Literature DB >> 31335283

Determinants of Rejection Rate for Coronary CT Angiography Fractional Flow Reserve Analysis.

Gianluca Pontone1, Jonathan R Weir-McCall1, Andrea Baggiano1, Alberico Del Torto1, Laura Fusini1, Marco Guglielmo1, Giuseppe Muscogiuri1, Andrea Igoren Guaricci1, Daniele Andreini1, Manesh Patel1, Koen Nieman1, Takashi Akasaka1, Campbell Rogers1, Bjarne L Nørgaard1, Jeroen Bax1, Gilbert L Raff1, Kavitha Chinnaiyan1, Daniel Berman1, Timothy Fairbairn1, Lynne Hurwitz Koweek1, Jonathon Leipsic1.   

Abstract

Background Coronary artery fractional flow reserve (FFR) derived from CT angiography (FFTCT) enables functional assessment of coronary stenosis. Prior clinical trials showed 13%-33% of coronary CT angiography studies had insufficient quality for quantitative analysis with FFRCT. Purpose To determine the rejection rate of FFRCT analysis and to determine factors associated with technically unsuccessful calculation of FFRCT. Materials and Methods Prospectively acquired coronary CT angiography scans submitted as part of the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care (ADVANCE) registry (https://ClinicalTrials.gov: NCT02499679) and coronary CT angiography series submitted for clinical analysis were included. The primary outcome was the FFRCT rejection rate (defined as an inability to perform quantitative analysis with FFRCT). Factors that were associated with FFRCT rejection rate were assessed with multiple linear regression. Results In the ADVANCE registry, FFRCT rejection rate due to inadequate image quality was 2.9% (80 of 2778 patients; 95% confidence interval [CI]: 2.1%, 3.2%). In the 10 621 consecutive patients who underwent clinical analysis, the FFRCT rejection rate was 8.4% (n = 892; 95% CI: 6.2%, 7.2%; P < .001 vs the ADVANCE cohort). The main reason for the inability to perform FFRCT analysis was the presence of motion artifacts (63 of 80 [78%] and 729 of 892 [64%] in the ADVANCE and clinical cohorts, respectively). At multivariable analysis, section thickness in the ADVANCE (odds ratio [OR], 1.04; 95% CI: 1.001, 1.09; P = .045) and clinical (OR, 1.03; 95% CI: 1.02, 1.04; P < .001) cohorts and heart rate in the ADVANCE (OR, 1.05; 95% CI: 1.02, 1.08; P < .001) and clinical (OR, 1.06; 95% CI: 1.05, 1.07; P < .001) cohorts were independent predictors of rejection. Conclusion The rates for technically unsuccessful CT-derived fractional flow reserve in the ADVANCE registry and in a large clinical cohort were 2.9% and 8.4%, respectively. Thinner CT section thickness and lower patient heart rate may increase rates of completion of CT fractional flow reserve analysis. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sakuma in this issue.

Entities:  

Year:  2019        PMID: 31335283     DOI: 10.1148/radiol.2019182673

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  11 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.  [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

3.  Clinical applications of cardiac computed tomography: a consensus paper of the European Association of Cardiovascular Imaging-part II.

Authors:  Gianluca Pontone; Alexia Rossi; Marco Guglielmo; Marc R Dweck; Oliver Gaemperli; Koen Nieman; Francesca Pugliese; Pal Maurovich-Horvat; Alessia Gimelli; Bernard Cosyns; Stephan Achenbach
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2022-03-22       Impact factor: 9.130

4.  Machine Learning CT FFR: The Evolving Role of On-Site Techniques.

Authors:  Abdul Rahman Ihdayhid; Sagit Ben Zekry
Journal:  Radiol Cardiothorac Imaging       Date:  2020-06-25

Review 5.  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 6.  Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey.

Authors:  Nils Hampe; Jelmer M Wolterink; Sanne G M van Velzen; Tim Leiner; Ivana Išgum
Journal:  Front Cardiovasc Med       Date:  2019-11-26

Review 7.  Invasive and non-invasive assessment of ischaemia in chronic coronary syndromes: translating pathophysiology to clinical practice.

Authors:  Ozan M Demir; Haseeb Rahman; Tim P van de Hoef; Javier Escaned; Jan J Piek; Sven Plein; Divaka Perera
Journal:  Eur Heart J       Date:  2022-01-13       Impact factor: 29.983

8.  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

Review 9.  Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction.

Authors:  Alberico Del Torto; Andrea Igoren Guaricci; Francesca Pomarico; Marco Guglielmo; Laura Fusini; Francesco Monitillo; Daniela Santoro; Monica Vannini; Alexia Rossi; Giuseppe Muscogiuri; Andrea Baggiano; Gianluca Pontone
Journal:  Front Cardiovasc Med       Date:  2022-03-09

10.  Improving CT-Derived Fractional Flow Reserve Analysis: A Quality Improvement Initiative.

Authors:  Jeffrey Waltz; Madison Kocher; Jacob Kahn; Rebecca Leddy; Jordan H Chamberlin; Daniel Cook; Jeremy R Burt
Journal:  Cureus       Date:  2020-10-07
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