Literature DB >> 26747231

Coronary CT angiography derived fractional flow reserve: Methodology and evaluation of a point of care algorithm.

Adriaan Coenen1, Marisa M Lubbers2, Akira Kurata3, Atsushi Kono3, Admir Dedic2, Raluca G Chelu2, Marcel L Dijkshoorn3, Robert-Jan M van Geuns2, Max Schoebinger4, Lucian Itu5, Puneet Sharma6, Koen Nieman2.   

Abstract

BACKGROUND: Recently several publications described the diagnostic value of coronary CT angiography (coronary CTA) derived fractional flow reserve (CTA-FFR). For a recently introduced on-site CTA-FFR application, detailed methodology and factors potentially affecting performance have not yet been described.
OBJECTIVE: To provide a methodological background for an on-site CTA-FFR application and evaluate the effect of patient and acquisition characteristics.
METHODS: The on-site CTA-FFR application utilized a reduced-order hybrid model applying pressure drop models within stenotic regions. In 116 patients and 203 vessels the diagnostic performance of CTA-FFR was investigated using invasive FFR measurements as a reference. The effect of several potentially relevant factors on CTA-FFR was investigated.
RESULTS: 90 vessels (44%) had a hemodynamically relevant stenosis according to invasive FFR (threshold ≤0.80). The overall vessel-based sensitivity, specificity and accuracy of CTA-FFR were 88% (CI 95%:79-94%), 65% (55-73%) and 75% (69-81%). The specificity was significantly lower in the presence of misalignment artifacts (25%, CI: 6-57%). A non-significant reduction in specificity from 74% (60-85%) to 48% (26-70%) was found for higher coronary artery calcium scores. Left ventricular mass, diabetes mellitus and large vessel size increased the discrepancy between invasive FFR and CTA-FFR values.
CONCLUSIONS: On-site calculation of CTA-FFR can identify hemodynamically significant CAD with an overall per-vessel accuracy of 75% in comparison to invasive FFR. The diagnostic performance of CTA-FFR is negatively affected by misalignment artifacts. CTA-FFR is potentially affected by left ventricular mass, diabetes mellitus and vessel size.
Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational fluid dynamics; Coronary CT angiography; Diagnostic performance; Fractional flow reserve

Mesh:

Year:  2015        PMID: 26747231     DOI: 10.1016/j.jcct.2015.12.006

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


  12 in total

1.  Hybrid anatomo-functional imaging of coronary artery disease: Beneficial irrespective of its core components.

Authors:  Francesco Nudi; Giuseppe Biondi-Zoccai; Andrea Romagnoli; Orazio Schillaci; Alessandro Nudi; Francesco Versaci
Journal:  J Nucl Cardiol       Date:  2018-12-18       Impact factor: 5.952

2.  In Reply.

Authors:  Monique Tröbs
Journal:  Dtsch Arztebl Int       Date:  2019-06-28       Impact factor: 5.594

Review 3.  Plaque imaging with CT-a comprehensive review on coronary CT angiography based risk assessment.

Authors:  Márton Kolossváry; Bálint Szilveszter; Béla Merkely; Pál Maurovich-Horvat
Journal:  Cardiovasc Diagn Ther       Date:  2017-10

4.  Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study.

Authors:  Yi Xue; Min Wen Zheng; Yang Hou; Fan Zhou; Jian Hua Li; Yi Ning Wang; Chun Yu Liu; Chang Sheng Zhou; Jia Yin Zhang; Meng Meng Yu; Bo Zhang; Dai Min Zhang; Yan Yi; Lei Xu; Xiu Hua Hu; Guang Ming Lu; Chun Xiang Tang; Long Jiang Zhang
Journal:  Eur Radiol       Date:  2022-01-12       Impact factor: 5.315

5.  The effect of blood pressure on non-invasive fractional flow reserve derived from coronary computed tomography angiography.

Authors:  Akira Kurata; Adriaan Coenen; Marisa M Lubbers; Koen Nieman; Teruhito Kido; Tomoyuki Kido; Natsumi Yamashita; Kouki Watanabe; Gabriel P Krestin; Teruhito Mochizuki
Journal:  Eur Radiol       Date:  2016-08-19       Impact factor: 5.315

6.  Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

Authors:  Fay M A Nous; Ricardo P J Budde; Marisa M Lubbers; Yuzo Yamasaki; Isabella Kardys; Tobias A Bruning; Jurgen M Akkerhuis; Marcel J M Kofflard; Bas Kietselaer; Tjebbe W Galema; Koen Nieman
Journal:  Eur Radiol       Date:  2020-03-12       Impact factor: 5.315

7.  A study of noninvasive fractional flow reserve derived from a simplified method based on coronary computed tomography angiography in suspected coronary artery disease.

Authors:  Changzheng Shi; Dong Zhang; Kunlin Cao; Tao Zhang; Liangping Luo; Xin Liu; Heye Zhang
Journal:  Biomed Eng Online       Date:  2017-04-14       Impact factor: 2.819

8.  The predictive factors affecting false positive in on-site operated CT-fractional flow reserve based on fluid and structural interaction.

Authors:  Yuko O Kawaguchi; Shinichiro Fujimoto; Kanako K Kumamaru; Etsuro Kato; Tomotaka Dohi; Kazuhisa Takamura; Chihiro Aoshima; Yuki Kamo; Yoshiteru Kato; Makoto Hiki; Iwao Okai; Shinya Okazaki; Shigeki Aoki; Hiroyuki Daida
Journal:  Int J Cardiol Heart Vasc       Date:  2019-05-11

9.  Fractional Flow Reserve Estimated at Coronary CT Angiography in Intermediate Lesions: Comparison of Diagnostic Accuracy of Different Methods to Determine Coronary Flow Distribution.

Authors:  Satoru Kishi; Andreas A Giannopoulos; Anji Tang; Nahoko Kato; Yiannis S Chatzizisis; Carole Dennie; Yu Horiuchi; Kengo Tanabe; João A C Lima; Frank J Rybicki; Dimitris Mitsouras
Journal:  Radiology       Date:  2017-11-20       Impact factor: 29.146

10.  Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve.

Authors:  Wenbing Jiang; Yibin Pan; Yumeng Hu; Xiaochang Leng; Jun Jiang; Li Feng; Yongqing Xia; Yong Sun; Jian'an Wang; Jianping Xiang; Changling Li
Journal:  Biomed Eng Online       Date:  2021-08-04       Impact factor: 2.819

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