CT-derived fractional flow reserve on therapeutic management and outcomes compared with coronary CT angiography in coronary artery disease.

Abstract

Purpose: To determine the value of on-site deep learning-based CT-derived fractional flow reserve (CT-FFR) for therapeutic management and adverse clinical outcomes in patients suspected of coronary artery disease (CAD) compared with computed tomography coronary angiography (CCTA) alone.

Materials and methods: This single-center prospective study included consecutive patients suspected of CAD between June 2021 and September 2021 at our hospital. 461 patients were randomized into either CT-FFR+CCTA or CCTA alone group. The first endpoint was the invasive coronary angiography (ICA) efficiency, defined as the ICA with non-obstructive disease (stenosis <50%) and the ratio of revascularization to ICA (REV-to-ICA ratio) within 90 days. The second endpoint was the incidence of major adverse cardiac events (MACE) at 2 years.

Results: A total of 461 patients (267 [57.9%] men; median age, 64 [55-69]) were included. At 90 days, the rate of ICA with non-obstructive disease in CT-FFR+CCTA group was lower than the CCTA group (14.7 vs. 34.0%, P = 0.047). The REV-to-ICA ratio in the CT-FFR+CCTA group was significantly higher than the CCTA group (73.5% vs. 50.9%, P = 0.036). No significant difference in ICA efficiency was found in intermediate stenosis (25-69%) between the two groups (all P > 0.05). After a median follow-up of 23 (22, 24) months, MACE were observed in 11 patients in the CT-FFR+CCTA group and 24 in the CCTA group (5.9% vs. 10.0%, P = 0.095).

Conclusion: The on-site deep learning-based CT-FFR improved the efficiency of ICA utilization with the similarly low rate of MACE compared with CCTA alone.

Advances in knowledge: The on-site deep learning-based CT-FFR was superior to CCTA for therapeutic management.