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.
Highlights
Coronary artery disease (CAD) is a frequent comorbidity in patients with severe aortic stenosis and is recommended to be excluded prior to transcatheter aortic valve implantation (TAVI) [1,2]
In 7 patients, machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) was not feasible. Reasons for this were image quality hindering the seamless segmentation of the coronary tree or coronary anatomy, namely coronary anomalies or atypically dominant branches, outside of the boundaries the algorithm had been trained for [24], rendering errors during computation of CT-FFR (n = 4) (Figure 1)
Two patients initially categorized as false negative (FN) by coronary CT-angiography (cCTA) were recategorized as true positive (TP)
Summary
Coronary artery disease (CAD) is a frequent comorbidity in patients with severe aortic stenosis and is recommended to be excluded prior to transcatheter aortic valve implantation (TAVI) [1,2]. 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). With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. 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
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