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

• Verfasst von: Loßnitzer, Dirk [VerfasserIn]
• Verfasst von: Klenantz, Selina [VerfasserIn]
• Verfasst von: André, Florian [VerfasserIn]
• Verfasst von: Görich, Johannes [VerfasserIn]
• Verfasst von: Schoepf, U. Joseph [VerfasserIn]
• Verfasst von: Pazzo, Kyle L. [VerfasserIn]
• Verfasst von: Sommer, André [VerfasserIn]
• Verfasst von: Brado, Matthias [VerfasserIn]
• Verfasst von: Gückel, Friedemann [VerfasserIn]
• Verfasst von: Sokiranski, Roman [VerfasserIn]
• Verfasst von: Becher, Tobias [VerfasserIn]
• Verfasst von: Akın, Ibrahim [VerfasserIn]
• Verfasst von: Buß, Sebastian Johannes [VerfasserIn]
• Verfasst von: Baumann, Stefan [VerfasserIn]
• Titel: 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
• Verf.angabe: Dirk Lossnitzer, Selina Klenantz, Florian Andre, Johannes Goerich, U. Joseph Schoepf, Kyle L. Pazzo, Andre Sommer, Matthias Brado, Friedemann Gückel, Roman Sokiranski, Tobias Becher, Ibrahim Akin, Sebastian J. Buss and Stefan Baumann
• E-Jahr: 2022
• Jahr: 05 February 2022
• Umfang: 10 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 07.05.2024
• Titel Quelle: Enthalten in: BMC cardiovascular disorders
• Ort Quelle: London : BioMed Central, 2001
• Jahr Quelle: 2022
• Band/Heft Quelle: 22(2022), Artikel-ID 34, Seite 1-10
• ISSN Quelle: 1471-2261
• DOI: doi:10.1186/s12872-022-02467-2
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1887984798

Abstract

Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFRML) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFRML compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFRML over coronary computed tomography angiography (cCTA). 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-FFRML values were calculated from standard cCTA. CT-FFRML revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi2 values for the statistical relationship between CT-FFRML and stress perfusion CMR was significant (p < 0.0001). CT-FFRML 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-FFRML and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05). CT-FFRML 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.