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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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Detalles Bibliográficos
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
Descripción
Sumario: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.