Skip to main content

Advertisement

SpringerLink
Log in

Predictive value of the QFR in detecting vulnerable plaques in non-flow limiting lesions: a combined analysis of the PROSPECT and IBIS-4 study

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Studies have shown that the quantitative flow ratio (QFR), recently introduced to assess lesion severity from coronary angiography, provides useful prognostic information; however the additive value of this technique over intravascular imaging in detecting lesions that are likely to cause events is yet unclear. We analysed data acquired in the PROSPECT and IBIS-4 studies, in particular the baseline virtual histology-intravascular ultrasound (VH-IVUS) and angiographic data from 17 non-culprit lesions with a presumable vulnerable phenotype (i.e., thin or thick cap fibroatheroma) that caused major adverse cardiac events or required revascularization (MACE) at 5-year follow-up and from a group of 78 vulnerable plaques that remained quiescent. The segments studied by VH-IVUS were identified in coronary angiography and the QFR was estimated. The additive value of 3-dimensional quantitative coronary angiography (3D-QCA) and of the QFR in predicting MACE at 5 year follow-up beyond plaque characteristics was examined. It was found that MACE lesions had a greater plaque burden (PB) and smaller minimum lumen area (MLA) on VH-IVUS, a longer length and a smaller minimum lumen diameter (MLD) on 3D-QCA and a lower QFR compared with lesions that remained quiescent. By univariate analysis MLA, PB, MLD, lesion length on 3D-QCA and QFR were predictors of MACE. In multivariate analysis a low but normal QFR (> 0.80 to < 0.97) was the only independent prediction of MACE (HR 3.53, 95% CI 1.16–10.75; P = 0.027). In non-flow limiting lesions with a vulnerable phenotype, QFR may provide additional prognostic information beyond plaque morphology for predicting MACE throughout 5 years.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Park J, Lee JM, Koo BK et al (2018) Clinical relevance of functionally insignificant moderate coronary artery stenosis assessed by 3-vessel fractional flow reserve measurement. J Am Heart Assoc. https://doi.org/10.1161/JAHA.117.008055

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ahmed N, Layland J, Carrick D et al (2016) Safety of guidewire-based measurement of fractional flow reserve and the index of microvascular resistance using intravenous adenosine in patients with acute or recent myocardial infarction. Int J Cardiol. https://doi.org/10.1016/j.ijcard.2015.09.014

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tu S, Westra J, Yang J et al (2016) Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: the international multicenter FAVOR Pilot Study. JACC Cardiovasc Interv. https://doi.org/10.1016/j.jcin.2016.07.013

    Article  PubMed  Google Scholar 

  4. Westra J, Andersen BK, Campo G et al (2018) 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. https://doi.org/10.1161/JAHA.118.009603

    Article  PubMed  PubMed Central  Google Scholar 

  5. Xu B, Tu S, Qiao S et al (2017) Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2017.10.035

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hamaya R, Hoshino M, Kanno Y et al (2019) Prognostic implication of three-vessel contrast-flow quantitative flow ratio in patients with stable coronary artery disease. EuroIntervention 15:180–188. https://doi.org/10.4244/EIJ-D-18-00896

    Article  PubMed  Google Scholar 

  7. Tu S, Westra J, Adjedj J et al (2019) Fractional flow reserve in clinical practice: from wire-based invasive measurement to image-based computation. Eur Heart J. https://doi.org/10.1093/eurheartj/ehz918

    Article  PubMed  Google Scholar 

  8. Lee JM, Choi G, Koo BK et al (2018) Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging. https://doi.org/10.1016/j.jcmg.2018.01.023

    Article  PubMed  Google Scholar 

  9. Stone GW, Maehara A, Lansky AJ et al (2011) A prospective Natural-History Study of coronary atherosclerosis. N Engl J Med. https://doi.org/10.1056/NEJMoa1002358

    Article  PubMed  Google Scholar 

  10. Räber L, Taniwaki M, Zaugg S et al (2015) Effect of high-intensity statin therapy on atherosclerosis in non-infarct-related coronary arteries (IBIS-4): a serial intravascular ultrasonography study. Eur Heart J 36:490–500. https://doi.org/10.1093/eurheartj/ehu373

    Article  PubMed  Google Scholar 

  11. Stone PH, Maehara A, Coskun AU et al (2018) Role of low endothelial shear stress and plaque characteristics in the prediction of nonculprit major adverse cardiac events: the PROSPECT Study. JACC Cardiovasc Imaging. https://doi.org/10.1016/j.jcmg.2017.01.031

