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Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography?

  • Xin Liu
  • Yabin Wang
  • Heye Zhang
  • Youbing Yin
  • Kunlin Cao
  • Zhifan Gao
  • Huafeng Liu
  • William Kongto Hau
  • Lei Gao
  • Yundai Chen
  • Feng CaoEmail author
  • Wenhua HuangEmail author
Cardiac
  • 111 Downloads

Abstract

Background

We aimed to compare the performance of FFRCT and FFRQCA in assessing the functional significance of coronary artery stenosis in patients suffering from coronary artery disease with stable angina.

Method

A total of 101 stable coronary heart disease (CAD) patients with 181 lesions were recruited. FFRCT and FFRQCA were compared using invasive fractional flow reserve (FFR) as a reference standard. Comparisons between FFRCT and FFRQCA were conducted based on strategies of the geometric reconstruction, boundary conditions, and geometric characteristics. The performance of FFRCT and FFRQCA in detecting hemodynamic significance was also investigated.

Results

The performance of FFRCT and FFRQCA in discriminating hemodynamically significant lesions was compared. Good correlation and agreement with invasive FFR was found using FFRCT and FFRQCA (r = 0.809, p < 0.001 and r = 0.755, p < 0.001). A significant difference was observed in the complex coronary artery tree, in which relatively better prediction was observed using FFRCT than FFRQCA when analyzing the stenosis distributed in the middle segment of a stenotic branch (p = 0.036). Moreover, FFRCT was found to be better at predicting hemodynamically insignificant stenosis than FFRQCA (p = 0.007), while the performance of the two parameters was similar in discriminating functional significant lesions using an FFR threshold of ≤ 0.8 as a reference standard.

Conclusion

FFRCT and FFRQCA could both accurately rule out functional insignificant lesions in stable CAD patients. FFRCT was found to be better for the noninvasive screening of CAD patients with stable angina than FFRQCA.

Key Points

• FFR CT and FFR QCA were both in good correlation and agreement with invasive FFR measurements.

• FFR CT is superior in accuracy and consistency compared to FFR QCA in patients with stenoses distributed in left coronary artery.

• The noninvasive nature of FFR CT could provide potential benefit for stable CAD patients on disease management.

Keywords

Myocardial fractional flow reserve Computed tomography angiography Coronary artery disease Hemodynamics Myocardial ischemia 

Abbreviations

ANOVA

Analysis of variance

AUC

Area under the receiver operating characteristic curve

BFN

Bifurcation number

CAD

Coronary artery disease

CCTA

Coronary CT angiography

DB

Diagonal artery

FAME

The fractional flow reserve versus angiography for guiding PCI in patients with multivessel coronary artery disease

FFR

Fractional flow reserve

FFRCT

Computed tomography–derived FFR

FFRQCA

Quantitative coronary angiography-derived FFR

GZ

Gray zone

IC

Ischemia confirmed

ICA

Invasive coronary angiography

LAD

Left anterior descending branches

LCA

Left coronary artery

LCX

Left circumflex branches

LR

Stenosis distribution in LCA and RCA

NPV

Negative predictive value

OM

Obtuse marginal artery

PCI

Percutaneous coronary intervention

PPV

Positive predictive value

QCA

Quantitative coronary angiography

RCA

Right coronary artery

S

Stratification according to FFR

SD

Standard deviation

StO

Number of lesions in a single branch

Notes

Funding

This work was supported by grants from Shenzhen–Hong Kong Innovation Circle Program (SGLH20161212104605195), National Key Research and Development Program of China (2016YFC1300300, 2017YFC1103403, 2016YFA0100900), Shenzhen Science and Technology Program (JCYJ20170413114916687, JCYJ20170306092258717), National Natural Science Foundation of China (61771464, U1801265, 81500360, 81227901, 81530058, and 81570272), the Science and Technology Project of Guangdong Province (2016B090925001, 2017B090912006), and China Postdoctoral Science Foundation (2016T90990 and 2016M603026).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Feng Cao.

Conflict of interest

The authors report no relationships that could be construed as a conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained.

Ethical approval

Institutional Review Board approved the present study.

