Literature DB >> 29409717

Comparison of invasively measured FFR with FFR derived from coronary CT angiography for detection of lesion-specific ischemia: Results from a PC-based prototype algorithm.

Jens Röther1, Maximilian Moshage2, Damini Dey3, Chris Schwemmer4, Monique Tröbs2, Florian Blachutzik2, Stephan Achenbach2, Christian Schlundt2, Mohamed Marwan2.   

Abstract

BACKGROUND: We evaluated the diagnostic accuracy of a novel prototype for on-site determination of CT-based FFR (cFFR) on a standard personal computer (PC) compared to invasively measured FFR in patients with suspected coronary artery disease.
METHODS: A total of 91 vessels in 71 patients (mean age 65 ± 9 years) in whom coronary CT angiography had been performed due to suspicion of coronary artery disease, and who subsequently underwent invasive coronary angiography with FFR measurement were analyzed. For both cFFR and FFR, a threshold of ≤0.80 was used to indicate a hemodynamically relevant stenosis. The mean time needed to calculate cFFR was 12.4 ± 3.4 min. A very close correlation between cFFR and FFR could be shown (r = 0.85; p < 0.0001) with Bland-Altman analysis showing moderate agreement between FFR and cFFR with mild systematic overestimation of FFR values in CT (mean difference 0.0049, 95% limits of agreement ±2SD -0.007 to 0.008). Compared to FFR, the sensitivity of cFFR to detect hemodynamically significant lesions was 91% (19/21, 95% CI: 70%-99%), specificity was 96% (67/70, 95% CI: 88%-99%), positive predictive value 86% (95% CI: 65%-97%) and negative predictive value was 97% (95% CI: 90%-100%) with an accuracy of 93%.
CONCLUSION: cFFR obtained using an on-site algorithm implemented on a standard PC shows high diagnostic accuracy to detect lesions causing ischemia as compared to FFR. Importantly, the time needed for analysis is short which may be useful for improving clinical workflow.
Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational fluid dynamics; Coronary artery disease; Coronary computed tomography angiography; Fractional flow reserve; cFFR

Mesh:

Year:  2018        PMID: 29409717     DOI: 10.1016/j.jcct.2018.01.012

Source DB:  PubMed          Journal:  J Cardiovasc Comput Tomogr        ISSN: 1876-861X


  8 in total

1.  Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR.

Authors:  Xiuhua Hu; Minglei Yang; Lu Han; Yujiao Du
Journal:  Int J Cardiovasc Imaging       Date:  2018-07-30       Impact factor: 2.357

2.  Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis.

Authors:  Baiyan Zhuang; Shuli Wang; Shihua Zhao; Minjie Lu
Journal:  Eur Radiol       Date:  2019-11-06       Impact factor: 5.315

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Journal:  Eur Radiol       Date:  2022-06-22       Impact factor: 5.315

4.  Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve in Patients with Anomalous Origin of the Right Coronary Artery from the Left Coronary Sinus.

Authors:  Chun Xiang Tang; Meng Jie Lu; Joseph Uwe Schoepf; Christian Tesche; Maximilian Bauer; John Nance; Parkwood Griffith; Guang Ming Lu; Long Jiang Zhang
Journal:  Korean J Radiol       Date:  2020-02       Impact factor: 3.500

Review 5.  Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.

Authors:  Nitesh Gautam; Prachi Saluja; Abdallah Malkawi; Mark G Rabbat; Mouaz H Al-Mallah; Gianluca Pontone; Yiye Zhang; Benjamin C Lee; Subhi J Al'Aref
Journal:  Healthcare (Basel)       Date:  2022-01-26

6.  Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve.

Authors:  Yushui Han; Ahmed Ibrahim Ahmed; Chris Schwemmer; Myra Cocker; Talal S Alnabelsi; Jean Michel Saad; Juan C Ramirez Giraldo; Mouaz H Al-Mallah
Journal:  Open Heart       Date:  2022-03

7.  2021 Top 10 Articles in the Arquivos Brasileiros de Cardiologia and the Revista Portuguesa de Cardiologia.

Authors:  Ricardo Fontes-Carvalho; Gláucia Maria Moraes de Oliveira; Pedro Gonçalves-Teixeira; Carlos Eduardo Rochitte; Nuno Cardim
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8.  Diagnostic Performance of a Machine Learning-Based CT-Derived FFR in Detecting Flow-Limiting Stenosis.

Authors:  Thamara Carvalho Morais; Antonildes Nascimento Assunção-Jr; Roberto Nery Dantas Júnior; Carla Franco Grego da Silva; Caroline Bastida de Paula; Roberto Almeida Torres; Tiago Augusto Magalhães; César Higa Nomura; Luiz Francisco Rodrigues de Ávila; José Rodrigues Parga Filho
Journal:  Arq Bras Cardiol       Date:  2021-06       Impact factor: 2.000

  8 in total

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