    Article  PubMed  Google Scholar 

  12. García-García HM, Mintz GS, Lerman A et al (2009) Tissue characterisation using intravascular radiofrequency data analysis: Recommendations for acquisition, analysis, interpretation and reporting. EuroIntervention. https://doi.org/10.4244/EIJV5I2A29

    Article  PubMed  Google Scholar 

  13. Tu S, Huang Z, Koning G et al (2010) A novel three-dimensional quantitative coronary angiography system: In-vivo comparison with intravascular ultrasound for assessing arterial segment length. Catheter Cardiovasc Interv. https://doi.org/10.1002/ccd.22502

    Article  PubMed  Google Scholar 

  14. Calvert PA, Obaid DR, O’Sullivan M et al (2011) Association between IVUS findings and adverse outcomes in patients with coronary artery disease: the VIVA (VH-IVUS in vulnerable atherosclerosis) study. JACC Cardiovasc Imaging. https://doi.org/10.1016/j.jcmg.2011.05.005

    Article  PubMed  Google Scholar 

  15. Waksman R (2018) Assessment of coronary near-infrared spectroscopy imaging to detect vulnerable plaques and vulnerable patients. The Lipid-Rich Plaque study. San Diego, pp 21–25

  16. Prati F, Romagnoli E, Gatto L et al (2018) Relationship between OCT coronary plaque morphology and clinical outcome (CLIMA). In: EuroPCR, Paris

  17. Johnson TW, Räber L, di Mario C et al (2019) Clinical use of intracoronary imaging. Part 2: acute coronary syndromes, ambiguous coronary angiography findings, and guiding interventional decision-making: an expert consensus document of the European Association of Percutaneous Cardiovascular Intervent. Eur Heart J. https://doi.org/10.1093/eurheartj/ehz332

    Article  PubMed  Google Scholar 

  18. Papafaklis MI, Mizuno S, Takahashi S et al (2016) Incremental predictive value of combined endothelial shear stress, plaque necrotic core, and plaque burden for future cardiac events: a post-hoc analysis of the PREDICTION Study. Int J Cardiol. https://doi.org/10.1016/j.ijcard.2015.08.208

    Article  PubMed  Google Scholar 

  19. Barbato E, Toth GG, Johnson NP et al (2016) A prospective Natural History Study of Coronary atherosclerosis using fractional flow reserve. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2016.08.055

    Article  PubMed  Google Scholar 

  20. Johnson NP, Tóth GG, Lai D et al (2014) Prognostic value of fractional flow reserve: linking physiologic severity to clinical outcomes. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2014.07.973

    Article  PubMed  PubMed Central  Google Scholar 

  21. Spitaleri G, Tebaldi M, Biscaglia S et al (2018) Quantitative flow ratio identifies Nonculprit Coronary lesions requiring revascularization in patients with ST-segment-elevation myocardial infarction and multivessel disease. Circ Cardiovasc Interv. https://doi.org/10.1161/CIRCINTERVENTIONS.117.006023

    Article  PubMed  Google Scholar 

  22. Lauri F, Macaya F, Mejía-Rentería H et al (2019) Angiography-derived functional assessment of non-culprit coronary stenoses during primary percutaneous coronary intervention for ST-elevation myocardial infarction. EuroIntervention. https://doi.org/10.4244/EIJ-D-18-01165

    Article  PubMed  Google Scholar 

  23. Pinilla-Echeverri N, Mehta SR, Wang J et al (2019) Non-culprit lesion plaque morphology in patients with ST-Segment elevation myocardial infarction: results from the COMPLETE trial optical coherence tomography (OCT) Substudy. American Heart Association Scientific Sessions, Philadelphia

    Google Scholar 

  24. Tian J, Dauerman H, Toma C et al (2014) Prevalence and characteristics of TCFA and degree of coronary artery stenosis: An OCT, IVUS, and angiographic study. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2014.05.052

    Article  PubMed  Google Scholar 

  25. Bourantas CV, Garcia-Garcia HM, Diletti R et al (2013) Early detection and invasive passivation of future culprit lesions: a future potential or an unrealistic pursuit of chimeras? Am Heart J 165:869–881

    Article  Google Scholar 

  26. Bourantas CV, Garcia-Garcia HM, Torii R et al (2016) Vulnerable plaque detection: an unrealistic quest or a feasible objective with a clinical value? Heart 102:581–589

    Article  CAS  Google Scholar 

  27. Ha J, Kim JS, Lim J et al (2016) Assessing computational fractional flow reserve from optical coherence tomography in patients with intermediate coronary stenosis in the left anterior descending artery. Circ Cardiovasc Interv. https://doi.org/10.1161/CIRCINTERVENTIONS.116.003613

