Skip to main content

Advertisement

Log in

Analysis of the perivascular fat attenuation index and quantitative plaque parameters in relation to haemodynamically impaired myocardial ischaemia

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

Abstract

To investigate the correlation between quantitative plaque parameters, the perivascular fat attenuation index, and myocardial ischaemia caused by haemodynamic impairment. Patients with stable angina who had invasive flow reserve fraction (FFR) assessment and coronary artery computed tomography (CT) angiography were retrospectively enrolled. A total of 138 patients were included in this study, which were categorized into the FFR < 0.75 group (n = 43), 0.75 ≤ FFR ≤ 0.8 group (n = 37), and FFR > 0.8 group (n = 58), depending on the range of FFR values. The perivascular FAI and CTA-derived parameters, including plaque length (PL), total plaque volume (TPV), minimum lumen area (MLA), and narrowest degree (ND), were recorded for the lesions. An FFR < 0.75 was defined as myocardial-specific ischaemia. The relationships between myocardial ischaemia and parameters such as the PL, TPV, MLA, ND, and FAI were analysed using a logistic regression model and receiver operating characteristic (ROC) curves to compare the diagnostic accuracy of various indicators for myocardial ischaemia. The PL, TPV, ND, and FAI were greater in the FFR < 0.75 group than in the grey area group and the FFR > 0.80 group (all p < 0.05). The MLA in the FFR < 0.75 group was lower than that in the grey area group and the FFR > 0.80 group (both P < 0.05). There were no significant differences in the PL, TPV, or ND between the grey area and the FFR > 0.80 group, but there was a significant difference in the FAI. The coronary artery lesions with FFRs ≤ 0.75 had the greatest FAI values. Multivariate analysis revealed that the perivascular FAI and PL density are significant predictors of myocardial ischaemia. The FAI has some predictive value for myocardial ischaemia (AUC = 0.781). After building a combination model using the FAI and plaque length, the predictive power increased (AUC, 0.781 vs. 0.918), and the change was statistically significant (P < 0.001). The combined model of PL + FAI demonstrated great diagnostic efficacy in identifying myocardial ischaemia caused by haemodynamic impairment; the lower the FFR was, the greater the FAI. Thus, the PL + FAI could be a combined measure to securely rule out myocardial ischaemia.

Graphical abstract

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Kim KH, Doh JH, Koo BK, Min JK, Erglis A, Yang HM 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(1):72–78. https://doi.org/10.1016/j.jcin.2013.05.024

    Article  PubMed  Google Scholar 

  2. Johnson TR, Nikolaou K, Busch S, Leber AW, Becker A, Wintersperger BJ et al (2007) Diagnostic accuracy of dual-source computed tomography in the diagnosis of coronary artery disease. Invest Radiol 42(10):684–691. https://doi.org/10.1097/RLI.0b013e31806907d0

    Article  PubMed  Google Scholar 

  3. Motoyama S, Sarai M, Harigaya H, Anno H, Inoue K, Hara T et al (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54(1):49–57. https://doi.org/10.1016/j.jacc.2009.02.068

    Article  PubMed  Google Scholar 

  4. Von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Duguay TM, Albrecht MH, van Assen M 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. https://doi.org/10.1007/s00330-018-5834-z

    Article  Google Scholar 

  5. Gaur S, Øvrehus KA, Dey D, Leipsic J, Bøtker HE, Jensen JM et al (2016) Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J 37(15):1220–1227. https://doi.org/10.1093/eurheartj/ehv690

    Article  PubMed  PubMed Central  Google Scholar 

  6. Driessen RS, Stuijfzand WJ, Raijmakers PG, Danad I, Min JK, Leipsic JA et al (2018) Effect of plaque burden and morphology on myocardial blood flow and fractional flow reserve. J Am Coll Cardiol 71(5):499–509. https://doi.org/10.1016/j.jacc.2017.11.054

    Article  PubMed  Google Scholar 

  7. Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M (2022) Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther 7(1):131. https://doi.org/10.1038/s41392-022-00955-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Goeller M, Achenbach S, Duncker H, Dey D, Marwan M (2021) Imaging of the pericoronary adipose tissue (PCAT) using cardiac computed tomography: modern clinical implications. J Thorac Imaging 36(3):149–161. https://doi.org/10.1097/rti.0000000000000583

    Article  PubMed  Google Scholar 

  9. Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L et al (2017) Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. https://doi.org/10.1126/scitranslmed.aal2658

    Article  PubMed  Google Scholar 

  10. Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Hutt Centeno E 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. https://doi.org/10.1016/s0140-6736(18)31114-0

    Article  PubMed  PubMed Central  Google Scholar 

  11. Goeller M, Achenbach S, Cadet S, Kwan AC, Commandeur F, Slomka PJ 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. https://doi.org/10.1001/jamacardio.2018.1997

    Article  PubMed  PubMed Central  Google Scholar 

  12. Pijls NH, van Schaardenburgh P, Manoharan G, Boersma E, Bech JW, van’t Veer M et al (2007) Percutaneous coronary intervention of functionally nonsignificant stenosis: 5-year follow-up of the DEFER Study. J Am Coll Cardiol. 49(21):2105–2111. https://doi.org/10.1016/j.jacc.2007.01.087

