On-Site Computed Tomography-Derived Fractional Flow Reserve to Guide the Management of Patients with Stable Coronary Artery Disease: the TARGET Randomized Trial.

Background: Computed tomography-derived fractional flow reserve (CT-FFR) using on-site machine learning enables identification of both the presence of coronary artery disease and vessel-specific ischemia. However, it is unclear whether on-site CT-FFR improves clinical or economic outcomes when compared with the standard of care in patients with stable coronary artery disease. Methods: In total 1,216 patients with stable coronary artery disease and an intermediate stenosis of 30% to 90% on coronary computed tomographic angiography (CCTA) were randomized to an on-site CT-FFR care pathway using machine learning or to standard care in 6 Chinese medical centers. The primary endpoint was the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease who did not undergo intervention within 90 days. Secondary endpoints included major adverse cardiovascular events (MACE), quality of life, symptoms of angina, and medical expenditure at 1 year. Results: Baseline characteristics were similar in both groups with 72.4% (881/1,216) having either typical or atypical anginal symptoms. A total of 421 of 608 patients (69.2%) in the CT-FFR care group and 483 of 608 patients (79.4%) in the standard care group underwent invasive coronary angiography. Compared to standard care, the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease not undergoing intervention was significantly reduced in the CT-FFR care group (28.3% [119/421] vs. 46.2% [223/483] P<0.001). Overall more patients underwent revascularization in the CT-FFR care group than in the standard care group (49.7% [302/608] vs. 42.8% [260/608], P=0.02), but of MACE at 1 year did not differ (hazard ratio, 0.88; 95%CI, 0.59 to 1.30). Quality of life and symptoms improved similarly during follow-up in both groups and there was a trend towards lower costs in the CT-FFR care group (difference, -¥4233; 95%CI, -¥8165 to ¥973, P=0.07). Conclusions: On-site CT-FFR using machine learning reduced the proportion of patients with stable coronary artery disease undergoing invasive coronary angiography without obstructive disease or requiring intervention within 90 days, but increased revascularization overall without improving symptoms or quality of life, or reducing major adverse cardiovascular events.