Stefan Baumann1, Matthias Renker2, U Joseph Schoepf3, Carlo N De Cecco4, Adriaan Coenen5, Jakob De Geer6, Mariusz Kruk7, Young-Hak Kim8, Moritz H Albrecht9, Taylor M Duguay4, Brian E Jacobs4, Richard R Bayer10, Sheldon E Litwin10, Christel Weiss11, Ibrahim Akin12, Martin Borggrefe12, Dong Hyun Yang13, Cezary Kepka7, Anders Persson6, Koen Nieman14, Christian Tesche15. 1. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany. 2. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Kerckhoff Heart and Thorax Center, Department of Cardiology, Bad Nauheim, Germany. 3. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA. Electronic address: schoepf@musc.edu. 4. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA. 5. Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands. 6. Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, CMIV, Linköping University, Linköping, Sweden. 7. Coronary Disease and Structural Heart Diseases Department, Invasive Cardiology and Angiology Department, Institute of Cardiology, Warsaw, Poland. 8. Department of Cardiology, Heart Institute Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 9. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. 10. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA. 11. Medical Faculty Mannheim, Department of Medical Statistics and Biomathematics, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany. 12. First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany. 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, CA, USA. 15. Heart & Vascular Center, Medical University of South Carolina, Charleston, SC, USA; Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany; Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany.
Abstract
PURPOSE: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia. METHOD: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. RESULTS: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. CONCLUSIONS: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
PURPOSE: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia. METHOD: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. RESULTS: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. CONCLUSIONS: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
Authors: Nidaa Mikail; Alexia Rossi; Susan Bengs; Ahmed Haider; Barbara E Stähli; Angela Portmann; Alessio Imperiale; Valerie Treyer; Alexander Meisel; Aju P Pazhenkottil; Michael Messerli; Vera Regitz-Zagrosek; Philipp A Kaufmann; Ronny R Buechel; Cathérine Gebhard Journal: Eur J Nucl Med Mol Imaging Date: 2022-08-17 Impact factor: 10.057
Authors: Giuseppe Muscogiuri; Marly Van Assen; Christian Tesche; Carlo N De Cecco; Mattia Chiesa; Stefano Scafuri; Marco Guglielmo; Andrea Baggiano; Laura Fusini; Andrea I Guaricci; Mark G Rabbat; Gianluca Pontone Journal: Biomed Res Int Date: 2020-12-16 Impact factor: 3.411