Christian Tesche1, Katharina Otani2, Carlo N De Cecco3, Adriaan Coenen4, Jakob De Geer5, Mariusz Kruk6, Young-Hak Kim7, Moritz H Albrecht8, Stefan Baumann9, Matthias Renker10, Richard R Bayer11, Taylor M Duguay3, Sheldon E Litwin11, Akos Varga-Szemes3, Daniel H Steinberg12, Dong Hyun Yang13, Cezary Kepka6, Anders Persson5, Koen Nieman14, U Joseph Schoepf15. 1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany; Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany. 2. Advanced Therapies Innovation Department, Siemens Healthcare K.K., Tokyo, Japan. 3. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina. 4. Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands. 5. Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, CMIV, Linköping University, Linköping, Sweden. 6. Coronary Disease and Structural Heart Diseases Department, Invasive Cardiology and Angiology Department, Institute of Cardiology, Warsaw, Poland. 7. Department of Cardiology, Heart Institute Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 8. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. 9. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; First Department of Medicine, Faculty of Medicine Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, Mannheim, Germany. 10. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Department of Cardiology, Kerckhoff Heart Center, Bad Nauheim, Germany. 11. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina. 12. Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina. 13. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 14. Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Cardiovascular Institute, Stanford University School of Medicine, Stanford, California. 15. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina. Electronic address: schoepf@musc.edu.
Abstract
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: A total of 482 vessels from 314 patients (age 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 to 0.85] vs. 0.85 [95% CI: 0.82 to 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).
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: A total of 482 vessels from 314 patients (age 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 to 0.85] vs. 0.85 [95% CI: 0.82 to 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).
Authors: Jonathan James Hyett Bray; Moghees Ahmad Hanif; Mohammad Alradhawi; Jacob Ibbetson; Surinder Singh Dosanjh; Sabrina Lucy Smith; Mahmood Ahmad; Dominic Pimenta Journal: Eur Heart J Open Date: 2022-03-17
Authors: C R Aditya; Naveen Chakravarthy Sattaru; Kumaraguruparan Gopal; R Rahul; G Chandra Shekara; Omaima Nasif; Sulaiman Ali Alharbi; S S Raghavan; S Arockia Jayadhas Journal: Biomed Res Int Date: 2022-06-23 Impact factor: 3.246
Authors: Andrew Lin; Márton Kolossváry; Manish Motwani; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey Journal: J Cardiovasc Comput Tomogr Date: 2021-03-22