Literature DB >> 29156145

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

Satoru Kishi1, Andreas A Giannopoulos1, Anji Tang1, Nahoko Kato1, Yiannis S Chatzizisis1, Carole Dennie1, Yu Horiuchi1, Kengo Tanabe1, João A C Lima1, Frank J Rybicki1, Dimitris Mitsouras1.   

Abstract

Purpose To compare the diagnostic accuracy of different computed tomographic (CT) fractional flow reserve (FFR) algorithms for vessels with intermediate stenosis. Materials and Methods This cross-sectional HIPAA-compliant and human research committee-approved study applied a four-step CT FFR algorithm in 61 patients (mean age, 69 years ± 10; age range, 29-89 years) with a lesion of intermediate-diameter stenosis (25%-69%) at CT angiography who underwent FFR measurement within 90 days. The per-lesion diagnostic performance of CT FFR was tested for three different approaches to estimate blood flow distribution for CT FFR calculation. The first two, the Murray law and the Huo-Kassab rule, used coronary anatomy; the third used contrast material opacification gradients. CT FFR algorithms and CT angiography percentage diameter stenosis (DS) measurements were compared by using the area under the receiver operating characteristic curve (AUC) to detect FFRs of 0.8 or lower. Results Twenty-five lesions (41%) had FFRs of 0.8 or lower. The AUC of CT FFR determination by using contrast material gradients (AUC = 0.953) was significantly higher than that of the Huo-Kassab (AUC = 0.882, P = .043) and Murray law models (AUC = 0.871, P = .033). All three AUCs were higher than that for 50% or greater DS at CT angiography (AUC = 0.596, P < .001). Correlation of CT FFR with FFR was highest for gradients (Spearman ρ = 0.80), followed by the Huo-Kassab rule (ρ = 0.68) and Murray law (ρ = 0.67) models. All CT FFR algorithms had small biases, ranging from -0.015 (Murray) to -0.049 (Huo-Kassab). Limits of agreement were narrowest for gradients (-0.182, 0.147), followed by the Huo-Kassab rule (-0.246, 0.149) and the Murray law (-0.285, 0.256) models. Conclusion Clinicians can perform CT FFR by using a four-step approach on site to accurately detect hemodynamically significant intermediate-stenosis lesions. Estimating blood flow distribution by using coronary contrast opacification variations may improve CT FFR accuracy. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 29156145      PMCID: PMC5896162          DOI: 10.1148/radiol.2017162620

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   29.146


  32 in total

1.  Quantification of arterial plaque and lumen density with MDCT.

Authors:  Narinder S Paul; Joerg Blobel; Hany Kashani; Murray Rice; Ali Ursani
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

Review 2.  Expert consensus statement on the use of fractional flow reserve, intravascular ultrasound, and optical coherence tomography: a consensus statement of the Society of Cardiovascular Angiography and Interventions.

Authors:  Amir Lotfi; Allen Jeremias; William F Fearon; Marc D Feldman; Roxana Mehran; John C Messenger; Cindy L Grines; Larry S Dean; Morton J Kern; Lloyd W Klein
Journal:  Catheter Cardiovasc Interv       Date:  2013-11-13       Impact factor: 2.692

Review 3.  Does coronary flow trump coronary anatomy?

Authors:  K Lance Gould
Journal:  JACC Cardiovasc Imaging       Date:  2009-08

4.  Relative atherosclerotic plaque volume by CT coronary angiography trumps conventional stenosis assessment for identifying flow-limiting lesions.

Authors:  Nahoko Kato; Satoru Kishi; Armin Arbab-Zadeh; Frank J Rybicki; Shuzou Tanimoto; Jiro Aoki; Mika Watanabe; Yu Horiuchi; Koichi Furui; Kazuhiro Hara; Kenji Ibukuro; Joao A C Lima; Kengo Tanabe
Journal:  Int J Cardiovasc Imaging       Date:  2017-06-08       Impact factor: 2.357

5.  Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study.

Authors:  Bon-Kwon Koo; Andrejs Erglis; Joon-Hyung Doh; David V Daniels; Sanda Jegere; Hyo-Soo Kim; Allison Dunning; Tony DeFrance; Alexandra Lansky; Jonathan Leipsic; James K Min
Journal:  J Am Coll Cardiol       Date:  2011-11-01       Impact factor: 24.094

6.  Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis.

