Literature DB >> 29103607

Experience With an On-Site Coronary Computed Tomography-Derived Fractional Flow Reserve Algorithm for the Assessment of Intermediate Coronary Stenoses.

Patrick M Donnelly1, Márton Kolossváry2, Júlia Karády2, Peter A Ball1, Stephanie Kelly1, Donna Fitzsimons1, Mark S Spence1, Csilla Celeng2, Tamás Horváth3, Bálint Szilveszter2, Hendrik W van Es4, Martin J Swaans4, Béla Merkely2, Pál Maurovich-Horvat5.   

Abstract

Fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) is a new technique for the diagnosis of ischemic coronary artery stenoses. The aim of this prospective study was to evaluate the diagnostic performance of a novel on-site computed tomography-based fractional flow reserve algorithm (CT-FFR) compared with invasive FFR as the gold standard, and to determine whether its diagnostic performance is affected by interobserver variations in lumen segmentation. We enrolled 44 consecutive patients (64.6 ± 8.9 years, 34% female) with 60 coronary atherosclerotic lesions who underwent coronary CTA and invasive coronary angiography in 2 centers. An FFR value ≤0.8 was considered significant. Coronary CTA scans were evaluated by 2 expert readers, who manually adjusted the semiautomated coronary lumen segmentations for effective diameter stenosis (EDS) assessment and on-site CT-FFR simulation. The mean CT-FFR value was 0.77 ± 0.15, whereas the mean EDS was 43.6 ± 16.9%. The sensitivity, specificity, positive predictive value, and negative predictive value of CT-FFR versus EDS with a cutoff of 50% were the following: 91%, 72%, 63%, and 93% versus 52%, 87%, 69%, and 77%, respectively. The on-site CT-FFR demonstrated significantly better diagnostic performance compared with EDS (area under the curve 0.89 vs 0.74, respectively, p <0.001). The CT-FFR areas under the curve of the 2 readers did not show any significant difference (0.89 vs 0.88, p = 0.74). In conclusion, on-site CT-FFR simulation is feasible and has better diagnostic performance than anatomic stenosis assessment. Furthermore, the diagnostic performance of the on-site CT-FFR simulation algorithm does not depend on the readers' semiautomated lumen segmentation adjustments.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 29103607     DOI: 10.1016/j.amjcard.2017.09.018

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  9 in total

1.  Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience.

Authors:  Matthias Eberhard; Tin Nadarevic; Andrej Cousin; Jochen von Spiczak; Ricarda Hinzpeter; Andre Euler; Fabian Morsbach; Robert Manka; Dagmar I Keller; Hatem Alkadhi
Journal:  Cardiovasc Diagn Ther       Date:  2020-08

Review 2.  Functional cardiac CT-Going beyond Anatomical Evaluation of Coronary Artery Disease with Cine CT, CT-FFR, CT Perfusion and Machine Learning.

Authors:  Joyce Peper; Dominika Suchá; Martin Swaans; Tim Leiner
Journal:  Br J Radiol       Date:  2020-08-12       Impact factor: 3.039

3.  Diagnostic Performance of On-Site Coronary CT Angiography-derived Fractional Flow Reserve Based on Patient-specific Lumped Parameter Models.

Authors:  Robbert W van Hamersvelt; Michiel Voskuil; Pim A de Jong; Martin J Willemink; Ivana Išgum; Tim Leiner
Journal:  Radiol Cardiothorac Imaging       Date:  2019-10-31

4.  Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis.

Authors:  Kanako K Kumamaru; Erin Angel; Kelsey N Sommer; Vijay Iyer; Michael F Wilson; Nikhil Agrawal; Aishwarya Bhardwaj; Sharma B Kattel; Sandra Kondziela; Saurabh Malhotra; Christopher Manion; Katherine Pogorzelski; Tharmathai Ramanan; Abhishek C Sawant; Mary M Suplicki; Sameer Waheed; Shinichiro Fujimoto; Umesh C Sharma; Frank J Rybicki; Ciprian N Ionita
Journal:  Radiol Cardiothorac Imaging       Date:  2019-08-29

Review 5.  [Beyond Coronary CT Angiography: CT Fractional Flow Reserve and Perfusion].

Authors:  Moon Young Kim; Dong Hyun Yang; Ki Seok Choo; Whal Lee
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2022-01-21

6.  Application of speCtraL computed tomogrAphy to impRove specIficity of cardiac compuTed tomographY (CLARITY study): rationale and design.

Authors:  Robbert Willem van Hamersvelt; Ivana Išgum; Pim A de Jong; Maarten Jan Maria Cramer; Geert E H Leenders; Martin J Willemink; Michiel Voskuil; Tim Leiner
Journal:  BMJ Open       Date:  2019-03-01       Impact factor: 2.692

Review 7.  Computed tomographic evaluation of myocardial ischemia.

Authors:  Yuki Tanabe; Akira Kurata; Takuya Matsuda; Kazuki Yoshida; Dhiraj Baruah; Teruhito Kido; Teruhito Mochizuki; Prabhakar Rajiah
Journal:  Jpn J Radiol       Date:  2020-02-05       Impact factor: 2.374

Review 8.  Fractional Flow Reserve: Patient Selection and Perspectives.

Authors:  Joyce Peper; Leonie M Becker; Jan-Peter van Kuijk; Tim Leiner; Martin J Swaans
Journal:  Vasc Health Risk Manag       Date:  2021-12-14

9.  Diagnostic accuracy of on-site coronary computed tomography-derived fractional flow reserve in the diagnosis of stable coronary artery disease.

Authors:  J Peper; J Schaap; B J W M Rensing; J C Kelder; M J Swaans
Journal:  Neth Heart J       Date:  2021-12-15       Impact factor: 2.380

  9 in total

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