Key result
Machine learning-based CT-FFR achieved a diagnostic accuracy of 75.0% (sensitivity 86.4%, specificity 66.1%) for detecting coronary artery disease in TAVI patients, outperforming standard cCTA.
Why the study?
Coronary artery disease commonly coexists with aortic valve stenosis, and non-invasive screening could avoid invasive coronary angiography before TAVI.
Does machine learning-based CT-FFR derived from cCTA improve the diagnosis of comorbid CAD in patients undergoing TAVI compared to cCTA alone?
Observational (n=100)
No
Does machine learning-based CT-FFR derived from cCTA improve the diagnosis of comorbid CAD in patients undergoing TAVI compared to cCTA alone?
Absolute Event Rate: 75% vs 63%
Machine learning-based CT-FFR derived from preoperative cCTA shows good diagnostic accuracy for detecting significant CAD in TAVI candidates, potentially reducing the need for invasive coronary angiography.
May improve CAD detection in TAVI candidates; leaves open whether it reduces invasive angiography pending prospective validation.
OBJECTIVES: Since coronary artery disease (CAD) is a common comorbidity in patients with aortic valve stenosis, invasive coronary angiography (ICA) can be avoided if significant CAD can be screened with the non-invasive coronary CT angiography (cCTA). This study aims to evaluate the ability of machine learning-based CT coronary fractional flow reserve (CT-FFR) derived from cCTA to aid in the diagnosis of comorbid CAD in patients undergoing transcatheter aortic valve implantation (TAVI). METHODS: A total of 100 patients who underwent both cCTA and ICA assessments prior to TAVI procedure between January 2021 and July 2023 were included. Coronary stenosis was assessed using both cCTA data and machine learning-generated CT-FFR image information for patients/major coronary vessels. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant, with ICA serving as the diagnostic gold standard. RESULTS: A total of 400 major coronary vessels were identified in 100 eligible patients who underwent TAVI. CT-FFR was 86.4% sensitive and 66.1% specific to diagnose CAD, with a positive predictive value (PPV) of 66.7% and a negative predictive value (NPV) of 86.0%. The diagnostic accuracy (Acc) was 75.0%, with a false positive rate (FPR) of 33.9%. At the vessel level, CT-FFR showed a sensitivity of 77.6% and a specificity of 76.9%. The PPV was 44.0% and the NPV was 93.6%. The Acc was 77.0% and the FPR was 23.1%. For all patient/vessel units, CT-FFR outperformed cCTA. CONCLUSION: Machine learning-based CT-FFR can effectively detect coronary hemodynamic abnormalities. Combined with preoperative cCTA in TAVI patients, it is an effective tool to rule out significant CAD, reducing unnecessary coronary angiography in this high-risk population. CLINICAL TRIAL NUMBER: Not applicable.
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Wang et al. (2025) conducted an observational in Severe aortic valve stenosis or regurgitation (n=100). Machine learning-based CT-FFR vs. cCTA was evaluated on Diagnostic accuracy for coronary artery disease at the patient level. Machine learning-based CT-FFR achieved a diagnostic accuracy of 75.0% (sensitivity 86.4%, specificity 66.1%) for detecting coronary artery disease in TAVI patients, outperforming standard cCTA.
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