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European Radiology
Published in: European Radiology 2/2020

01-02-2020 | CT Angiography | Cardiac

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

Authors: Mengmeng Yu, Xu Dai, Jianhong Deng, Zhigang Lu, Chengxing Shen, Jiayin Zhang

Published in: European Radiology | Issue 2/2020

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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.
Appendix
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Literature
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 52:1724–1732CrossRef
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 52:2135–2144CrossRef
3.
Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336CrossRef
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):49–57CrossRef
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):448–457CrossRef
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 47:1655–1662CrossRef
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 2:404–411CrossRef
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 50:1161–1170CrossRef
9.
Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245CrossRef
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):3647–3657CrossRef
11.
Antonopoulos AS, Sanna F, Sabharwal N et al (2017) Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med 9:eaal2658CrossRef
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):929–939CrossRef
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):858–863CrossRef
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):42–52CrossRef
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):1703–1708CrossRef
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):837–845CrossRef
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–E213CrossRef
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):2715–2721CrossRef
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):64–72CrossRef
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):e007217CrossRef
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):713–721CrossRef
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):247–254CrossRef
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 265:256–261CrossRef
24.
Waksman R, Legutko J, Singh J et al (2013) FIRST: fractional flow reserve and intravascular ultrasound relationship study. J Am Coll Cardiol 61:917–923CrossRef
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 28:221–228CrossRef
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):350–357CrossRef
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 13:1220–1227CrossRef
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):2655–2664CrossRef
29.
30.
31.
Metadata
Title
Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study
Authors
Mengmeng Yu
Xu Dai
Jianhong Deng
Zhigang Lu
Chengxing Shen
Jiayin Zhang
Publication date
01-02-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 2/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06400-8

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