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Kandangwa P, Cheng K, Patel M, Sherwin SJ, de Silva R, Weinberg PD. Relative Residence Time Can Account for Half of the Anatomical Variation in Fatty Streak Prevalence Within the Right Coronary Artery. Ann Biomed Eng 2024:10.1007/s10439-024-03607-9. [PMID: 39287909 DOI: 10.1007/s10439-024-03607-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024]
Abstract
PURPOSE The patchy anatomical distribution of atherosclerosis has been attributed to variation in haemodynamic wall shear stress (WSS). The consensus is that low WSS and a high Oscillatory Shear Index (OSI) trigger the disease. We found that atherosclerosis at aortic branch sites correlates threefold better with transverse WSS (transWSS), a metric which quantifies multidirectional near-wall flow. Coronary artery disease has greater clinical significance than aortic disease but computation of WSS metrics is complicated by the substantial vessel motion occurring during each cardiac cycle. Here we present the first comparison of the distribution of atherosclerosis with WSS metrics computed for moving coronary arteries. METHODS Maps of WSS metrics were computed using dynamic geometries reconstructed from angiograms of ten non-stenosed human right coronary arteries (RCAs). They were compared with maps of fatty streak prevalence derived from a previous study of 1852 RCAs. RESULTS Time average WSS (TAWSS), OSI, transWSS and the cross-flow index (CFI), a non-dimensional form of the transWSS, gave non-significant or significant but low spatial correlations with lesion prevalence. The highest correlation coefficient (0.71) was for the relative residence time (RRT), a metric that decreases with TAWSS and increases with OSI. The coefficient was not changed if RRT was calculated using CFI, which captures multidirectional WSS only, rather than OSI, which encompasses both multidirectional and oscillatory WSS. CONCLUSION Contrary to our earlier findings in the aorta, low WSS in combination with highly multidirectional flow correlates best with lesion location in the RCA, explaining approximately half of its anatomical variation.
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Affiliation(s)
- Pratik Kandangwa
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
- Department of Aeronautics, Imperial College London, London, SW7 2AZ, UK
| | - Kevin Cheng
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Miten Patel
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
- Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Spencer J Sherwin
- Department of Aeronautics, Imperial College London, London, SW7 2AZ, UK
| | - Ranil de Silva
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
- Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Peter D Weinberg
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
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Taylor DJ, Saxton H, Halliday I, Newman T, Hose DR, Kassab GS, Gunn JP, Morris PD. Systematic review and meta-analysis of Murray's law in the coronary arterial circulation. Am J Physiol Heart Circ Physiol 2024; 327:H182-H190. [PMID: 38787386 PMCID: PMC11380967 DOI: 10.1152/ajpheart.00142.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024]
Abstract
Murray's law has been viewed as a fundamental law of physiology. Relating blood flow ([Formula: see text]) to vessel diameter (D) ([Formula: see text]·∝·D3), it dictates minimum lumen area (MLA) targets for coronary bifurcation percutaneous coronary intervention (PCI). The cubic exponent (3.0), however, has long been disputed, with alternative theoretical derivations, arguing this should be closer to 2.33 (7/3). The aim of this meta-analysis was to quantify the optimum flow-diameter exponent in human and mammalian coronary arteries. We conducted a systematic review and meta-analysis of all articles quantifying an optimum flow-diameter exponent for mammalian coronary arteries within the Cochrane library, PubMed Medline, Scopus, and Embase databases on 20 March 2023. A random-effects meta-analysis was used to determine a pooled flow-diameter exponent. Risk of bias was assessed with the National Institutes of Health (NIH) quality assessment tool, funnel plots, and Egger regression. From a total of 4,772 articles, 18 were suitable for meta-analysis. Studies included data from 1,070 unique coronary trees, taken from 372 humans and 112 animals. The pooled flow diameter exponent across both epicardial and transmural arteries was 2.39 (95% confidence interval: 2.24-2.54; I2 = 99%). The pooled exponent of 2.39 showed very close agreement with the theoretical exponent of 2.33 (7/3) reported by Kassab and colleagues. This exponent may provide a more accurate description of coronary morphometric scaling in human and mammalian coronary arteries, as compared with Murray's original law. This has important implications for the assessment, diagnosis, and interventional treatment of coronary artery disease.
