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Computational Fluid Dynamics And Related Predictive Approaches To Study Type B Aortic Dissection: A Narrative Review
Kristina V. Montez, BS
1, Maham Rahimi, MD, PhD
2.
1Texas A&M University College of Medicine, Houston, TX, USA,
2Houston Methodist Hospital, Houston, TX, USA.
OBJECTIVES: This narrative literature review explores the use of computational fluid dynamic (CFD) methods to study Type B Aortic Dissection (TBAD). The review aims to provide an overview of the pathophysiology and clinical significance of TBAD and aneurysmal degeneration, the principles, methodologies, and parameters used in these models, the current challenges and limitations in recent studies, and potential future directions within this field.
METHODS: A search was conducted using Google Scholar, PubMed, arXiv, and Web of Science. Search terms included “aortic dissection,” “Type B,” “computational fluid dynamics,” “fluid-structure interaction (FSI)” and “4D flow MRI.” Additional articles were obtained from manual searches of the references found in the retrieved literature. Sources were selected based on relevance to the topic, including peer-reviewed articles, case reports, conference papers and pre-prints, ranging from older, foundational articles to more recent, forefront works.
RESULTS: For risk stratification and subsequent treatment, AD patients undergo imaging studies, such as CTA, MRI, and 4D flow MRI. Typically, uncomplicated cases undergo dynamic studies, while urgent, complicated cases undergo static studies. This imaging data can be used to inform the morphology and boundary conditions of CFD models, creating patient-specific simulations to study TBAD and analyze wall shear stress, velocity profiles, pressure gradients, and flow patterns. In the CFD studies reviewed, the majority utilized patient-specific models, however, some implemented ideal, generic aortic models to analyze the hemodynamic changes associated with TBAD, examining aneurysmal degeneration, dissection propagation, and effects of thrombosis, false lumen motion, and fenestrations. Other CFD studies examined hemodynamic changes following surgical intervention (e.g. TEVAR). Additional articles explored considerations for patient-specific CFD boundary conditions, tissue material-property assumptions, and CFD validation with in vitro phantom models. Small sample size was a common limitation amongst the studies, typically under 5 patients, the highest with n=53. A small subset of studies used FSI with patient-specific models constructed from multiple imaging modalities for comprehensive TBAD analysis.
CONCLUSIONS: Simulations integrating multiple imaging modalities and FSI with CFD have yet to be consistently implemented with large sample sizes. CFD and related predictive approaches can be used to analyze patient-specific data-integrated TBAD models to understand the short-term and long-term outcomes for individuals. The results from validated models can inform clinical decision-making for TBAD treatment, optimally stratifying treatment plan(s) to improve patient outcomes.
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