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Computational Fluid Dynamics Modeling of Hemodynamic Parameters in the Human Diseased Aorta: A Systematic Review
Chi Wei Ong1, Ian Wee2, Nicholas Syn2
1Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore; SingVaSC, Singapore Vascular Surgical Collaborative, Singapore, Singapore.
Insights
Computational fluid dynamics (CFD) shows promise for predicting aortic disease progression and treatment outcomes. This systematic review highlights CFD
Area of Science:
- Cardiovascular Science
- Medical Imaging Analysis
- Computational Fluid Dynamics (CFD)
Background:
- Computational fluid dynamics (CFD) analysis correlates blood flow with aortic pathology, aiding disease prediction and treatment guidance.
- A comprehensive systematic review of CFD applications in aortic diseases and their treatments is lacking.
Purpose of the Study:
- To systematically review and analyze the published literature on the application of CFD in various aortic diseases.
- To assess the quality and validation methods of CFD studies in aortic pathology.
Main Methods:
- Systematic review of 136 articles investigating CFD in aortic aneurysms, dissections, and coarctation.
- Inclusion of treated and untreated aortic pathologies analyzed with CFD.
- Grading of studies using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach based on CFD validation.
Main Results:
- No randomized controlled trials were found for CFD efficacy in aortic pathology.
- A majority of studies are observational; only 21% utilized clinical imaging for CFD result validation, and these were high-quality.
- Limited independent validation of CFD results exists in the current literature.
Conclusions:
- CFD provides valuable hemodynamic parameters (e.g., wall shear stress, vorticity) for both treated and untreated aortic diseases.
- These parameters hold potential for predicting disease progression and the impact of surgical interventions.
- CFD can assist clinicians in optimizing treatment choices and timing for aortic conditions.
Background:
The analysis of the correlation between blood flow and aortic pathology through computational fluid dynamics (CFD) shows promise in predicting disease progression, the effect of operative intervention, and guiding patient treatment. However, to date, there has not been a comprehensive systematic review of the published literature describing CFD in aortic diseases and their treatment.
Methods:
This review includes 136 published articles which have investigated the application of CFD in all types of aortic disease (aneurysms, dissections, and coarctation). We took into account case studies of both, treated or untreated pathology, investigated with CFD. We also graded all studies using an author-defined Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach based on the validation method used for the CFD results.
Results:
There are no randomized controlled trials assessing the efficacy of CFD as applied to aortic pathology, treated or untreated. Although a large number of observational studies are available, those using clinical imaging tools as independent validation of the calculated CFD results exist in far smaller numbers. Only 21% of all studies used clinical imaging as a tool to validate the CFD results and these were graded as high-quality studies.
Conclusions:
Contemporary evidence shows that CFD can provide additional hemodynamic parameters such as wall shear stress, vorticity, disturbed laminar flow, and recirculation regions in untreated and treated aortic disease. These have the potential to predict the progression of aortic disease, the effect of operative intervention, and ultimately help guide the choice and timing of treatment to the benefit of patients and clinicians alike.
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