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Affiliation(s)
- Daniel J Taylor
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
- NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Harry Saxton
- Materials and Engineering Research Institute, Sheffield Hallam University, Sheffield, United Kingdom
| | - Ian Halliday
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Tom Newman
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - D R Hose
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, United States
| | - Julian P Gunn
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Paul D Morris
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- NIHR Sheffield Biomedical Research Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
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Hsieh YF, Lee CK, Wang W, Huang YC, Lee WJ, Wang TD, Chou CY. Coronary CT angiography-based estimation of myocardial perfusion territories for coronary artery FFR and wall shear stress simulation. Sci Rep 2021; 11:13855. [PMID: 34226598 PMCID: PMC8257574 DOI: 10.1038/s41598-021-93237-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/21/2021] [Indexed: 11/30/2022] Open
Abstract
This study aims to apply a CCTA-derived territory-based patient-specific estimation of boundary conditions for coronary artery fractional flow reserve (FFR) and wall shear stress (WSS) simulation. The non-invasive simulation can help diagnose the significance of coronary stenosis and the likelihood of myocardial ischemia. FFR is often regarded as the gold standard to evaluate the functional significance of stenosis in coronary arteries. In another aspect, proximal wall shear stress (\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{{WSS}_{prox}}$$\end{document}WSSprox) can also be an indicator of plaque vulnerability. During the simulation process, the mass flow rate of the blood in coronary arteries is one of the most important boundary conditions. This study utilized the myocardium territory to estimate and allocate the mass flow rate. 20 patients are included in this study. From the knowledge of anatomical information of coronary arteries and the myocardium, the territory-based FFR and the \documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{{WSS}_{prox}}$$\end{document}WSSprox can both be derived from fluid dynamics simulations. Applying the threshold of distinguishing between significant and non-significant stenosis, the territory-based method can reach the accuracy, sensitivity, and specificity of 0.88, 0.90, and 0.80, respectively. For significantly stenotic cases (\documentclass[12pt]{minimal}
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\begin{document}$$\mathrm{FFR}_{m}$$\end{document}FFRm\documentclass[12pt]{minimal}
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\begin{document}$$\le$$\end{document}≤ 0.80), the vessels usually have higher wall shear stress in the proximal region of the lesion.
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Affiliation(s)
- Yu-Fang Hsieh
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, 106, Taiwan
| | - Chih-Kuo Lee
- Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, 300, Taiwan
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, 106, Taiwan
| | - Yu-Cheng Huang
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, 100, Taiwan
| | - Wen-Jeng Lee
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, 100, Taiwan
| | - Tzung-Dau Wang
- Cardiovascular Center and Divisions of Cardiology and Hospital Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan
| | - Cheng-Ying Chou
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, 106, Taiwan.
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Cerrato E, Mejía-Rentería H, Franzè A, Quadri G, Belliggiano D, Biscaglia S, Lo Savio L, Spataro F, Erriquez A, Giacobbe F, Vergara-Uzcategui C, di Girolamo D, Tebaldi M, Varbella F, Campo G, Escaned J. Quantitative flow ratio as a new tool for angiography-based physiological evaluation of coronary artery disease: a review. Future Cardiol 2021; 17:1435-1452. [PMID: 33739146 DOI: 10.2217/fca-2020-0199] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The functional evaluation of coronary stenoses has obtained important clinical results in recent years, resulting in strong guideline recommendations. Nonetheless, the use of coronary wire-based functional evaluation has not yet become part of the routine in catheterization laboratories for several reasons, including the need to advance a wire into the coronary vessel to interrogate the stenosis. Angiography-derived indexes have been introduced to expand the current use of physiology to estimate the functional meaning of a stenosis on the basis of angiographic data only. The most studied and validated angiography-derived index is certainly the quantitative flow ratio. This article will summarize the basics of the quantitative flow ratio, the related validation studies and its current and future applications.
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Affiliation(s)
- Enrico Cerrato
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Hernan Mejía-Rentería
- Department of Cardiology, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos & Universidad Complutense de Madrid, Madrid, Spain
| | - Alfonso Franzè
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Giorgio Quadri
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Davide Belliggiano
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Simone Biscaglia
- Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Cona, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, RA, Italy
| | - Luca Lo Savio
- Interventional Cardiology Unit, Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Fabio Spataro
- Interventional Cardiology Unit, Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Andrea Erriquez
- Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Cona, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, RA, Italy
| | - Federico Giacobbe
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Carlos Vergara-Uzcategui
- Department of Cardiology, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos & Universidad Complutense de Madrid, Madrid, Spain
| | | | - Matteo Tebaldi
- Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Cona, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, RA, Italy
| | - Ferdinando Varbella
- Interventional Cardiology Unit, San Luigi Gonzaga University Hospital, Orbassano & Rivoli Infermi Hospital, Rivoli, Turin, Italy
| | - Gianluca Campo
- Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Cona, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, RA, Italy
| | - Javier Escaned
- Department of Cardiology, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos & Universidad Complutense de Madrid, Madrid, Spain
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Brummer AB, Lymperopoulos P, Shen J, Tekin E, Bentley LP, Buzzard V, Gray A, Oliveras I, Enquist BJ, Savage VM. Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling. J R Soc Interface 2021; 18:20200624. [PMID: 33402023 PMCID: PMC7879751 DOI: 10.1098/rsif.2020.0624] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks—mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii—which dictate essential biologic functions related to resource transport and supply—are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship across organisms for how metabolic rate depends on body mass.
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Affiliation(s)
- Alexander B Brummer
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.,Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | | | - Jocelyn Shen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elif Tekin
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lisa P Bentley
- Department of Biology, Sonoma State University, Rohnert Park, CA, USA
| | - Vanessa Buzzard
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA
| | - Andrew Gray
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Imma Oliveras
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
| | - Brian J Enquist
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.,Santa Fe Institute, Santa Fe, NM, USA
| | - Van M Savage
- Institute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.,Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,Santa Fe Institute, Santa Fe, NM, USA
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