Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries
Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.871912/full |
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| author | Daniel J. Taylor Jeroen Feher Ian Halliday Ian Halliday D. Rodney Hose D. Rodney Hose Rebecca Gosling Rebecca Gosling Rebecca Gosling Louise Aubiniere-Robb Marcel van ‘t Veer Marcel van ‘t Veer Danielle Keulards Pim A. L. Tonino Pim A. L. Tonino Michel Rochette Julian Gunn Julian Gunn Julian Gunn Paul D. Morris Paul D. Morris Paul D. Morris |
| author_facet | Daniel J. Taylor Jeroen Feher Ian Halliday Ian Halliday D. Rodney Hose D. Rodney Hose Rebecca Gosling Rebecca Gosling Rebecca Gosling Louise Aubiniere-Robb Marcel van ‘t Veer Marcel van ‘t Veer Danielle Keulards Pim A. L. Tonino Pim A. L. Tonino Michel Rochette Julian Gunn Julian Gunn Julian Gunn Paul D. Morris Paul D. Morris Paul D. Morris |
| author_sort | Daniel J. Taylor |
| collection | DOAJ |
| description | Background: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α DP) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown.Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree.Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001).Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions. |
| first_indexed | 2024-04-12T11:41:08Z |
| format | Article |
| id | doaj.art-bab7ad7baf3c44589ca2658b2d645601 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-12T11:41:08Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-bab7ad7baf3c44589ca2658b2d6456012022-12-22T03:34:37ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-04-011310.3389/fphys.2022.871912871912Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary ArteriesDaniel J. Taylor0Jeroen Feher1Ian Halliday2Ian Halliday3D. Rodney Hose4D. Rodney Hose5Rebecca Gosling6Rebecca Gosling7Rebecca Gosling8Louise Aubiniere-Robb9Marcel van ‘t Veer10Marcel van ‘t Veer11Danielle Keulards12Pim A. L. Tonino13Pim A. L. Tonino14Michel Rochette15Julian Gunn16Julian Gunn17Julian Gunn18Paul D. Morris19Paul D. Morris20Paul D. Morris21Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomANSYS France, Villeurbanne, FranceDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomInsigneo Institute for In Silico Medicine, Sheffield, United KingdomDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomInsigneo Institute for In Silico Medicine, Sheffield, United KingdomDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomInsigneo Institute for In Silico Medicine, Sheffield, United KingdomDepartment of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United KingdomDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomDepartment of Cardiology, Catharina Hospital, Eindhoven, NetherlandsDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsDepartment of Cardiology, Catharina Hospital, Eindhoven, NetherlandsDepartment of Cardiology, Catharina Hospital, Eindhoven, NetherlandsDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsANSYS France, Villeurbanne, FranceDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomInsigneo Institute for In Silico Medicine, Sheffield, United KingdomDepartment of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United KingdomDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United KingdomInsigneo Institute for In Silico Medicine, Sheffield, United KingdomDepartment of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United KingdomBackground: Quantification of coronary blood flow is used to evaluate coronary artery disease, but our understanding of flow through branched systems is poor. Murray’s law defines coronary morphometric scaling, the relationship between flow (Q) and vessel diameter (D) and is the basis for minimum lumen area targets when intervening on bifurcation lesions. Murray’s original law (Q α DP) dictates that the exponent (P) is 3.0, whilst constant blood velocity throughout the system would suggest an exponent of 2.0. In human coronary arteries, the value of Murray’s exponent remains unknown.Aim: To establish the exponent in Murray’s power law relationship that best reproduces coronary blood flows (Q) and microvascular resistances (Rmicro) in a bifurcating coronary tree.Methods and Results: We screened 48 cases, and were able to evaluate inlet Q and Rmicro in 27 branched coronary arteries, taken from 20 patients, using a novel computational fluid dynamics (CFD) model which reconstructs 3D coronary anatomy from angiography and uses pressure-wire measurements to compute Q and Rmicro distribution in the main- and side-branches. Outputs were validated against invasive measurements using a Rayflow™ catheter. A Murray’s power law exponent of 2.15 produced the strongest correlation and closest agreement with inlet Q (zero bias, r = 0.47, p = 0.006) and an exponent of 2.38 produced the strongest correlation and closest agreement with Rmicro (zero bias, r = 0.66, p = 0.0001).Conclusions: The optimal power law exponents for Q and Rmicro were not 3.0, as dictated by Murray’s Law, but 2.15 and 2.38 respectively. These data will be useful in assessing patient-specific coronary physiology and tailoring revascularisation decisions.https://www.frontiersin.org/articles/10.3389/fphys.2022.871912/fullbifurcationleft main coronary arterystable anginatranslational physiologyMurray’s exponent |
| spellingShingle | Daniel J. Taylor Jeroen Feher Ian Halliday Ian Halliday D. Rodney Hose D. Rodney Hose Rebecca Gosling Rebecca Gosling Rebecca Gosling Louise Aubiniere-Robb Marcel van ‘t Veer Marcel van ‘t Veer Danielle Keulards Pim A. L. Tonino Pim A. L. Tonino Michel Rochette Julian Gunn Julian Gunn Julian Gunn Paul D. Morris Paul D. Morris Paul D. Morris Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries Frontiers in Physiology bifurcation left main coronary artery stable angina translational physiology Murray’s exponent |
| title | Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries |
| title_full | Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries |
| title_fullStr | Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries |
| title_full_unstemmed | Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries |
| title_short | Refining Our Understanding of the Flow Through Coronary Artery Branches; Revisiting Murray’s Law in Human Epicardial Coronary Arteries |
| title_sort | refining our understanding of the flow through coronary artery branches revisiting murray s law in human epicardial coronary arteries |
| topic | bifurcation left main coronary artery stable angina translational physiology Murray’s exponent |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.871912/full |
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