One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study

Abstract

Objectives: To explore downstream management and outcomes of machine learning (ML)-based CT derived fractional flow reserve (FFR_CT) 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_CT 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_CT 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_CT 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_CT 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_CT 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_CT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04).

Conclusion: In patients with intermediate stenosis, the FFR_CT 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_CT 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 FFR_CTshows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE. • ML-based FFR_CT strategy as a non-invasive "one-stop-shop" modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

Keywords: Computed tomography angiography; Coronary artery disease; Fractional flow reserve; Invasive coronary angiography.