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Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study

Abstract

Objective

This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions.

Methods

Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning–based FFRCT, and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant.

Results

This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFRCT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFRCT (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFRCT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093).

Conclusions

Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy.

Key Points

• Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions.

• Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning–based FFR CT for predicting ischemic coronary stenosis.

• No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features.

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Abbreviations

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

DS:

Diameter stenosis

FAI:

Fat attenuation index

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

LAP:

Low-attenuation plaque

LL:

Lesion length

NRS:

Napkin-ring sign

PR:

Positive remodeling

TPV:

Total plaque volume

References

  1. 1.

    Budoff MJ, Dowe D, Jollis JG et al (2008) Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 52:1724–1732

  2. 2.

    Meijboom WB, Meijs MF, Schuijf JD et al (2008) Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. J Am Coll Cardiol 52:2135–2144

  3. 3.

    Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336

  4. 4.

    Motoyama S, Sarai M, Harigaya H et al (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54(1):49–57

  5. 5.

    Otsuka K, Fukuda S, Tanaka A et al (2013) Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome. JACC Cardiovasc Imaging 6(4):448–457

  6. 6.

    Hoffmann U, Moselewski F, Nieman K et al (2006) Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by multidetector computed tomography. J Am Coll Cardiol 47:1655–1662

  7. 7.

    Hadamitzky M, Freismith B, Meyer T et al (2009) Prognostic value of coronary computed tomographic angiography for prediction of cardiac events in patients with suspected coronary artery disease. JACC Cardiovasc Imaging 2:404–411

  8. 8.

    Min JK, Shaw LJ, Devereux RB et al (2007) Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol 50:1161–1170

  9. 9.

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

  10. 10.

    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

  11. 11.

    Antonopoulos AS, Sanna F, Sabharwal N et al (2017) Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med 9:eaal2658

  12. 12.

    Oikonomou EK, Marwan M, Desai MY et al (2018) Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 392(10151):929–939

  13. 13.

    Goeller M, Achenbach S, Cadet S et al (2018) Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease. JAMA Cardiol 3(9):858–863

  14. 14.

    Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121(1):42–52

  15. 15.

    Pijls NH, De Bruyne B, Peels K et al (1996) Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med 334(26):1703–1708

  16. 16.

    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845

  17. 17.

    Grant RW, Stephens JM (2015) Fat in flames: influence of cytokines and pattern recognition receptors on adipocyte lipolysis. Am J Physiol Endocrinol Metab 309(3):E205–E213

  18. 18.

    Lavi S, McConnell JP, Rihal CS et al (2007) Local production of lipoprotein-associated phospholipase A2 and lysophosphatidylcholine in the coronary circulation: association with early coronary atherosclerosis and endothelial dysfunction in humans. Circulation 115(21):2715–2721

  19. 19.

    Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288(1):64–72

  20. 20.

    Coenen A, Kim YH, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging 11(6):e007217

  21. 21.

    Li M, Zhang J, Pan J et al (2013) Coronary stenosis: morphologic index characterized by using CT angiography correlates with fractional flow reserve and is associated with hemodynamic status. Radiology 269(3):713–721

  22. 22.

    Yu M, Zhao Y, Li W et al (2018) Relationship of the Duke jeopardy score combined with minimal lumen diameter as assessed by computed tomography angiography to the hemodynamic relevance of coronary artery stenosis. J Cardiovasc Comput Tomogr 12(3):247–254

  23. 23.

    Yu M, Lu Z, Li W et al (2018) CT morphological index provides incremental value to machine learning based CT-FFR for predicting hemodynamically significant coronary stenosis. Int J Cardiol 265:256–261

  24. 24.

    Waksman R, Legutko J, Singh J et al (2013) FIRST: fractional flow reserve and intravascular ultrasound relationship study. J Am Coll Cardiol 61:917–923

  25. 25.

    Brugaletta S, Garcia-Garcia HM, Shen ZJ et al (2012) Morphology of coronary artery lesions assessed by virtual histology intravascular ultrasound tissue characterization and fractional flow reserve. Int J Cardiovasc Imaging 28:221–228

  26. 26.

    Ahmadi A, Stone GW, Leipsic J et al (2016) Association of coronary stenosis and plaque morphology with fractional flow reserve and outcomes. JAMA Cardiol 1(3):350–357

  27. 27.

    Gaur S, Øvrehus 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 13:1220–1227

  28. 28.

    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(6):2655–2664

  29. 29.

    von KnebelDoeberitz PL, De Cecco CN, Schoepf UJ et al (2018) Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol. https://doi.org/10.1007/s00330-018-5834-z

  30. 30.

    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

  31. 31.

    van Hamersvelt RW, Zreik M, Voskuil M et al (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

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Author information

Correspondence to Jiayin Zhang.

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Guarantor

The scientific guarantor of this publication is Dr. Jiayin Zhang.

Conflict of interest

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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by hospital IRB.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The current study has a major overlap of study cohorts (151 patients) with one previously accepted article published in European Radiology. However, the aims of these two studies were completely different. The current study is focusing on the perivascular fat attenuation index whereas the previous study was focusing on quantified subtended myocardial mass.

Methodology

• retrospective

• comparative study

• performed at one institution

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Yu, M., Dai, X., Deng, J. et al. Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study. Eur Radiol 30, 673–681 (2020) doi:10.1007/s00330-019-06400-8

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Keywords

  • Adipose tissue
  • Atheroma
  • Computed tomography angiography
  • Coronary artery disease
  • Myocardial fractional flow reserve