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Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Authors :
Gohmann, Robin Fabian
Seitz, Patrick
Pawelka, Konrad
Majunke, Nicolas
Schug, Adrian
Heiser, Linda
Renatus, Katharina
Desch, Steffen
Lauten, Philipp
Holzhey, David
Noack, Thilo
Wilde, Johannes
Kiefer, Philipp
Krieghoff, Christian
Lücke, Christian
Ebel, Sebastian
Gottschling, Sebastian
Borger, Michael A.
Thiele, Holger
Panknin, Christoph
Abdel-Wahab, Mohamed
Horn, Matthias
Gutberlet, Matthias
Gohmann, Robin Fabian
Seitz, Patrick
Pawelka, Konrad
Majunke, Nicolas
Schug, Adrian
Heiser, Linda
Renatus, Katharina
Desch, Steffen
Lauten, Philipp
Holzhey, David
Noack, Thilo
Wilde, Johannes
Kiefer, Philipp
Krieghoff, Christian
Lücke, Christian
Ebel, Sebastian
Gottschling, Sebastian
Borger, Michael A.
Thiele, Holger
Panknin, Christoph
Abdel-Wahab, Mohamed
Horn, Matthias
Gutberlet, Matthias
Publication Year :
2022

Abstract

Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1402190868
Document Type :
Electronic Resource