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

Author:

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

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.

Publisher

MDPI AG

Subject

General Medicine

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement;Journal of Clinical Medicine;2024-09-04

2. Diagnostic Challenges in Aortic Stenosis;Journal of Cardiovascular Development and Disease;2024-05-23

3. Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications;Frontiers in Cardiovascular Medicine;2023-12-21

4. Convolutional Neural Networks for the Segmentation of Coronary Arteries;2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE);2023-12-04

5. Aortic Regurgitation: Diagnosis and Evaluation;Current Treatment Options in Cardiovascular Medicine;2023-11-27

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