Methodology

• retrospective

• observational

• performed at one institution

Supplementary material

330_2019_6023_MOESM1_ESM.docx (579 kb)
ESM 1 (DOCX 578 kb)

References

  1. 1.
    Smits PC, Abdel-Wahab M, Neumann FJ et al (2017) Fractional flow reserve–guided multivessel angioplasty in myocardial infarction. N Engl J Med 376:1234–1244CrossRefGoogle Scholar
  2. 2.
    Tonino PA, Fearon WF, De Bruyne B et al (2010) Angiographic versus functional severity of coronary artery stenoses in the FAME study. J Am Coll Cardiol 55:2816CrossRefGoogle Scholar
  3. 3.
    Ciccarelli G, Barbato E, Toth GG et al (2018) Angiography versus hemodynamics to predict the natural history of coronary stenoses: fractional flow reserve versus angiography in multivessel evaluation 2 substudy. Circulation 137:1475–1485CrossRefGoogle Scholar
  4. 4.
    Pijls NHJ, Fearon WF, Tonino PA et al (2010) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention in patients with multivessel coronary artery disease: 2-year follow-up of the FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) study. J Am Coll Cardiol 56:177Google Scholar
  5. 5.
    van Nunen LX, Zimmermann FM, Tonino PA et al (2015) Fractional flow reserve versus angiography for guidance of PCI in patients with multivessel coronary artery disease (FAME): 5-year follow-up of a randomised controlled trial. Lancet 386:1853–1860CrossRefGoogle Scholar
  6. 6.
    Rimac G, Fearon WF, De Bruyne B et al (2017) Clinical value of post–percutaneous coronary intervention fractional flow reserve value: a systematic review and meta-analysis. Am Heart J 183:1–9CrossRefGoogle Scholar
  7. 7.
    Xaplanteris P, Fournier S, Pijls NH et al (2018) Five-year outcomes with PCI guided by fractional flow reserve. N Engl J Med 379:250–259CrossRefGoogle Scholar
  8. 8.
    Fearon WF, Nishi T, De Bruyne B et al (2018) Clinical outcomes and cost-effectiveness of fractional flow reserve–guided percutaneous coronary intervention in patients with stable coronary artery disease: three-year follow-up of the FAME 2 trial (fractional flow reserve versus angiography for multivessel evaluation). Circulation 137:480–487CrossRefGoogle Scholar
  9. 9.
    Johnson NP, Johnson DT, Kirkeeide RL et al (2015) Repeatability of fractional flow reserve despite variations in systemic and coronary hemodynamics. JACC Cardiovasc Interv 8:1018–1027CrossRefGoogle Scholar
  10. 10.
    Joseph TA, Lehrich J, Chan PS et al (2017) Use of fractional flow reserve in elderly patients undergoing elective percutaneous coronary intervention. JACC Cardiovasc Interv 10:419–420Google Scholar
  11. 11.
    Taylor CA, Figueroa CA (2009) Patient-specific modeling of cardiovascular mechanics. Annu Rev Biomed Eng 11:109–134CrossRefGoogle Scholar
  12. 12.
    Tu S, Barbato E, Köszegi Z et al (2014) Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv 7:768–777Google Scholar
  13. 13.
    Siogkas PK, Anagnostopoulos CD, Liga R et al (2018) Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve. Eur Radiol.  https://doi.org/10.1007/s00330-018-5781-8
  14. 14.
    Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28:2655–2664CrossRefGoogle Scholar
  15. 15.
    Coenen A, Lubbers MM, Kurata A et al (2017) Diagnostic value of transmural perfusion ratio derived from dynamic CT-based myocardial perfusion imaging for the detection of haemodynamically relevant coronary artery stenosis. Eur Radiol 27:2309–2316CrossRefGoogle Scholar
  16. 16.
    Nakazato R, Park HB, Berman DS et al (2013) Noninvasive fractional flow reserve derived from computed tomography angiography for coronary lesions of intermediate stenosis severity: results from the DeFACTO study. Circ Cardiovasc Imaging 6:881–889CrossRefGoogle Scholar
  17. 17.
    Min JK, Berman DS, Budoff MJ et al (2011) Rationale and design of the DeFACTO (determination of fractional flow reserve by anatomic computed tomographic angiography) study. J Cardiovasc Comput Tomogr 5:301–309CrossRefGoogle Scholar
  18. 18.