    Article  PubMed  Google Scholar 

  13. Antoniades C, Shirodaria C, Warrick N, Cai S, de Bono J, Lee J et al (2006) 5-methyltetrahydrofolate rapidly improves endothelial function and decreases superoxide production in human vessels: effects on vascular tetrahydrobiopterin availability and endothelial nitric oxide synthase coupling. Circulation 114(11):1193–1201. https://doi.org/10.1161/circulationaha.106.612325

    Article  CAS  PubMed  Google Scholar 

  14. Margaritis M, Sanna F, Lazaros G, Akoumianakis I, Patel S, Antonopoulos AS et al (2017) Predictive value of telomere length on outcome following acute myocardial infarction: evidence for contrasting effects of vascular vs. blood oxidative stress. Eur Heart J 38(41):3094–3104. https://doi.org/10.1093/eurheartj/ehx177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Antoniades C, Kotanidis CP, Berman DS (2019) State-of-the-art review article. Atherosclerosis affecting fat: What can we learn by imaging perivascular adipose tissue. J Cardiovasc Comput Tomogr 13(5):288–296. https://doi.org/10.1016/j.jcct.2019.03.006

    Article  PubMed  PubMed Central  Google Scholar 

  16. Libby P, Ridker PM, Maseri A (2002) Inflammation and atherosclerosis. Circulation 105(9):1135–1143. https://doi.org/10.1161/hc0902.104353

    Article  CAS  PubMed  Google Scholar 

  17. Ross R (1999) Atherosclerosis—an inflammatory disease. N Engl J Med 340(2):115–126. https://doi.org/10.1056/nejm199901143400207

    Article  CAS  PubMed  Google Scholar 

  18. 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):E205-213. https://doi.org/10.1152/ajpendo.00053.2015

    Article  CAS  PubMed  Google Scholar 

  19. Hoshino M, Yang S, Sugiyama T, Zhang J, Kanaji Y, Yamaguchi M et al (2020) Peri-coronary inflammation is associated with findings on coronary computed tomography angiography and fractional flow reserve. J Cardiovasc Comput Tomogr 14(6):483–489. https://doi.org/10.1016/j.jcct.2020.02.002

    Article  PubMed  Google Scholar 

  20. Gaibazzi N, Martini C, Botti A, Pinazzi A, Bottazzi B, Palumbo AA (2019) Coronary inflammation by computed tomography pericoronary fat attenuation in MINOCA and Tako-Tsubo syndrome. J Am Heart Assoc 8(17):e013235. https://doi.org/10.1161/jaha.119.013235

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yu M, Dai X, Deng J, Lu Z, Shen C, Zhang J (2020) Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study. Eur Radiol 30(2):673–681. https://doi.org/10.1007/s00330-019-06400-8

    Article  PubMed  Google Scholar 

  22. Kanaji Y, Hirano H, Sugiyama T, Hoshino M, Horie T, Misawa T et al (2020) Pre-percutaneous coronary intervention pericoronary adipose tissue attenuation evaluated by computed tomography predicts global coronary flow reserve after urgent revascularization in patients with non-ST-segment-elevation acute coronary syndrome. J Am Heart Assoc 9(17):e016504. https://doi.org/10.1161/jaha.120.016504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kurata A, Fukuyama N, Hirai K, Kawaguchi N, Tanabe Y, Okayama H et al (2019) On-site computed tomography-derived fractional flow reserve using a machine-learning algorithm—clinical effectiveness in a retrospective multicenter cohort. Circ J 83(7):1563–1571. https://doi.org/10.1253/circj.CJ-19-0163

    Article  CAS  PubMed  Google Scholar 

  24. Doris MK, Otaki Y, Arnson Y, Tamarappoo B, Goeller M, Gransar H et al (2018) Non-invasive fractional flow reserve in vessels without severe obstructive stenosis is associated with coronary plaque burden. J Cardiovasc Comput Tomogr 12(5):379–384. https://doi.org/10.1016/j.jcct.2018.05.003

    Article  PubMed  Google Scholar 

  25. Ahmadi A, Stone GW, Leipsic J, Serruys PW, Shaw L, Hecht H et al (2016) Association of coronary stenosis and plaque morphology with fractional flow reserve and outcomes. JAMA Cardiol 1(3):350–357. https://doi.org/10.1001/jamacardio.2016.0263

    Article  PubMed  Google Scholar 

  26. Waksman R, Legutko J, Singh J, Orlando Q, Marso S, Schloss T et al (2013) FIRST: fractional flow reserve and intravascular ultrasound relationship study. J Am Coll Cardiol 61(9):917–923. https://doi.org/10.1016/j.jacc.2012.12.012

    Article  PubMed  Google Scholar 

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Contributions

The authors listed below have contributed signifcantly to the submitted work. YL: Wrote the main manuscript text, data collection and design of the study. RG: conception and design of the study, performing the date analysis. KJ: Collection of FFR data. JA, JL, PF: Post processing analysis of CTA images. JM has took part in conception and design of the study. All authors read and approved the fnal manuscript.

Corresponding author

Correspondence to Jing Ma.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Long, Y., Guo, R., Jin, K. et al. Analysis of the perivascular fat attenuation index and quantitative plaque parameters in relation to haemodynamically impaired myocardial ischaemia. Int J Cardiovasc Imaging (2024). https://doi.org/10.1007/s10554-024-03122-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10554-024-03122-x

Keywords

Navigation