Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography.

Background: Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort.
Methods: Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments.
Results: ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%-79.5%] vs. 54.8% [45.7%-63.8%]), LAD (79.3 [73.9-84.0] vs. 59.6 [53.5-65.6]), LCX (84.1 [76.0-90.3] vs. 63.7 [54.1-72.6]), proximal (81.5 [74.6-87.1] vs. 63.8 [55.9-71.2]), middle (81.2 [75.7-85.9] vs. 59.4 [53.0-65.6]) and distal stenosis location (67.4 [57.0-76.6] vs. 51.6 [41.1-62.0]).
Conclusion: In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.
Competing Interests: Declaration of competing interest Dr. Renker has received speaker fees from Abbott. Dr. Baumann has received consulting fees from Phillips Volcano. Dr. Tesche has received research support and honoraria for speaking from Siemens. Dr. Kim received proctor/speaker fees from Abbott, Boston Scientific, Edwards Lifesciences, Medtronic, Meril. Dr. Nieman reports unrestricted institutional support from Siemens Healthineers, Bayer, GE, and Heartflow Inc., and consultancy honoraria from Siemens Medical Solutions USA. Dr. Persson reports on institutional support from Siemens Healthineers. Dr. Schoepf has received grants and/or personal fees from Bayer, Bracco, Elucid BioImaging, GE, Guerbet, HeartFlow Inc., Keya Medical, and Siemens. All other authors declare that they have no financial disclosures.
(Copyright © 2021 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)