Literature DB >> 33778507

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

Kanako K Kumamaru1, Erin Angel1, Kelsey N Sommer1, Vijay Iyer1, Michael F Wilson1, Nikhil Agrawal1, Aishwarya Bhardwaj1, Sharma B Kattel1, Sandra Kondziela1, Saurabh Malhotra1, Christopher Manion1, Katherine Pogorzelski1, Tharmathai Ramanan1, Abhishek C Sawant1, Mary M Suplicki1, Sameer Waheed1, Shinichiro Fujimoto1, Umesh C Sharma1, Frank J Rybicki1, Ciprian N Ionita1.   

Abstract

PURPOSE: To measure the inter- and intraobserver variability among operators of varying expertise in conducting CT-derived fractional flow reserve (CT FFR) measurements on-site by using structural and fluid analysis and to evaluate differences in reproducibility between two different training methods for end users.
MATERIALS AND METHODS: This retrospective analysis of the prospectively enrolled cohort included 22 symptomatic patients who underwent both 320-detector row coronary CT angiography and catheter-derived fractional flow reserve (FFR) within 90 days. Thirteen operators of varying expertise were assigned to one of two training arms: arm 1, on-site training by a specialist in CT FFR technology; arm 2, self-training through use of written materials. After the training, all 13 operators reviewed the CT data and measured CT FFR in 24 vessels in 22 patients. Inter- and intraoperator variability and agreements between CT FFR and catheter-derived FFR measurements were evaluated.
RESULTS: The overall intraclass correlation coefficient (ICC) among operators was 0.71 (95% confidence interval: 0.58, 0.83) with a mean absolute difference (± standard deviation) of 0.027 ± 0.022. The operators in arm 2 showed greater interoperator differences than those in arm 1 (0.031 ± 0.024 vs 0.023 ± 0.018; P = .024). Among operators who recalculated CT FFR, the mean CT FFR value did not significantly differ between the first and second calculations (ICC, 0.66; 95% confidence interval: 0.46, 0.87), with the medical specialists producing the lowest intraoperator variability (0.053 ± 0.060). The overall correlation coefficient between CT FFR and catheter FFR was r = 0.61, with a mean absolute difference of 0.096 ± 0.089.
CONCLUSION: Good reproducibility of CT FFR values calculated on-site on the basis of structural and fluid analysis was observed among operators of varying expertise. Face-to-face training sessions may cause less variability.© RSNA, 2019Supplemental material is available for this article. 2019 by the Radiological Society of North America, Inc.

Entities:  

Year:  2019        PMID: 33778507      PMCID: PMC7977693          DOI: 10.1148/ryct.2019180012

Source DB:  PubMed          Journal:  Radiol Cardiothorac Imaging        ISSN: 2638-6135


  18 in total

Review 1.  Coronary pressure measurement and fractional flow reserve.

Authors:  N H Pijls; B De Bruyne
Journal:  Heart       Date:  1998-12       Impact factor: 5.994

2.  Fractional flow reserve-guided revascularization: practical implications of a diagnostic gray zone and measurement variability on clinical decisions.

Authors:  Ricardo Petraco; Sayan Sen; Sukhjinder Nijjer; Mauro Echavarria-Pinto; Javier Escaned; Darrel P Francis; Justin E Davies
Journal:  JACC Cardiovasc Interv       Date:  2013-03       Impact factor: 11.195

3.  Fractional flow reserve derived from coronary CT angiography: variation of repeated analyses.

Authors:  Sara Gaur; Hiram G Bezerra; Jens F Lassen; Evald H Christiansen; Kentaro Tanaka; Jesper M Jensen; Keith G Oldroyd; Jonathon Leipsic; Stephan Achenbach; Anne K Kaltoft; Hans Erik Bøtker; Bjarne L Nørgaard
Journal:  J Cardiovasc Comput Tomogr       Date:  2014-07-11

4.  Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome.

Authors:  Taylor M Duguay; Christian Tesche; Rozemarijn Vliegenthart; Carlo N De Cecco; Han Lin; Moritz H Albrecht; Akos Varga-Szemes; Domenico De Santis; Ullrich Ebersberger; Richard R Bayer; Sheldon E Litwin; Ellen Hoffmann; Daniel H Steinberg; U Joseph Schoepf
Journal:  Am J Cardiol       Date:  2017-07-25       Impact factor: 2.778

5.  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

6.  Noninvasive Computed Tomography-Derived Fractional Flow Reserve Based on Structural and Fluid Analysis: Reproducibility of On-site Determination by Unexperienced Observers.

Authors:  Keiken Ri; Kanako K Kumamaru; Shinichiro Fujimoto; Yuko Kawaguchi; Tomotaka Dohi; Sou Yamada; Kazuhisa Takamura; Yosuke Kogure; Norikazu Yamada; Etsuro Kato; Ryusuke Irie; Tomohiro Takamura; Michimasa Suzuki; Masaaki Hori; Shigeki Aoki; Hiroyuki Daida
Journal:  J Comput Assist Tomogr       Date:  2018 Mar/Apr       Impact factor: 1.826

Review 7.  Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis.

Authors:  Charles A Taylor; Timothy A Fonte; James K Min
Journal:  J Am Coll Cardiol       Date:  2013-04-03       Impact factor: 24.094

8.  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

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.  Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study.

Authors:  Pamela S Douglas; Gianluca Pontone; Mark A Hlatky; Manesh R Patel; Bjarne L Norgaard; Robert A Byrne; Nick Curzen; Ian Purcell; Matthias Gutberlet; Gilles Rioufol; Ulrich Hink; Herwig Walter Schuchlenz; Gudrun Feuchtner; Martine Gilard; Daniele Andreini; Jesper M Jensen; Martin Hadamitzky; Karen Chiswell; Derek Cyr; Alan Wilk; Furong Wang; Campbell Rogers; Bernard De Bruyne
Journal:  Eur Heart J       Date:  2015-09-01       Impact factor: 29.983

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Authors:  Jeff Joseph; Benjamin Weppner; Nandor K Pinter; Mohammad Mahdi Shiraz Bhurwani; Andre Monteiro; Ammad Baig; Jason Davies; Adnan Siddiqui; Ciprian N Ionita
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

2.  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

3.  Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia.

Authors:  Dirk Lossnitzer; Selina Klenantz; Florian Andre; Johannes Goerich; U Joseph Schoepf; Kyle L Pazzo; Andre Sommer; Matthias Brado; Friedemann Gückel; Roman Sokiranski; Tobias Becher; Ibrahim Akin; Sebastian J Buss; Stefan Baumann
Journal:  BMC Cardiovasc Disord       Date:  2022-02-05       Impact factor: 2.298

  3 in total

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