    Min JK, Koo B-K, Erglis A et al (2012) Usefulness of noninvasive fractional flow reserve computed from coronary computed tomographic angiograms for intermediate stenoses confirmed by quantitative coronary angiography. Am J Cardiol 110:971–976CrossRefGoogle Scholar
  19. 19.
    van Hamersvelt RW, Zreik M, Voskuil M, Viergever MA, Išgum I, Leiner T (2018) Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol.  https://doi.org/10.1007/s00330-018-5822-3
  20. 20.
    Nørgaard BL, Leipsic J, Gaur S et al (2014) 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 63:1145–1155Google Scholar
  21. 21.
    Nishi T, Fearon WF (2018) Fractional flow reserve derived from routine coronary angiography. Int J Cardiol 271:51–52CrossRefGoogle Scholar
  22. 22.
    Yazaki K, Otsuka M, Kataoka S et al (2017) Applicability of 3-dimensional quantitative coronary angiography-derived computed fractional flow reserve for intermediate coronary stenosis. Circ J 81:988–992CrossRefGoogle Scholar
  23. 23.
    Koo BK, Erglis A, Doh JH et al (2011) 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. J Am Coll Cardiol 58:1989–1997Google Scholar
  24. 24.
    Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic ct angiography. JAMA 308:1237–1245CrossRefGoogle Scholar
  25. 25.
    Voros S, Rinehart S, Qian Z et al (2011) Prospective validation of standardized, 3-dimensional, quantitative coronary computed tomographic plaque measurements using radiofrequency backscatter intravascular ultrasound as reference standard in intermediate coronary arterial lesions: results from the ATLANTA (assessment of tissue characteristics, lesion morphology, and hemodynamics by angiography with fractional flow reserve, intravascular ultrasound and virtual histology, and noninvasive computed tomography in atherosclerotic plaques) I study. JACC Cardiovasc Interv 4:198–208Google Scholar
  26. 26.
    van der Giessen AG, Groen HC, Doriot PA et al (2011) The influence of boundary conditions on wall shear stress distribution in patients specific coronary trees. J Biomech 44:1089–1095CrossRefGoogle Scholar
  27. 27.
    Abbara S, Blanke P, Maroules CD et al (2016) SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr 10:435–449CrossRefGoogle Scholar
  28. 28.
    Scanlon PJ, Faxon DP, Audet AM et al (1999) ACC/AHA guidelines for coronary angiography: executive summary and recommendations: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (committee on coronary angiography) developed in collaboration with the Society for Cardiac Angiography and Interventions. Circulation 99:2345–2357CrossRefGoogle Scholar
  29. 29.
    Levine GN, Bates ER, Blankenship JC et al (2016) 2015 ACC/AHA/SCAI Focused Update on Primary Percutaneous Coronary Intervention for Patients With ST-Elevation Myocardial Infarction: An Update of the 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention and the 2013 ACCF/AHA Guideline for the Management of STElevation Myocardial Infarction. J Am Coll Cardiol 67:1235–1250Google Scholar
  30. 30.
    Tonino PA, De Bruyne B, Pijls NHJ et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224CrossRefGoogle Scholar
  31. 31.
    Liu X, Peng C, Xia Y et al (2017) Hemodynamics analysis of the serial stenotic coronary arteries. Biomed Eng Online 16:127CrossRefGoogle Scholar
  32. 32.
    Wang X, Peng C, Liu X, Pan Z (2018) Functional assessment of stenotic coronary artery in 3D geometric reconstruction from fusion of intravascular ultrasound and X-ray angiography. IEEE Access 6:53330–53341CrossRefGoogle Scholar
  33. 33.
    Gao Z, Liu X, Qi S, Wu W, Hau WK, Zhang H (2017) Automatic segmentation of coronary tree in CT angiography images. Int J Adapt Control Signal Process.  https://doi.org/10.1002/acs.2762
  34. 34.
    Xu P, Liu X, Zhang H et al (2018) Assessment of boundary conditions for CFD simulation in human carotid artery. Biomech Model Mechanobiol 17:1581–1597CrossRefGoogle Scholar
  35. 35.
    Kim HJ, Vignon-Clementel IE, Coogan JS, Figueroa CA, Jansen KE, Taylor CA (2010) Patient-specific modeling of blood flow and pressure in human coronary arteries. Ann Biomed Eng 38:3195–3209CrossRefGoogle Scholar
