BACKGROUND: Coronary artery disease is common in patients with severe aortic stenosis. Computed tomography-derived fractional flow reserve (CT-FFR) is a clinically used modality for assessing coronary artery disease, however, its use has not been validated in patients with severe aortic stenosis. This study assesses the safety, feasibility, and validity of CT-FFR in patients with severe aortic stenosis. METHODS: Prospectively recruited patients underwent standard-protocol invasive FFR and coronary CT angiography (CTA). CTA images were analyzed by central core laboratory (HeartFlow, Inc) for independent evaluation of CT-FFR. CT-FFR data were compared with FFR (ischemia defined as FFR ≤0.80). RESULTS: Forty-two patients (68 vessels) underwent FFR and CTA; 39 patients (92.3%) and 60 vessels (88.2%) had interpretable CTA enabling CT-FFR computation. Mean age was 76.2±6.7 years (71.8% male). No patients incurred complications relating to premedication, CTA, or FFR protocol. Mean FFR and CT-FFR were 0.83±0.10 and 0.77±0.14, respectively. CT calcium score was 1373.3±1392.9 Agatston units. On per vessel analysis, there was positive correlation between FFR and CT-FFR (Pearson correlation coefficient, R=0.64, P<0.0001). Sensitivity, specificity, positive predictive value, and negative predictive values were 73.9%, 78.4%, 68.0%, and 82.9%, respectively, with 76.7% diagnostic accuracy. The area under the receiver-operating characteristic curve for CT-FFR was 0.83 (0.72-0.93, P<0.0001), which was higher than that of CTA and quantitative coronary angiography (P=0.01 and P<0.001, respectively). Bland-Altman plot showed mean bias between FFR and CT-FFR as 0.059±0.110. On per patient analysis, the sensitivity, specificity, positive predictive, and negative predictive values were 76.5%, 77.3%, 72.2%, and 81.0% with 76.9% diagnostic accuracy. The per patient area under the receiver-operating characteristic curve analysis was 0.81 (0.67-0.95, P<0.0001). CONCLUSIONS: CT-FFR is safe and feasible in patients with severe aortic stenosis. Our data suggests that the diagnostic accuracy of CT-FFR in this cohort potentially enables its use in clinical practice and provides the foundation for future research into the use of CT-FFR for coronary evaluation pre-aortic valve replacement.
BACKGROUND:Coronary artery disease is common in patients with severe aortic stenosis. Computed tomography-derived fractional flow reserve (CT-FFR) is a clinically used modality for assessing coronary artery disease, however, its use has not been validated in patients with severe aortic stenosis. This study assesses the safety, feasibility, and validity of CT-FFR in patients with severe aortic stenosis. METHODS: Prospectively recruited patients underwent standard-protocol invasive FFR and coronary CT angiography (CTA). CTA images were analyzed by central core laboratory (HeartFlow, Inc) for independent evaluation of CT-FFR. CT-FFR data were compared with FFR (ischemia defined as FFR ≤0.80). RESULTS: Forty-two patients (68 vessels) underwent FFR and CTA; 39 patients (92.3%) and 60 vessels (88.2%) had interpretable CTA enabling CT-FFR computation. Mean age was 76.2±6.7 years (71.8% male). No patients incurred complications relating to premedication, CTA, or FFR protocol. Mean FFR and CT-FFR were 0.83±0.10 and 0.77±0.14, respectively. CT calcium score was 1373.3±1392.9 Agatston units. On per vessel analysis, there was positive correlation between FFR and CT-FFR (Pearson correlation coefficient, R=0.64, P<0.0001). Sensitivity, specificity, positive predictive value, and negative predictive values were 73.9%, 78.4%, 68.0%, and 82.9%, respectively, with 76.7% diagnostic accuracy. The area under the receiver-operating characteristic curve for CT-FFR was 0.83 (0.72-0.93, P<0.0001), which was higher than that of CTA and quantitative coronary angiography (P=0.01 and P<0.001, respectively). Bland-Altman plot showed mean bias between FFR and CT-FFR as 0.059±0.110. On per patient analysis, the sensitivity, specificity, positive predictive, and negative predictive values were 76.5%, 77.3%, 72.2%, and 81.0% with 76.9% diagnostic accuracy. The per patient area under the receiver-operating characteristic curve analysis was 0.81 (0.67-0.95, P<0.0001). CONCLUSIONS: CT-FFR is safe and feasible in patients with severe aortic stenosis. Our data suggests that the diagnostic accuracy of CT-FFR in this cohort potentially enables its use in clinical practice and provides the foundation for future research into the use of CT-FFR for coronary evaluation pre-aortic valve replacement.
Authors: Suhny Abbara; Philipp Blanke; Christopher D Maroules; Michael Cheezum; Andrew D Choi; B Kelly Han; Mohamed Marwan; Chris Naoum; Bjarne L Norgaard; Ronen Rubinshtein; Paul Schoenhagen; Todd Villines; Jonathon Leipsic Journal: J Cardiovasc Comput Tomogr Date: 2016-10-12
Authors: Alexia Rossi; Carlo N De Cecco; Simon R O Kennon; Lu Zou; Felix G Meinel; William Toscano; Sabrina Segreto; Stephan Achenbach; Jörg Hausleiter; U Joseph Schoepf; Francesca Pugliese Journal: J Cardiovasc Comput Tomogr Date: 2017-06-22
Authors: Gilbert L Raff; Kavitha M Chinnaiyan; Ricardo C Cury; Mario T Garcia; Harvey S Hecht; Judd E Hollander; Brian O'Neil; Allen J Taylor; Udo Hoffmann Journal: J Cardiovasc Comput Tomogr Date: 2014-06-12
Authors: Michael Michail; Hakim-Moulay Dehbi; Nitesh Nerlekar; Justin E Davies; Andrew S P Sharp; Suneel Talwar; James D Cameron; Adam J Brown; Dennis T Wong; Anthony Mathur; Alun D Hughes; Om Narayan Journal: Catheter Cardiovasc Interv Date: 2019-01-02 Impact factor: 2.692
Authors: Bjarne L Nørgaard; Jonathon Leipsic; Sara Gaur; Sujith Seneviratne; Brian S Ko; Hiroshi Ito; Jesper M Jensen; Laura Mauri; Bernard De Bruyne; Hiram Bezerra; Kazuhiro Osawa; Mohamed Marwan; Christoph Naber; Andrejs Erglis; Seung-Jung Park; Evald H Christiansen; Anne Kaltoft; Jens F Lassen; Hans Erik Bøtker; Stephan Achenbach Journal: J Am Coll Cardiol Date: 2014-01-30 Impact factor: 24.094
Authors: Cameron Dowling; Michael Michail; Jun Michael Zhang; Andrea Comella; Udit Thakur; Robert Gooley; Liam McCormick; Adam J Brown; Dennis T L Wong Journal: Cardiovasc Diagn Ther Date: 2022-06
Authors: Robin Fabian Gohmann; Patrick Seitz; Konrad Pawelka; Nicolas Majunke; Adrian Schug; Linda Heiser; Katharina Renatus; Steffen Desch; Philipp Lauten; David Holzhey; Thilo Noack; Johannes Wilde; Philipp Kiefer; Christian Krieghoff; Christian Lücke; Sebastian Ebel; Sebastian Gottschling; Michael A Borger; Holger Thiele; Christoph Panknin; Mohamed Abdel-Wahab; Matthias Horn; Matthias Gutberlet Journal: J Clin Med Date: 2022-02-28 Impact factor: 4.241