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 Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.1186/s12872-022-02467-2
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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- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12872-022-02467-2
- https://bmccardiovascdisord.biomedcentral.com/counter/pdf/10.1186/s12872-022-02467-2
- OA Status
- gold
- Cited By
- 11
- References
- 39
- Related Works
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- OpenAlex ID
- https://openalex.org/W4210671637
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210671637Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s12872-022-02467-2Digital Object Identifier
- 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 ischemiaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-05Full publication date if available
- Authors
-
Dirk Loßnitzer, Selina Klenantz, Florian André, J. Goerich, U. Joseph Schoepf, Kyle L. Pazzo, André J. Sommer, M. Brado, F. Gückel, R. Sokiranski, Tobias Becher, İbrahim Akın, Sebastian J. Buss, Stefan BaumannList of authors in order
- Landing page
-
https://doi.org/10.1186/s12872-022-02467-2Publisher landing page
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https://bmccardiovascdisord.biomedcentral.com/counter/pdf/10.1186/s12872-022-02467-2Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://bmccardiovascdisord.biomedcentral.com/counter/pdf/10.1186/s12872-022-02467-2Direct OA link when available
- Concepts
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Medicine, Fractional flow reserve, Angiology, Perfusion, Stenosis, Perfusion scanning, Myocardial perfusion imaging, Cardiology, Magnetic resonance imaging, Internal medicine, Radiology, Hemodynamics, Coronary artery disease, Computed tomography angiography, Angiography, Myocardial infarction, Coronary angiographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
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2025: 6, 2024: 2, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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