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Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis

  • Cardiac
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Abstract

Objectives

A method named computed tomography angiography-derived fractional flow reserve (FFRCT) is an alternative method for detecting hemodynamically significant coronary stenosis. We carried out a meta-analysis to derive reliable assessment of the diagnostic performances of FFRCT and compare the diagnostic accuracy with CCTA using FFR as reference.

Methods

We searched PubMed, EMBASE, The Cochrane Library, and Web of science for relevant articles published from January 2008 until May 2019 using the following search terms: FFRCT, noninvasive FFR, non-invasive FFR, noninvasive fractional flow reserve, non-invasive fractional flow reserve, and CCTA. Pooled estimates of sensitivity and specificity with the corresponding 95% confidence intervals (CIs) and the summary receiver operating characteristic curve (sROC) were determined.

Results

Sixteen studies published between 2011 and 2019 were included with a total of 1852 patients and 2731 vessels. The pooled sensitivity and specificity for FFRCT at the per-patient level was 89% (95% CI, 85–92%) and 71% (95% CI, 61–80%), respectively, while on the per-vessel basis was 85% (95% CI, 82–88%) and 82% (95% CI, 75–87%), respectively. No apparent difference in the sensitivity at per-patient and per-vessel level between FFRCT and CCTA was observed (0.89 versus 0.93 at per-patient; 0.85 versus 0.88 at per-vessel). However, the specificity of FFRCT was higher than CCTA (0.71 versus 0.32 at per-patient analysis; 0.82 versus 0.46 at per-vessel analysis).

Conclusions

FFRCT obtained a high diagnostic performance and is a viable alternative to FFR for detecting coronary ischemic lesions.

Key Points

• Noninvasive FFRCThas higher specificity for anatomical and physiological assessment of coronary artery stenosis compared with CCTA.

• Noninvasive FFRCTis a viable alternative to invasive FFR for the detection and exclusion of coronary lesions that cause ischemia.

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Abbreviations

AUC:

Area under the SROC

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CIs:

Confidence intervals

CMR:

Cardiovascular magnetic resonance

CTP:

Computed tomography perfusion

FFR:

Fractional flow reserve

FFRCT:

Computed tomography angiography-derived fractional flow reserve

FN:

False negative

FP:

False positive

I2 :

Inconsistency index

ICA:

Invasive coronary angiography

LR−:

Negative likelihood ratio

LR+:

Positive likelihood ratio

NPV:

Negative predictive value

PPV:

Positive predictive value

SPECT:

Single-photon emission computed tomography

SROC:

Summary receiver operating characteristic curve

TN:

True negative

TP:

True positive

References

  1. Laslett LJ, Alagona P Jr, Clark BA 3rd et al (2012) The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the American College of Cardiology. J Am Coll Cardiol 60:S1eS49

    Google Scholar 

  2. Mozaffarian D, Benjamin EJ, Go AS et al (2016) Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation 133:e38iseas

  3. De Bruyne B, Pijls NH, Kalesan B et al (2012) Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N Engl J Med 367:991–1001

    PubMed  Google Scholar 

  4. Task Force Members, Montalescot G, Sechtem U et al (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34:2949–3003

    Google Scholar 

  5. Matsumura M, Johnson NP, Fearon WF et al (2017) Accuracy of fractional flow reserve measurements in clinical practice: observations from a core laboratory analysis. JACC Cardiovasc Interv 10:1392–1401

    PubMed  Google Scholar 

  6. Xu B, Whitbourn R, Wilson A et al (2014) Clinical impact of fractional flow reserve in a real-world cohort of patients. Int J Cardiol 172:251–252

    PubMed  Google Scholar 

  7. Min JK, Feignoux J, Treutenaere J, Laperche T, Sablayrolles J (2010) The prognostic value of multidetector coronary CT angiography for the prediction of major adverse cardiovascular events: a multicenter observational cohort study. Int J Cardiovasc Imaging 26:721–728

    PubMed  Google Scholar 

  8. Andrew M, John H (2015) The challenge of coronary calcium on coronary computed tomographic angiography (CCTA) scans: effect on interpretation and possible solutions. Int J Cardiovasc Imaging 31:145–157

    PubMed  Google Scholar 

  9. Arbab-Zadeh A, Hoe J (2011) Quantification of coronary arterial stenoses by multidetector CT angiography in comparison with conventional angiography methods, caveats, and implications. JACC Cardiovasc Imaging 4:191–202

    PubMed  Google Scholar 

  10. Taylor CA, Fonte TA, Min JK (2013) Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve. J Am Coll Cardiol 61:2233–2241

    PubMed  Google Scholar 

  11. Gaur S, Ovrehus KA, Dey D et al (2016) Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J 37:1220–1227

    PubMed  PubMed Central  Google Scholar 

  12. Kim KH, Doh JH, Koo BK et al (2014) A novel noninvasive technology for treatment planning using virtual coronary stenting and computed tomography-derived computed fractional flow reserve. JACC Cardiovasc Interv 7:72–78