    Article  PubMed  Google Scholar 

  28. Jang SJ, Ahn JM, Kim B et al (2017) Comparison of accuracy of one-use methods for calculating fractional flow reserve by intravascular optical coherence tomography to that determined by the pressure-wire method. Am J Cardiol. https://doi.org/10.1016/j.amjcard.2017.08.010

    Article  PubMed  Google Scholar 

  29. Siogkas PK, Papafaklis MI, Lakkas L et al (2019) Virtual functional assessment of coronary stenoses using intravascular ultrasound imaging: a proof-of-concept Pilot Study. Heart Lung Circ. https://doi.org/10.1016/j.hlc.2018.02.011

    Article  PubMed  Google Scholar 

  30. Yu W, Huang J, Jia D et al (2019) Diagnostic accuracy of intracoronary optical coherence tomography-derived fractional flow reserve for assessment of coronary stenosis severity. EuroIntervention. https://doi.org/10.4244/eij-d-19-00182

    Article  PubMed  Google Scholar 

  31. Huang J, Emori H, Ding D et al (2020) Comparison of diagnostic performance of intracoronary optical coherence tomography-based and angiography-based fractional flow reserve for evaluation of coronary stenosis. EuroIntervention. https://doi.org/10.4244/EIJ-D-19-01034

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

HS is funded by research funds of the British Heart Foundation (Project Grant: PG/17/18/32883), TZ by the Swiss National Science Foundation (Grant Number: 323530-171146), AR by research funds of Whipps Cross University Hospital while AB, AM and CB are supported by the Barts NIHR Biomedical Research Centre.

Author information

Authors and Affiliations

  1. Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

    Hannah Safi, Christos V. Bourantas, Anantharaman Ramasamy, Thomas Zanchin, Vincenzo Tufaro, Chongying Jin, Anthony Mathur & Andreas Baumbach

  2. Institute of Cardiovascular Sciences, University College London, London, UK

    Hannah Safi, Christos V. Bourantas & Alexandra Lansky

  3. William Harvey Research Institute, Queen Mary University London, London, UK

    Christos V. Bourantas, Anantharaman Ramasamy, Anthony Mathur & Andreas Baumbach

  4. Department of Cardiology, Bern University Hospital, Bern, Switzerland

    Thomas Zanchin, Sarah Bär, Stephan Windecker & Lorenz Räber

  5. Department of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Chongying Jin

  6. Department of Mechanical Engineering, University College London, London, UK

    Ryo Torii

  7. CTU Bern, Institute of Social and Preventive Medicine, Bern University, Bern, Switzerland

    Alexios Karagiannis

  8. Medis Medical Imaging Systems bv, Leiden, The Netherlands

    Johan H. C. Reiber

  9. Department of Interventional Cardiology, Thoraxcenter, Erasmus Medical Center, Rotterdam, The Netherlands

    Yoshinubo Onuma

  10. Division of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Alexandra Lansky

  11. Department of Cardiology, Columbia University Medical Center and the Cardiovascular Research Foundation, New York, NY, USA

    Akiko Maehara & Gregg W. Stone

  12. Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, UK

    Patrick W. Serruys

  13. Cardiovascular Division, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Peter Stone

Authors
  1. Hannah Safi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christos V. Bourantas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anantharaman Ramasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Zanchin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarah Bär
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vincenzo Tufaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chongying Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ryo Torii
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alexios Karagiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Johan H. C. Reiber
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Anthony Mathur
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yoshinubo Onuma
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Stephan Windecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Alexandra Lansky
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Akiko Maehara
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Patrick W. Serruys
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Peter Stone
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Andreas Baumbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Gregg W. Stone
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Lorenz Räber
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

Corresponding author

Correspondence to Christos V. Bourantas.

Ethics declarations

Conflict of interest

PWS, GWS and AK have received personal fees from Philips/Volcano and Abbott Vascular, CVB from Philips/Volcano, SW from Abbott and LR from Abbott, Amgen, AstraZeneca, CSL Behring, Sanofi, and Vifor and institutional fees from Abbott, Biotronik, Boston Scientific, Heartflow, Sanofi and Regeneron. JHCR is the CEO of Medis Medical Imaging Systems. None of the other authors have a conflict of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safi, H., Bourantas, C.V., Ramasamy, A. et al. Predictive value of the QFR in detecting vulnerable plaques in non-flow limiting lesions: a combined analysis of the PROSPECT and IBIS-4 study. Int J Cardiovasc Imaging 36, 993–1002 (2020). https://doi.org/10.1007/s10554-020-01805-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-020-01805-9

Keywords

Search

Navigation