One-year Outcomes of CCTA Alone Versus Machine Learning-based FFR for Coronary Artery Disease: a Single-center, Prospective Study

Objectives: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFR) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis.

Methods: In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFR group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up.

Results: In total, 567 participants were allocated to the CCTA group and 566 to the FFR group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFR group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFR group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFR group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFR group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04).

Conclusion: In patients with intermediate stenosis, the FFR strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy.

Key Points: • In stable patients with intermediate stenosis, ML-based FFR strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy. • Compared with the CCTA strategy, ML-based FFRshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFR strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.