    PubMed  Google Scholar 

  13. Norgaard BL, Leipsic J, Koo BK et al (2016) Coronary computed tomography angiography derived fractional flow reserve and plaque stress. Curr Cardiovasc Imaging Rep 9:2

    PubMed  PubMed Central  Google Scholar 

  14. Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the Prisma statement. Ann Intern Med 4(264-9):W64

    Google Scholar 

  15. Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies 1. Radiology 272:826–832

    Google Scholar 

  16. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58(10):982–990

    PubMed  Google Scholar 

  17. Higgins JP, Thompson SG, Deek JJ et al (2003) Measuring inconsistency in meta-analyses. BMJ 327:557in60

    Google Scholar 

  18. Irwig L, Tosteson AN, Gatsonis C et al (1994) Guidelines for meta-analyses evaluating diagnostic tests. Ann Intern Med 120:667es f

    Google Scholar 

  19. Jones CM, Athanasiou T (2005) Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. Ann Thorac Surg 79:16–20

    PubMed  Google Scholar 

  20. Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58:882–893

    PubMed  Google Scholar 

  21. Jaeschke R, Guyatt GH, Sackett DL (1994) Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA 271(9):703–707

    CAS  PubMed  Google Scholar 

  22. Ko BS, Wong DT, Cameron JD et al (2014) 320-row CT coronary angiography predicts freedom from revascularisation and acts as a gatekeeper to defer invasive angiography in stable coronary artery disease: a fractional flow reserve-correlated study. Eur Radiol 24:738–747

    PubMed  Google Scholar 

  23. Wardziak L, Kruk M, Pleban W et al (2019) Coronary CTA enhanced with CTA based FFR analysis provides higher diagnostic value than invasive coronary angiography in patients with intermediate coronary stenosis. J Cardiovasc Comput Tomogr 13(1):62–67

    PubMed  Google Scholar 

  24. 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 58:1989–1997

    PubMed  Google Scholar 

  25. Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Norgaard 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 63:1145–1155

    PubMed  Google Scholar 

  27. Renker M, Schoepf UJ, Wang R et al (2014) Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol 114:1303–1308

    PubMed  Google Scholar 

  28. Wang R, Renker M, Schoepf UJ et al (2015) Diagnostic value of quantitative stenosis predictors with coronary CT angiography compared to invasive fractional flow reserve. Eur J Radiol 84:1509–1515

    PubMed  Google Scholar 

  29. Coenen A, Lubbers MM, Kurata A et al (2015) Fractional flow reserve computed from non-invasive CT angiography data: diagnostic performance of an on-site clinician operated computational fluid dynamics algorithm 1. Radiology 274:674–683

    PubMed  Google Scholar 

  30. Min JK, Koo BK, 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 110:971–976

    PubMed  Google Scholar 

  31. Wong DT, Ko BS, Cameron JD et al (2013) Transluminal attenuation gradient in coronary computed tomography angiography is a novel noninvasive approach to the identification of functionally significant coronary artery stenosis: a comparison with fractional flow reserve. J Am Coll Cardiol 61:1271–1279

    PubMed  Google Scholar 

  32. Tesche C, De Cecco CN, Caruso D et al (2016) Coronary CT angiography derived morphological and functional quantitative plaque markers correlated with invasive fractional flow reserve for detecting hemodynamically significant stenosis. J Cardiovasc Comput Tomogr 10:199–206

    PubMed  Google Scholar 

  33. Ko BS, Cameron JD, Munnur RK et al (2017) Noninvasive CT-derived FFR based on structural and fluid analysis: a comparison with invasive FFR for detection of functionally significant stenosis. JACC Cardiovasc Imaging 10:663–673

    PubMed  Google Scholar 

  34. Kruk M, Wardziak L, Demkow M et al (2016) Workstation-based calculation of CTA-based FFR for intermediate stenosis. JACC Cardiovasc Imaging 9:690–699

    PubMed  Google Scholar 

  35. Chung JH, Lee KE, Nam CW et al (2017) Diagnostic performance of a novel method for fractional flow reserve computed from noninvasive computed tomography angiography (NOVEL-FLOW Study). Am J Cardiol 120(3):362–368

    PubMed  Google Scholar 

  36. Rother J, Moshage M, Dey D et al (2018) Comparison of invasively measured FFR with FFR derived from coronary CT angiography for detection of lesion-specific ischemia: results from a PC-based prototype algorithm. J Cardiovasc Comput Tomogr 12(2):101–107

    PubMed  Google Scholar 

  37. Sand NPR, Veien KT, Nielsen SS et al (2018) Prospective comparison of FFR derived from coronary CT angiography with SPECT perfusion imaging in stable coronary artery disease: the ReASSESS study. JACC Cardiovasc Imaging 11(11):1640–1650