Authors:  Brian S Ko; James D Cameron; Ravi K Munnur; Dennis T L Wong; Yasuko Fujisawa; Takuya Sakaguchi; Kenji Hirohata; Jacqui Hislop-Jambrich; Shinichiro Fujimoto; Kazuhisa Takamura; Marcus Crossett; Michael Leung; Ahilan Kuganesan; Yuvaraj Malaiapan; Arthur Nasis; John Troupis; Ian T Meredith; Sujith K Seneviratne
Journal:  JACC Cardiovasc Imaging       Date:  2016-10-19

7.  Can differences in corrected coronary opacification measured with computed tomography predict resting coronary artery flow?

Authors:  Benjamin J W Chow; Malek Kass; Owen Gagné; Li Chen; Yeung Yam; Alexander Dick; George A Wells
Journal:  J Am Coll Cardiol       Date:  2011-03-15       Impact factor: 24.094

8.  Diagnostic accuracy of fractional flow reserve from anatomic CT angiography.

Authors:  James K Min; Jonathon Leipsic; Michael J Pencina; Daniel S Berman; Bon-Kwon Koo; Carlos van Mieghem; Andrejs Erglis; Fay Y Lin; Allison M Dunning; Patricia Apruzzese; Matthew J Budoff; Jason H Cole; Farouc A Jaffer; Martin B Leon; Jennifer Malpeso; G B John Mancini; Seung-Jung Park; Robert S Schwartz; Leslee J Shaw; Laura Mauri
Journal:  JAMA       Date:  2012-09-26       Impact factor: 56.272

9.  Noninvasive fractional flow reserve derived from computed tomography angiography for coronary lesions of intermediate stenosis severity: results from the DeFACTO study.

Authors:  Ryo Nakazato; Hyung-Bok Park; Daniel S Berman; Heidi Gransar; Bon-Kwon Koo; Andrejs Erglis; Fay Y Lin; Allison M Dunning; Matthew J Budoff; Jennifer Malpeso; Jonathon Leipsic; James K Min
Journal:  Circ Cardiovasc Imaging       Date:  2013-09-30       Impact factor: 7.792

10.  Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps).

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

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  7 in total

1.  High-Risk Plaque Regression and Stabilization: Hybrid Noninvasive Morphological and Hemodynamic Assessment.

Authors:  Andreas A Giannopoulos; Dimitrios Mitsouras; Andrea Bartykowszki; Béla Merkely; Yiannis S Chatzizisis; Ronny R Buechel; Philipp A Kaufmann; Oliver Gaemperli; Pál Maurovich-Horvat
Journal:  Circ Cardiovasc Imaging       Date:  2018-07       Impact factor: 7.792

2.  Multimodal Multiparametric Three-dimensional Image Fusion in Coronary Artery Disease: Combining the Best of Two Worlds.

Authors:  Jochen von Spiczak; Manoj Mannil; Hanna Model; Chris Schwemmer; Sebastian Kozerke; Frank Ruschitzka; Hatem Alkadhi; Robert Manka
Journal:  Radiol Cardiothorac Imaging       Date:  2020-04-16

3.  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

4.  The transluminal attenuation gradient in coronary CT angiography for the detection of hemodynamically significant disease: can all arteries be treated equally?

Authors:  Shinichiro Fujimoto; Andreas A Giannopoulos; Kanako K Kumamaru; Rie Matsumori; Anji Tang; Etsuro Kato; Yuko Kawaguchi; Kazuhisa Takamura; Katsumi Miyauchi; Hiroyuki Daida; Frank J Rybicki; Dimitris Mitsouras
Journal:  Br J Radiol       Date:  2018-04-12       Impact factor: 3.039

5.  Use of the volume-averaged Murray's deviation method for the characterization of branching geometry in liver fibrosis: a preliminary study on vascular circulation.

Authors:  Wenjuan Lv; Jianbo Jian; Jingyi Liu; Xinyan Zhao; Xiaohong Xin; Chunhong Hu
Journal:  Quant Imaging Med Surg       Date:  2022-02

Review 6.  Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease.

Authors:  Haoxuan Lu; Yudong Yao; Li Wang; Jianing Yan; Shuangshuang Tu; Yanqing Xie; Wenming He
Journal:  Comput Math Methods Med       Date:  2022-04-26       Impact factor: 2.809

Review 7.  [Prognosis of patients with vulnerable plaques indicated by coronary CT angiography].

Authors:  Zhanlu Li; He Huang; Wenbin Zhang; Min Wang; Guosheng Fu
Journal:  Zhejiang Da Xue Xue Bao Yi Xue Ban       Date:  2020-05-25
  7 in total

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