Diagnostic performance of computed tomography-derived fractional flow reserve in patients with significant coronary artery stenosis

Abstract

Objective

To determine the diagnostic performance of computed tomography-derived fractional flow reserve (FFRCT) in patients with significant coronary artery stenosis (≥50%) on coronary computed tomography angiography (CCTA).

Material and methods

The FFRCT was retrospectively calculated for 73 patients with CAD-RADS 3 or 4 on CCTA who underwent conventional coronary angiography, using a deep learning-based software (DEEPVESSEL-FFR, Keya Medical, Kanbai Tech). A FFRCT value <0.80 was considered haemodynamically significant. Conventional coronary angiography was used as the reference standard to compare the effectiveness of the individual use of CCTA, the individual use of FFRCT and the combined use of CCTA and FFRCT.

Results

A FFRCT of <0.80 was observed in 62 of the 219 vessels analysed (28.3%). In a per-vessel analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 95.5%, 75%, 62.7%, 97.4%, and 81.3%, respectively for ≥50% stenosis on CCTA, and 67.2%, 88.8%, 72.6%, 85.9% and 82.2%, respectively for FFRCT, when compared to conventional coronary angiography. The combined CCTA + FFRCT interpretation improved the diagnostic performance of the test both on a per-vessel (area under the curve 0.91 vs. 0.85 for CCTA; p = 0.03 and 0.78 for FFRCT; p = 0.007) and per-patient basis (area under the curve 0.89 vs. 0.81 for CCTA; p = 0.005 and 0.79 for FFRCT; p = 0.002).

Conclusion

The combined CCTA + FFRCT interpretation with deep learning-based software improves the diagnostic performance of individual CCTA or FFRCT readings in patients with significant coronary artery stenosis.