    PubMed  Google Scholar 

  38. von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ et al (2019) Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol 29(5):2378–2387

    Google Scholar 

  39. Abdulla J, Abildstrom SZ, Gotzsche O, Christensen E, Kober L, Torp-Pedersen C (2007) 64-multislice detector computed tomography coronary angiography as potential alternative to conventional coronary angiography: a systematic review and meta-analysis. Eur Heart J 28(24):3042–3050

    PubMed  Google Scholar 

  40. Schuijf JD, Achenbach S, de Feyter PJ, Bax JJ (2011) Current applications and limitations of coronary computed tomography angiography in stable coronary artery disease. Heart 97(4):330–337

    PubMed  Google Scholar 

  41. Yu M, Lu Z, Shen C et al (2019) The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFRCT, or high-risk plaque features? Eur Radiol 29(7):3647–3657

    PubMed  Google Scholar 

  42. Norgaard BL, Gaur S, Leipsic J et al (2015) 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 8(9):1045–1055

    PubMed  Google Scholar 

  43. Liu X, Wang Y, Zhang H et al (2019) Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography? Eur Radiol 29(7):3669–3677

    PubMed  Google Scholar 

  44. Cook CM, Petraco R, Shun-Shin MJ et al (2017) Diagnostic accuracy of computed tomography–derived fractional flow reserve. JAMA Cardiol 2(7):803

    PubMed  Google Scholar 

  45. Baumann S, Renker M, Hetjens S et al (2016) Comparison of coronary computed tomography angiography-derived vs invasive fractional flow reserve assessment: meta-analysis with subgroup evaluation of intermediate stenosis. Acad Radiol 23(11):1402–1411

    PubMed  Google Scholar 

  46. Celeng C, Leiner T, Maurovich-Horvat P et al (2018) Anatomical and functional computed tomography for diagnosing hemodynamically significant coronary artery disease: a metaanalysis. JACC Cardiovasc Imaging 12:1316–1325

  47. Gonzalez JA, Lipinski MJ, Flors L, Shaw PW, Kramer CM, Salerno M (2015) Meta-analysis of diagnostic performance of coronary computed tomography angiography, computed tomography perfusion, and computed tomography-fractional flow reserve in functional myocardial ischemia assessment versus invasive fractional flow reserve. Am J Cardiol 116:1469–1478

    PubMed  PubMed Central  Google Scholar 

  48. Deng SB, Jing XD, Wang J et al (2015) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in coronary artery disease: a systematic review and meta-analysis. Int J Cardiol 184:703–709

    PubMed  Google Scholar 

  49. Caruso D, Eid M, Schoepf UJ et al (2016) Dynamic CT myocardial perfusion imaging. Eur J Radiol 85:1893–1899

    PubMed  Google Scholar 

  50. Melikian N, De Bondt P, Tonino P et al (2010) Fractional flow reserve and myocardial perfusion imaging in patients with angiographic multivessel coronary artery disease. JACC Cardiovasc Interv 3:307–314

    PubMed  Google Scholar 

  51. Heydari B, Jerosch-Herold M, Kwong RY (2011) Assessment of myocardial ischemia with cardiovascular magnetic resonance. Prog Cardiovasc Dis 54:191–203

    PubMed  PubMed Central  Google Scholar 

  52. Bech GJ, De Bruyne B, Pijls NH et al (2001) Fractional flow reserve to determine the appropriateness of angioplasty in moderate coronary stenosis: a randomized trial. Circulation 103(24):2928–2934

    CAS  PubMed  Google Scholar 

  53. Tesche C, Vliegenthart R, Duguay TM et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol 120(12):2121–2127

    PubMed  Google Scholar 

  54. Leipsic J, Yang TH, Thompson A et al (2014) CT angiography (CTA) and diagnostic performance of noninvasive fractional flow reserve: results from the determination of fractional flow reserve by anatomic CTA (DeFACTO) Study. AJR Am J Roentgenol 202:989–994

    PubMed  Google Scholar 

  55. Siogkas PK, Anagnostopoulos CD, Liga R et al (2019) Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve. Eur Radiol 29(4):2117–2126

    PubMed  Google Scholar 

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Funding

This study has received funding by Research Grant of National Natural Science Foundation of China (81571647, 81971588, 81620108015, 81771811), and Capital Clinical Special Program (Z191100006619021).

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Author notes

  1. Dr. Baiyan Zhuang and Shuli Wang contributed equally to this work

Authors and Affiliations

  1. Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China

    Baiyan Zhuang, Shuli Wang, Shihua Zhao & Minjie Lu

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  1. Baiyan Zhuang
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  2. Shuli Wang
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Correspondence to Shihua Zhao or Minjie Lu.

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The scientific guarantor of this publication is Minjie Lu.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Zhuang, B., Wang, S., Zhao, S. et al. Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 30, 712–725 (2020). https://doi.org/10.1007/s00330-019-06470-8

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