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Comparison of coronary CT angiography-based and invasive coronary angiography-based quantitative flow ratio for functional assessment of coronary stenosis: A multicenter retrospective analysis

  • Author Footnotes
    1 The first two authors equally contributed.
    Zehang Li
    Footnotes
    1 The first two authors equally contributed.
    Affiliations
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
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  • Author Footnotes
    1 The first two authors equally contributed.
    Guanyu Li
    Footnotes
    1 The first two authors equally contributed.
    Affiliations
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
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  • Liudan Chen
    Affiliations
    Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
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  • Daixin Ding
    Affiliations
    The Lambe Institute for Translational Medicine and Curam, National University of Ireland, Galway, Ireland
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  • Yankai Chen
    Affiliations
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
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  • Jiayin Zhang
    Affiliations
    Department of Radiology, Shanghai Jiao Tong University Affiliated First People's Hospital, Shanghai, China
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  • Lei Xu
    Affiliations
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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  • Takashi Kubo
    Affiliations
    Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan
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  • Su Zhang
    Affiliations
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
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  • Yining Wang
    Correspondence
    Corresponding author. No. 1, Shuaifuyuan, Dongcheng District, Beijing 100730, China.
    Affiliations
    Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Xuhui Zhou
    Correspondence
    Corresponding author. Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518036, China.
    Affiliations
    Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
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  • Shengxian Tu
    Correspondence
    Corresponding author. Med-X Research Institute, Room 123, No. 1954, Hua Shan Road, Shanghai 200030, China.
    Affiliations
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    1 The first two authors equally contributed.

      Abstract

      Background

      The aim of this study was to evaluate the diagnostic performance of coronary CT angiography (CTA)-based quantitative flow ratio (QFR), namely CT-QFR, and compare it with invasive coronary angiography (ICA)-based Murray law QFR (μQFR), using fractional flow reserve (FFR) as the reference standard.

      Methods

      Patients who underwent coronary CTA, ICA and pressure wire-based FFR assessment within two months were retrospectively analyzed. CT-QFR and μQFR were computed in blinded fashion and compared with FFR, all applying the same cut-off value of ≤0.80 to identify hemodynamically significant stenosis.

      Results

      Paired comparison between CT-QFR and μQFR was performed in 191 vessels from 167 patients. Average FFR was 0.81 ​± ​0.10 and 42.4% vessels had an FFR ≤0.80. CT-QFR had a slightly lower correlation with FFR compared with μQFR, although statistically non-significant (r ​= ​0.87 versus 0.90, p ​= ​0.110). The vessel-level diagnostic performance of CT-QFR was slightly lower but without statistical significance than μQFR (AUC ​= ​0.94 versus 0.97, difference: −0.03 [95%CI: −0.00-0.06], p ​= ​0.095), and substantially higher than diameter stenosis by CTA (AUC difference: 0.17 [95%CI: −0.10-0.23], p ​< ​0.001). The patient-level diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio for CT-QFR to identify FFR value ​≤ ​0.80 was 88%, 90%, 86%, 86%, 91%, 6.59 and 0.12, respectively. The diagnostic accuracy of CT-QFR was 84% in extensively calcified lesions, while in vessels with no or less calcification, CT-QFR showed a comparable diagnostic accuracy with μQFR (91% versus 92%, p ​= ​0.595). Intra- and inter-observer variability in CT-QFR analysis was −0.00 ​± ​0.04 and 0.00 ​± ​0.04, respectively.

      Conclusions

      Performance in diagnosis of hemodynamically significant coronary stenosis by CT-QFR was slightly lower but without statistical significance than μQFR, and substantially higher than CTA-derived diameter stenosis. Extensively calcified lesions reduced the diagnostic accuracy of CT-QFR.

