Author + information
- Received December 4, 2018
- Revision received June 10, 2019
- Accepted June 19, 2019
- Published online August 14, 2019.
- Christian Tesche, MDa,b,c,
- Katharina Otani, PhDd,
- Carlo N. De Cecco, MD, PhDa,
- Adriaan Coenen, MDe,f,
- Jakob De Geer, MD, PhDg,
- Mariusz Kruk, MD, PhDh,
- Young-Hak Kim, MD, PhDi,
- Moritz H. Albrecht, MDa,j,
- Stefan Baumann, MDa,k,
- Matthias Renker, MDa,l,
- Richard R. Bayer, MDa,m,
- Taylor M. Duguay, BSa,
- Sheldon E. Litwin, MDa,m,
- Akos Varga-Szemes, MD, PhDa,
- Daniel H. Steinberg, MDm,
- Dong Hyun Yang, MD, PhDn,
- Cezary Kepka, MD, PhDh,
- Anders Persson, MD, PhDg,
- Koen Nieman, MDe,f,o and
- U. Joseph Schoepf, MDa,m,∗ ()
- aDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
- bDepartment of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany
- cDepartment of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany
- dAdvanced Therapies Innovation Department, Siemens Healthcare K.K., Tokyo, Japan
- eDepartment of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- fDepartment of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- gDepartment of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, CMIV, Linköping University, Linköping, Sweden
- hCoronary Disease and Structural Heart Diseases Department, Invasive Cardiology and Angiology Department, Institute of Cardiology, Warsaw, Poland
- iDepartment of Cardiology, Heart Institute Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
- jDepartment of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
- kFirst Department of Medicine, Faculty of Medicine Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, Mannheim, Germany
- lDepartment of Cardiology, Kerckhoff Heart Center, Bad Nauheim, Germany
- mDivision of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina
- nDepartment of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
- oCardiovascular Institute, Stanford University School of Medicine, Stanford, California
- ↵∗Address for correspondence:
Dr. U. Joseph Schoepf, Heart & Vascular Center, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, South Carolina 29425-2260.
Objectives This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning–based coronary computed tomography (CT) angiography (cCTA)–derived fractional flow reserve (CT-FFR).
Background CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.
Methods Four hundred eighty-two vessels from 314 patients (62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard.
Results The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥ 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57-0.85] vs. 0.85 [95% CI: 0.82-0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).
Conclusions Machine-learning–based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).
- coronary artery disease
- coronary computed tomography angiography
- computational fractional flow reserve
- invasive coronary angiography
Dr. Otani is an employee of Siemens Healthcare, Japan. Dr. De Cecco has received personal fees from Siemens and Bayer. Dr. Albrecht has received personal fees from Siemens and Bracco. Dr. Varga-Szemes has received personal fees from Siemens and Guerbet. Dr. Steinberg has received personal fees from Boston Scientific, Medtronic, Terumo, Abbott, and Edwards. Dr. Nieman has received personal fees from Siemens Healthineers, Bayer, GE, and Heartflow. Dr. Schoepf has received grants from Astellas, Bayer, GE, Medrad, and Siemens; and has recieved personal fees from Bayer, Euclid BioImaging, Siemens, and Heartflow, Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received December 4, 2018.
- Revision received June 10, 2019.
- Accepted June 19, 2019.
- 2019 American College of Cardiology Foundation
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