Automated computed tomography-derived fractional flow reserve model for diagnosing haemodynamically significant coronary artery disease: a prospective validation study.

Abstract

Aims: This study aims to assess the diagnostic performance of a novel computed tomography-derived fractional flow reserve (CT-FFR) algorithm and to compare its accuracy at three predefined sites: (i) at the location of invasive FFR measurements (CT-FFR_atloc), (ii) at selected sites determined by an automated module integrated within the algorithm (CT-FFR_auto), and (iii) distally in the vessel (CT-FFR_distal).

Methods and results: We prospectively recruited 108 consecutive patients with stable symptoms of coronary artery disease and at least one suspected obstructive lesion on coronary computed tomography angiography (CCTA). CT-FFR was validated against invasive FFR as gold standard using FFR ≤ 0.80 to define myocardial ischaemia. CT-FFR_atloc showed good correlation with invasive FFR (r = 0.67) and improved the ability to detect myocardial ischaemia compared with CCTA at both lesion [area under the curve (AUC) 0.83 vs. 0.65, P < 0.001] and patient level (AUC 0.87 vs. 0.74, P = 0.007). CT-FFR_auto demonstrated similar diagnostic accuracy to CT-FFR_atloc and significantly improved specificity compared with CT-FFR_distal (86% vs. 49%, P < 0.001). High end CT quality improved the diagnostic performance of CT-FFR_auto, demonstrating an AUC of 0.92; similarly, the performance was improved in patients with low-to-intermediate coronary artery calcium score with an AUC of 0.88.

Conclusion: Implementing an automated module to determine the site of CT-FFR evaluations was feasible, and CT-FFR_auto demonstrated comparable diagnostic accuracy to CT-FFR_atloc when assessed against invasive FFR. Both CT-FFR_atloc and CT-FFR_auto improved the diagnostic performance compared with CCTA and improved specificity compared with CT-FFR_distal. High end CT quality and low-to-intermediate calcium burden improved the diagnostic performance of our algorithm.

Clinicaltrialsgov identifier: NCT03045601.