biomedicines
Aortic Disease and Treatment Approaches
0.84
article

Investigation of Type A Aortic Dissection Using Computational Modelling

Djelloul Belkacemi, Mohammad Al‐Rawi, Eric T.A. Lim, Manar Khashram
article en
2 citations

Abstract

Aortic dissection is a catastrophic failure of the endothelial wall that could lead to malperfusion or rupture. Computational modelling tools may help detect arterial damage. Technological advancements have led to more sophisticated forms of modelling being made available to low-grade computers. These devices can create 3D models with clinical data, where the clinical blood pressure waveforms' model can be used to form boundary conditions for assessing hemodynamic parameters, modelling blood flow propagation along the aorta to predict the development of cardiovascular disease. This study presents patient-specific data for a rare case of severe Type A aortic dissection. CT scan images were taken nine months apart, consisting of the artery both before and after dissection. The results for the pre-dissection CT showed that the pressure waveform at the ascending aorta was higher, and the systolic pressure was lagging at the descending aorta. For the post-dissection analysis, we observed the same outcome; however, the amplitude for the waveform (systolic pressure) at the ascending aorta increased in the false lumen by 25% compared to the true lumen by 3%. Also, the waveform peak (systolic) was leading by 0.01 s. The hemodynamic parameter of wall shear stress (WSS) predicted the aneurysm's existence at the ascending aorta, as well as potential aortic dissection. The high WSS contours were located at the tear location at the peak blood flow of 0.14 s, which shows the potential of this tool for earlier diagnosis of aortic dissection.

BiomedicinesVol. 12(9)
Waikato Institute of Technology (NZ), University of Auckland (NZ), Waikato Hospital (NZ), Centre de Développement des Technologies Avancées (DZ), Renewable Energy Development Center (DZ)
Good health and well-being
Openalex Percentile: Top 24%
Aortic Disease and Treatment Approaches
0.84
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