  36. 36.
    Multiphysics C (2015) v. 5.2. COMSOL AB, Stockholm, SwedenGoogle Scholar
  37. 37.
    Adjedj J, De Bruyne B, Floré V et al (2016) Significance of intermediate values of fractional flow reserve in patients with coronary artery disease. Circulation 133:502–508Google Scholar
  38. 38.
    Rivolo S, Hadjilucas L, Sinclair M et al (2016) Impact of coronary bifurcation morphology on wave propagation. Am J Physiol Heart Circ Physiol 311:H855–H870CrossRefGoogle Scholar
  39. 39.
    Tanaka K, Bezerra HG, Gaur S et al (2016) Comparison between non-invasive (coronary computed tomography angiography derived) and invasive-fractional flow reserve in patients with serial stenoses within one coronary artery: a NXT trial substudy. Ann Biomed Eng 44:580–589CrossRefGoogle Scholar
  40. 40.
    Chow BJ, Abraham A, Wells GA et al (2009) Diagnostic accuracy and impact of computed tomographic coronary angiography on utilization of invasive coronary angiography. Circ Cardiovasc Imaging 2:16–23CrossRefGoogle Scholar
  41. 41.
    Budoff MJ, Nakazato R, Mancini GB et al (2016) CT angiography for the prediction of hemodynamic significance in intermediate and severe lesions: head-to-head comparison with quantitative coronary angiography using fractional flow reserve as the reference standard. JACC Cardiovasc Imaging 9:559–564CrossRefGoogle Scholar
  42. 42.
    Tu S, Jing J, Holm NR et al (2012) In vivo assessment of bifurcation optimal viewing angles and bifurcation angles by three-dimensional (3D) quantitative coronary angiography. Int J Cardiovasc Imaging 28:1617–1625CrossRefGoogle Scholar
  43. 43.
    Kim HY, Doh JH, Lim H-S et al (2017) Identification of coronary artery side branch supplying myocardial mass that may benefit from revascularization. JACC Cardiovasc Interv 10:571–581CrossRefGoogle Scholar
  44. 44.
    Li Y, Gutiérrez-Chico JL, Holm NR et al (2015) Impact of side branch modeling on computation of endothelial shear stress in coronary artery disease. J Am Coll Cardiol 66:125CrossRefGoogle Scholar
  45. 45.
    Pijls NHJ, De Bruyne B, Bech GJW et al (2000) Coronary pressure measurement to assess the hemodynamic significance of serial stenoses within one coronary artery. Circulation 102:2371CrossRefGoogle Scholar
  46. 46.
    Monno K, Yoda S, Hatta T et al (2018) Optimal cut-off value of non-invasive fractional flow reserve for coronary revascularization using a combination of nuclear cardiology in patients with stable coronary artery disease. J Am Coll Cardiol 71:A1578CrossRefGoogle Scholar
  47. 47.
    Tebaldi M, Biscaglia S, Pecoraro A, Fineschi M, Campo G (2016) Fractional flow reserve implementation in daily clinical practice: a European survey. Int J Cardiol 207:206–207CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Xin Liu
    • 1
    • 2
  • Yabin Wang
    • 3
  • Heye Zhang
    • 4
  • Youbing Yin
    • 5
  • Kunlin Cao
    • 5
  • Zhifan Gao
    • 6
  • Huafeng Liu
    • 7
  • William Kongto Hau
    • 8
  • Lei Gao
    • 3
  • Yundai Chen
    • 3
  • Feng Cao
    • 3
    Email author
  • Wenhua Huang
    • 9
    Email author
  1. 1.Shenzhen Institutes of Advanced Technology & National Clinical Research Center of Geriatric DiseaseChinese PLA General HospitalBeijingChina
  2. 2.Guangdong Academy Research on VR IndustryFoshan UniversityFoshanChina
  3. 3.Department of Cardiology & National Clinical Research Center of Geriatric DiseaseChinese PLA General HospitalBeijingChina
  4. 4.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  5. 5.Shenzhen Keya Medical TechnologyShenzhenChina
  6. 6.The School of Schulich Medicine and DentistryWestern UniversityLondonCanada
  7. 7.State Key Laboratory of Modern Optical InstrumentationZhejiang UniversityHangzhouChina
  8. 8.Department of Medicine and Therapeutics, Faculty of MedicineThe Chinese University of Hong KongHong KongChina
  9. 9.School of Basic Medical Science, Guangdong Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Medical BiomechanicsSouthern Medical UniversityGuangzhouChina

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