      Keywords

      Abbreviations:

      CABG (coronary artery bypass grafting), CAD (coronary artery disease), CFD (computational fluid dynamics), CTA (CT angiography), CT-QFR (CTA-derived quantitative flow ratio), DS% (percent diameter stenosis), FFR (fractional flow reserve), FFRct (CT-derived fractional flow reserve), ICA (invasive coronary angiography), LAD (left anterior descending artery), PCI (percutaneous coronary intervention), QFR (quantitative flow ratio), μQFR (invasive coronary angiography-based Murray law QFR)
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      References

        • Miller J.M.
        • Rochitte C.E.
        • Dewey M.
        • et al.
        Diagnostic performance of coronary angiography by 64-row CT.
        N Engl J Med. 2008; 359: 2324-2336https://doi.org/10.1056/NEJMoa0806576
        • Toth G.
        • Hamilos M.
        • Pyxaras S.
        • et al.
        Evolving concepts of angiogram: fractional flow reserve discordances in 4000 coronary stenoses.
        Eur Heart J. 2014; 35: 2831-2838ahttps://doi.org/10.1093/eurheartj/ehu094
        • Tonino P.A.L.
        • De Bruyne B.
        • Pijls N.H.J.
        • et al.
        Fractional flow reserve versus angiography for guiding percutaneous coronary intervention.
        N Engl J Med. 2009; 360: 213-224https://doi.org/10.1056/NEJMoa0807611
        • De Bruyne B.
        • Pijls N.H.J.
        • Kalesan B.
        • et al.
        Fractional flow reserve–guided PCI versus medical therapy in stable coronary disease.
        N Engl J Med. 2012; 367: 991-1001https://doi.org/10.1056/NEJMoa1205361
        • Tu S.
        • Westra J.
        • Yang J.
        • et al.
        Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: the international multicenter FAVOR pilot study.
        JACC Cardiovasc Interv. 2016; 9: 2024-2035https://doi.org/10.1016/j.jcin.2016.07.013
        • Xu B.
        • Tu S.
        • Qiao S.
        • et al.
        Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis.
        J Am Coll Cardiol. 2017; 70: 3077-3087https://doi.org/10.1016/j.jacc.2017.10.035
        • Westra J.
        • Andersen B.K.
        • Campo G.
        • et al.
        Diagnostic performance of in-procedure angiography-derived quantitative flow reserve compared to pressure-derived fractional flow reserve: the FAVOR II europe-Japan study.
        J Am Heart Assoc. 2018; 7https://doi.org/10.1161/JAHA.118.009603
        • Li Z.
        • Zhang J.
        • Xu L.
        • Yang W.
        • Tu S.
        Diagnostic accuracy of a fast computational approach to derive fractional flow reserve from coronary CT angiography.
        JACC. Cardiovascular imaging. 2019; 13
        • Tu S.
        • Ding D.
        • Chang Y.
        • Li C.
        • Wijns W.
        • Xu B.
        Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: a novel method based on bifurcation fractal law.
        Cathet Cardiovasc Interv. 2021; 97: 1040-1047
        • Bischoff B.
        • Hein F.
        • Meyer T.
        • et al.
        Impact of a reduced tube voltage on CT angiography and radiation dose results of the PROTECTION I study.
        JACC-Cardiovascular Imaging. 2009; 2: 940-946
        • Çiçek Ö.
        • Abdulkadir A.
        • Lienkamp S.S.
        • Brox T.
        • Ronneberger O.
        3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.
        Springer, Cham2016: 424-432
        • West G.B.
        A general model for the origin of allometric scaling laws in biology.
        Science. 1997; 276: 122-126https://doi.org/10.1126/science.276.5309.122
        • Murray C.
        The physiological principle of minimum work : I. The vascular system and the cost of blood volume.
        Proc Natl Acad Sci USA. 1926; 12: 207
        • Cerci R.
        • Vavere A.L.
        • Miller J.M.
        • et al.
        Patterns of coronary arterial lesion calcification by a novel, cross-sectional CT angiographic assessment.
        Int J Card Imag. 2013; 29: 1619-1627
        • Liu L.
        • Yang W.
        • Nagahara Y.
        • et al.
        The impact of image resolution on computation of fractional flow reserve: coronary computed tomography angiography versus 3-dimensional quantitative coronary angiography.
