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Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram

OBJECTIVES: This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient’s physiological response to stenting. BACKGROUND: Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated w...

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Autores principales: Gosling, Rebecca C., Morris, Paul D., Silva Soto, Daniel A., Lawford, Patricia V., Hose, D. Rodney, Gunn, Julian P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503181/
https://www.ncbi.nlm.nih.gov/pubmed/29550308
http://dx.doi.org/10.1016/j.jcmg.2018.01.019
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author Gosling, Rebecca C.
Morris, Paul D.
Silva Soto, Daniel A.
Lawford, Patricia V.
Hose, D. Rodney
Gunn, Julian P.
author_facet Gosling, Rebecca C.
Morris, Paul D.
Silva Soto, Daniel A.
Lawford, Patricia V.
Hose, D. Rodney
Gunn, Julian P.
author_sort Gosling, Rebecca C.
collection PubMed
description OBJECTIVES: This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient’s physiological response to stenting. BACKGROUND: Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated with improved clinical and economic outcomes compared with angiographic guidance alone. Virtual (v)FFR can be calculated based upon a 3-dimensional (3D) reconstruction of the coronary anatomy from the angiogram, using computational fluid dynamics (CFD) modeling. This technology can be used to perform virtual stenting, with a predicted post-PCI FFR, and the prospect of optimized treatment planning. METHODS: Patients undergoing elective PCI had pressure-wire–based FFR measurements pre- and post-PCI. A 3D reconstruction of the diseased artery was generated from the angiogram and imported into the VIRTUheart workflow, without the need for any invasive physiological measurements. VCI was performed using a radius correction tool replicating the dimensions of the stent deployed during PCI. Virtual FFR (vFFR) was calculated pre- and post-VCI, using CFD analysis. vFFR pre- and post-VCI were compared with measured (m)FFR pre- and post-PCI, respectively. RESULTS: Fifty-four patients and 59 vessels underwent PCI. The mFFR and vFFR pre-PCI were 0.66 ± 0.14 and 0.68 ± 0.13, respectively. Pre-PCI vFFR deviated from mFFR by ±0.05 (mean Δ = −0.02; SD = 0.07). The mean mFFR and vFFR post-PCI/VCI were 0.90 ± 0.05 and 0.92 ± 0.05, respectively. Post-VCI vFFR deviated from post-PCI mFFR by ±0.02 (mean Δ = −0.01; SD = 0.03). Mean CFD processing time was 95 s per case. CONCLUSIONS: The authors have developed a novel VCI tool, based upon the angiogram, that predicts the physiological response to stenting with a high degree of accuracy.
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spelling pubmed-65031812019-05-10 Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram Gosling, Rebecca C. Morris, Paul D. Silva Soto, Daniel A. Lawford, Patricia V. Hose, D. Rodney Gunn, Julian P. JACC Cardiovasc Imaging Article OBJECTIVES: This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient’s physiological response to stenting. BACKGROUND: Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated with improved clinical and economic outcomes compared with angiographic guidance alone. Virtual (v)FFR can be calculated based upon a 3-dimensional (3D) reconstruction of the coronary anatomy from the angiogram, using computational fluid dynamics (CFD) modeling. This technology can be used to perform virtual stenting, with a predicted post-PCI FFR, and the prospect of optimized treatment planning. METHODS: Patients undergoing elective PCI had pressure-wire–based FFR measurements pre- and post-PCI. A 3D reconstruction of the diseased artery was generated from the angiogram and imported into the VIRTUheart workflow, without the need for any invasive physiological measurements. VCI was performed using a radius correction tool replicating the dimensions of the stent deployed during PCI. Virtual FFR (vFFR) was calculated pre- and post-VCI, using CFD analysis. vFFR pre- and post-VCI were compared with measured (m)FFR pre- and post-PCI, respectively. RESULTS: Fifty-four patients and 59 vessels underwent PCI. The mFFR and vFFR pre-PCI were 0.66 ± 0.14 and 0.68 ± 0.13, respectively. Pre-PCI vFFR deviated from mFFR by ±0.05 (mean Δ = −0.02; SD = 0.07). The mean mFFR and vFFR post-PCI/VCI were 0.90 ± 0.05 and 0.92 ± 0.05, respectively. Post-VCI vFFR deviated from post-PCI mFFR by ±0.02 (mean Δ = −0.01; SD = 0.03). Mean CFD processing time was 95 s per case. CONCLUSIONS: The authors have developed a novel VCI tool, based upon the angiogram, that predicts the physiological response to stenting with a high degree of accuracy. Elsevier 2019-05 /pmc/articles/PMC6503181/ /pubmed/29550308 http://dx.doi.org/10.1016/j.jcmg.2018.01.019 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gosling, Rebecca C.
Morris, Paul D.
Silva Soto, Daniel A.
Lawford, Patricia V.
Hose, D. Rodney
Gunn, Julian P.
Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title_full Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title_fullStr Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title_full_unstemmed Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title_short Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram
title_sort virtual coronary intervention: a treatment planning tool based upon the angiogram
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503181/
https://www.ncbi.nlm.nih.gov/pubmed/29550308
http://dx.doi.org/10.1016/j.jcmg.2018.01.019
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