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Nair PJ, Pfaller MR, Dual SA, McElhinney DB, Ennis DB, Marsden AL. Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements. Ann Biomed Eng 2024; 52:1335-1346. [PMID: 38341399 DOI: 10.1007/s10439-024-03457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
Blood pressure gradient ( Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. Δ P across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. The 0D simulations were extremely efficient ( ∼ 15 s computation time) compared to 3D simulations ( ∼ 30 h computation time on a cluster). However, the 0D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.
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Affiliation(s)
- Priya J Nair
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Martin R Pfaller
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Seraina A Dual
- Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Doff B McElhinney
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Daniel B Ennis
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Alison L Marsden
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA.
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA.
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
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Lo SCY, McCullough JWS, Xue X, Coveney PV. Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations. J R Soc Interface 2024; 21:20230656. [PMID: 38593843 PMCID: PMC11003782 DOI: 10.1098/rsif.2023.0656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/05/2024] [Indexed: 04/11/2024] Open
Abstract
Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.
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Affiliation(s)
- Sharp C. Y. Lo
- Centre for Computational Science, University College London, London, UK
| | | | - Xiao Xue
- Centre for Computational Science, University College London, London, UK
| | - Peter V. Coveney
- Centre for Computational Science, University College London, London, UK
- Advanced Research Computing Centre, University College London, London, UK
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
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Nair PJ, Pfaller MR, Dual SA, McElhinney DB, Ennis DB, Marsden AL. Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.05.23295066. [PMID: 37732242 PMCID: PMC10508787 DOI: 10.1101/2023.09.05.23295066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Purpose Blood pressure gradient (Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0 D and 3 D deformable wall simulations. Methods Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17 ). 0 D simulations were performed first and used to tune boundary conditions and initialize 3 D simulations. Δ P across the CoA estimated using both 0 D and 3 D simulations were compared to invasive catheter-based pressure measurements for validation. Results The 0 D simulations were extremely efficient (~15 secs computation time) compared to 3 D simulations (~30 hrs computation time on a cluster). However, the 0 D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3 D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0 D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0 D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3 D simulations improved this to 88%. Conclusion Overall, a combined approach, using 0 D models to efficiently tune and launch 3 D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.
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Affiliation(s)
- Priya J. Nair
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Martin R. Pfaller
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Seraina A. Dual
- Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Doff B. McElhinney
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Daniel B. Ennis
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Alison L. Marsden
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
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Gharahi H, Filonova V, Mullagura HN, Nama N, Baek S, Figueroa CA. A multiscale framework for defining homeostasis in distal vascular trees: applications to the pulmonary circulation. Biomech Model Mechanobiol 2023; 22:971-986. [PMID: 36917305 DOI: 10.1007/s10237-023-01693-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/11/2023] [Indexed: 03/16/2023]
Abstract
Pulmonary arteries constitute a low-pressure network of vessels, often characterized as a bifurcating tree with heterogeneous vessel mechanics. Understanding the vascular complexity and establishing homeostasis is important to study diseases such as pulmonary arterial hypertension (PAH). The onset and early progression of PAH can be traced to changes in the morphometry and structure of the distal vasculature. Coupling hemodynamics with vessel wall growth and remodeling (G&R) is crucial for understanding pathology at distal vasculature. Accordingly, the goal of this study is to provide a multiscale modeling framework that embeds the essential features of arterial wall constituents coupled with the hemodynamics within an arterial network characterized by an extension of Murray's law. This framework will be used to establish the homeostatic baseline characteristics of a pulmonary arterial tree, including important parameters such as vessel radius, wall thickness and shear stress. To define the vascular homeostasis and hemodynamics in the tree, we consider two timescales: a cardiac cycle and a longer period of vascular adaptations. An iterative homeostatic optimization, which integrates a metabolic cost function minimization, the stress equilibrium, and hemodynamics, is performed at the slow timescale. In the fast timescale, the pulsatile blood flow dynamics is described by a Womersley's deformable wall analytical solution. Illustrative examples for symmetric and asymmetric trees are presented that provide baseline characteristics for the normal pulmonary arterial vasculature. The results are compared with diverse literature data on morphometry, structure, and mechanics of pulmonary arteries. The developed framework demonstrates a potential for advanced parametric studies and future G&R and hemodynamics modeling of PAH.
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Affiliation(s)
- Hamidreza Gharahi
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
| | - Vasilina Filonova
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Haritha N Mullagura
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA
| | - Nitesh Nama
- Department of Mechanical & Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Seungik Baek
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA
| | - C Alberto Figueroa
- Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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An efficient, localised approach for the simulation of elastic blood vessels using the lattice Boltzmann method. Sci Rep 2021; 11:24260. [PMID: 34930939 PMCID: PMC8688478 DOI: 10.1038/s41598-021-03584-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/01/2021] [Indexed: 11/08/2022] Open
Abstract
Many numerical studies of blood flow impose a rigid wall assumption due to the simplicity of its implementation compared to a full coupling with a solid mechanics model. In this paper, we present a localised method for incorporating the effects of elastic walls into blood flow simulations using the lattice Boltzmann method implemented by the open-source code HemeLB. We demonstrate that our approach is able to more accurately capture the flow behaviour expected in elastic walled vessels than ones with rigid walls. Furthermore, we show that this can be achieved with no loss of computational performance and remains strongly scalable on high performance computers. We finally illustrate that our approach captures the same trends in wall shear stress distribution as those observed in studies using a rigorous coupling between fluid dynamics and solid mechanics models to solve flow in personalised vascular geometries. These results demonstrate that our model can be used to efficiently and effectively represent flows in elastic blood vessels.
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