        Int J Card Imag. 2016; 32: 513-523https://doi.org/10.1007/s10554-015-0797-5
        • Kruk M.
        • Noll D.
        • Achenbach S.
        • et al.
        Impact of coronary artery calcium characteristics on accuracy of CT angiography.
        JACC Cardiovasc Imaging. 2014; 7: 49-58
        • Arbab-Zadeh A.
        • Miller J.M.
        • Rochitte C.E.
        • et al.
        Diagnostic accuracy of computed tomography coronary angiography according to pre-test probability of coronary artery disease and severity of coronary arterial calcification. The CORE-64 (Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomogr.
        J Am Coll Cardiol. 2012; 59: 379-387
        • Hoffmann U.
        • Moselewski F.
        • Cury R.C.
        • et al.
        Predictive value of 16-slice multidetector spiral computed tomography to detect significant obstructive coronary artery disease in patients at high risk for coronary artery disease: patient-versus segment-based analysis.
        ACC Curr J Rev. 2004; 14 (25): 25
        • Brodoefel H.
        • Burgstahler C.
        • Tsiflikas I.
        • et al.
        Dual-source CT: effect of heart rate, heart rate variability, and calcification on image quality and diagnostic accuracy.
        Radiology. 2008; 247: 346-355
        • Nørgaard B.L.
        • Gaur S.
        • Leipsic J.
        • et al.
        Influence of coronary calcification on the diagnostic performance of CT angiography derived FFR in coronary artery disease: a substudy of the NXT trial.
        JACC Cardiovasc Imaging. 2015; 8: 1045-1055
        • Patel M.R.
        • Peterson E.D.
        • Dai D.
        • et al.
        Low diagnostic yield of elective coronary angiography.
        N Engl J Med. 2010; 362: 886-895
        • Douglas P.S.
        • Pontone G.
        • Hlatky M.A.
        • et al.
        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 FFRCT : outcome and resource.
        Eur Heart J. 2015; 36: 3359-3367https://doi.org/10.1093/eurheartj/ehv444
        • Michael
        • Lu
        • Maros
        • et al.
        Noninvasive FFR derived from coronary CT angiography: management and outcomes in the PROMISE trial.
        JACC Cardiovasc Imaging. 2017; 10: 1350-1358
        • Koo B.K.
        • Erglis A.
        • Doh J.H.
        • et al.
        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 Noni.
        J Am Coll Cardiol. 2011; 58: 1989-1997https://doi.org/10.1016/j.jacc.2011.06.066
        • Min J.K.
        • Leipsic J.
        • Pencina M.J.
        • et al.
        Diagnostic accuracy of fractional flow reserve from anatomic CT angiography.
        JAMA. 2012; 308 (1237): 1237https://doi.org/10.1001/2012.jama.11274
        • Nørgaard B.L.
        • Leipsic J.
        • Gaur S.
        • et al.
        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).
        J Am Coll Cardiol. 2014; 63: 1145-1155https://doi.org/10.1016/j.jacc.2013.11.043
        • Itu L.
        • Rapaka S.
        • Passerini T.
        • et al.
        A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.
        J Appl Physiol. 2016; 121: 42-52
        • Coenen A.
        • Kim Y.H.
        • Kruk M.
        • et al.
        Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve result from the MACHINE consortium.
        Circ Cardiovasc Imaging. 2018; 11e007217
        • Cook C.M.
        • Petraco R.
        • Shun-Shin M.J.
        • et al.
        Diagnostic accuracy of computed tomography-derived fractional flow reserve A systematic review.
        JAMA Cardiol. 2017; 2: 803-810
        • Yu M.M.
        • Lu Z.G.
        • Li W.B.
        • Wei M.
        • Yan J.
        • Zhang J.Y.
        CT morphological index provides incremental value to machine learning based CT-FFR for predicting hemodynamically significant coronary stenosis.
        Int J Cardiol. 2018; 265: 256-261
        • Xu B.
        • Tu S.
        • Song L.
        • et al.
        Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial.
        Lancet. 2021; 398: 2149-2159
        • Liu L.
        • Ding F.
        • Gutiérrez-Chico J.L.
        • et al.
        Prognostic value of post-procedural μQFR for drug-coated balloons in the treatment of in-stent restenosis.
        Cardiol J. 2021; (Online ahead of print)https://doi.org/10.5603/CJ.a2021.0154