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Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia

BACKGROUND: Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFR(ML)) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFR(ML) compared to stress perfusion cardiovascular magnetic resonance (CM...

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Autores principales: Lossnitzer, Dirk, Klenantz, Selina, Andre, Florian, Goerich, Johannes, Schoepf, U. Joseph, Pazzo, Kyle L., Sommer, Andre, Brado, Matthias, Gückel, Friedemann, Sokiranski, Roman, Becher, Tobias, Akin, Ibrahim, Buss, Sebastian J., Baumann, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817462/
https://www.ncbi.nlm.nih.gov/pubmed/35120459
http://dx.doi.org/10.1186/s12872-022-02467-2
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author Lossnitzer, Dirk
Klenantz, Selina
Andre, Florian
Goerich, Johannes
Schoepf, U. Joseph
Pazzo, Kyle L.
Sommer, Andre
Brado, Matthias
Gückel, Friedemann
Sokiranski, Roman
Becher, Tobias
Akin, Ibrahim
Buss, Sebastian J.
Baumann, Stefan
author_facet Lossnitzer, Dirk
Klenantz, Selina
Andre, Florian
Goerich, Johannes
Schoepf, U. Joseph
Pazzo, Kyle L.
Sommer, Andre
Brado, Matthias
Gückel, Friedemann
Sokiranski, Roman
Becher, Tobias
Akin, Ibrahim
Buss, Sebastian J.
Baumann, Stefan
author_sort Lossnitzer, Dirk
collection PubMed
description BACKGROUND: Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFR(ML)) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFR(ML) compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFR(ML) over coronary computed tomography angiography (cCTA). METHODS: Our retrospective analysis included 269 vessels in 141 patients (mean age 67 ± 9 years, 78% males) who underwent clinically indicated cCTA and subsequent stress perfusion CMR within a period of 2 months. CT-FFR(ML) values were calculated from standard cCTA. RESULTS: CT-FFR(ML) revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi(2) values for the statistical relationship between CT-FFR(ML) and stress perfusion CMR was significant (p < 0.0001). CT-FFR(ML) and cCTA (≥ 70% stenosis) provided a per patient sensitivity of 88% (95%CI 64–99%) and 59% (95%CI 33–82%); specificity of 90% (95%CI 84–95%) and 85% (95%CI 78–91%); positive predictive value of 56% (95%CI 42–69%) and 36% (95%CI 24–50%); negative predictive value of 98% (95%CI 94–100%) and 94% (95%CI 90–96%); accuracy of 90% (95%CI 84–94%) and 82% (95%CI 75–88%) when compared to stress perfusion CMR. The accuracy of cCTA (≥ 50% stenosis) was 19% (95%CI 13–27%). The AUCs were 0.89 for CT-FFR(ML) and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05). CONCLUSION: CT-FFR(ML) compared to stress perfusion CMR as the reference standard shows high diagnostic power in the identification of patients with hemodynamically significant coronary artery stenosis. This could support the role of cCTA as gatekeeper for further downstream testing and may reduce the number of patients undergoing unnecessary invasive workup.
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spelling pubmed-88174622022-02-07 Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia Lossnitzer, Dirk Klenantz, Selina Andre, Florian Goerich, Johannes Schoepf, U. Joseph Pazzo, Kyle L. Sommer, Andre Brado, Matthias Gückel, Friedemann Sokiranski, Roman Becher, Tobias Akin, Ibrahim Buss, Sebastian J. Baumann, Stefan BMC Cardiovasc Disord Research Article BACKGROUND: Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFR(ML)) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFR(ML) compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFR(ML) over coronary computed tomography angiography (cCTA). METHODS: Our retrospective analysis included 269 vessels in 141 patients (mean age 67 ± 9 years, 78% males) who underwent clinically indicated cCTA and subsequent stress perfusion CMR within a period of 2 months. CT-FFR(ML) values were calculated from standard cCTA. RESULTS: CT-FFR(ML) revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi(2) values for the statistical relationship between CT-FFR(ML) and stress perfusion CMR was significant (p < 0.0001). CT-FFR(ML) and cCTA (≥ 70% stenosis) provided a per patient sensitivity of 88% (95%CI 64–99%) and 59% (95%CI 33–82%); specificity of 90% (95%CI 84–95%) and 85% (95%CI 78–91%); positive predictive value of 56% (95%CI 42–69%) and 36% (95%CI 24–50%); negative predictive value of 98% (95%CI 94–100%) and 94% (95%CI 90–96%); accuracy of 90% (95%CI 84–94%) and 82% (95%CI 75–88%) when compared to stress perfusion CMR. The accuracy of cCTA (≥ 50% stenosis) was 19% (95%CI 13–27%). The AUCs were 0.89 for CT-FFR(ML) and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05). CONCLUSION: CT-FFR(ML) compared to stress perfusion CMR as the reference standard shows high diagnostic power in the identification of patients with hemodynamically significant coronary artery stenosis. This could support the role of cCTA as gatekeeper for further downstream testing and may reduce the number of patients undergoing unnecessary invasive workup. BioMed Central 2022-02-05 /pmc/articles/PMC8817462/ /pubmed/35120459 http://dx.doi.org/10.1186/s12872-022-02467-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Lossnitzer, Dirk
Klenantz, Selina
Andre, Florian
Goerich, Johannes
Schoepf, U. Joseph
Pazzo, Kyle L.
Sommer, Andre
Brado, Matthias
Gückel, Friedemann
Sokiranski, Roman
Becher, Tobias
Akin, Ibrahim
Buss, Sebastian J.
Baumann, Stefan
Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title_full Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title_fullStr Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title_full_unstemmed Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title_short Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
title_sort stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817462/
https://www.ncbi.nlm.nih.gov/pubmed/35120459
http://dx.doi.org/10.1186/s12872-022-02467-2
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