Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed tomographic angiography (CCTA) is the first-line investigation to detect CAD in symptomatic patients. This diagnostic approach risks greater second-line heart tests and treatments at a cost to the patient and health system. The National Health Service funded use of an artificial intelligence (AI) diagnostic tool, computed tomography (CT)-derived fractional flow reserve (FFR-CT), in patients with chest pain to improve physician decision-making and reduce downstream tests. This observational cohort study assessed the impact of FFR-CT on cardiovascular outcomes by including all patients investigated with CCTA during the national AI implementation program at 27 hospitals (CCTA n = 90,553 and FFR-CT n = 7,863). FFR-CT was safe, with no difference in all-cause (n = 1,134 (3.2%) versus 1,612 (2.9%), adjusted-hazard ratio (aHR) 1.00 (0.93-1.08), P = 0.97) or cardiovascular mortality (n = 465 (1.3%) versus 617 (1.1%), aHR 0.96 (0.85-1.08), P = 0.48), while reducing invasive coronary angiograms (n = 5,720 (16%) versus 8,183 (14.9%), aHR 0.93 (0.90-0.97), P < 0.001) and noninvasive cardiac tests (189/1,000 patients versus 167/1,000), P < 0.001). Implementation of an AI-diagnostic tool as part of a health intervention program was safe and beneficial to the patient pathway and health system with fewer cardiac tests at 2 years.
© 2025. The Author(s).