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Prediction value of pericoronary fat attenuation index for coronary in-stent restenosis

  • Cardiac
  • Published:
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Abstract

Objectives

As a novel imaging marker, pericoronary fat attenuation index (FAI) reflects the local coronary inflammation which is one of the major mechanisms for in-stent restenosis (ISR). We aimed to validate the ability of pericoronary FAI to predict ISR in patients undergoing percutaneous coronary intervention (PCI).

Materials and methods

Patients who underwent coronary CT angiography (CCTA) before PCI within 1 week between January 2017 and December 2019 at our hospital and had follow-up invasive coronary angiography (ICA) or CCTA were enrolled. Pericoronary FAI was measured at the site where stents would be placed. ISR was defined as ≥ 50% diameter stenosis at follow-up ICA or CCTA in the in-stent area. Multivariable analysis using mixed effects logistic regression models was performed to test the association between pericoronary FAI and ISR at lesion level.

Results

A total of 126 patients with 180 target lesions were included in the study. During 22.5 months of mean interval time from index PCI to follow-up ICA or CCTA, ISR occurred in 40 (22.2%, 40/180) stents. Pericoronary FAI was associated with a higher risk of ISR (adjusted OR = 1.12, p = 0.028). The optimum cutoff was − 69.6 HU. Integrating the dichotomous pericoronary FAI into current state of the art prediction model for ISR improved the prediction ability of the model significantly (△area under the curve =  + 0.064; p = 0.001).

Conclusion

Pericoronary FAI around lesions with subsequent stent placement is independently associated with ISR and could improve the ability of current prediction model for ISR.

Clinical relevance statement

Pericoronary fat attenuation index can be used to identify the lesions with high risk for in-stent restenosis. These lesions may benefit from extra anti-inflammation treatment to avoid in-stent restenosis.

Key Points

• Pericoronary fat attenuation index reflects the local coronary inflammation.

• Pericoronary fat attenuation index around lesions with subsequent stents placement can predict in-stent restenosis.

• Pericoronary fat attenuation index can be used as a marker for future in-stent restenosis.

Graphical Abstract

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Abbreviations

CCTA:

Coronary CT angiography

DES:

Drug-eluting stents

FAI:

Fat attenuation index

hsCRP:

High-sensitivity C-reactive protein

ICA:

Invasive coronary angiography

ISR:

In-stent restenosis

PCAT:

Pericoronary adipose tissue

PCI:

Percutaneous coronary intervention

References

  1. Windecker S, Serruys PW, Wandel S et al (2008) Biolimus-eluting stent with biodegradable polymer versus sirolimus-eluting stent with durable polymer for coronary revascularisation (LEADERS): a randomised non-inferiority trial. Lancet 372:1163–1173

    Article  PubMed  CAS  Google Scholar 

  2. Dangas GD, Claessen BE, Caixeta A, Sanidas EA, Mintz GS, Mehran R (2010) In-stent restenosis in the drug-eluting stent era. J Am Coll Cardiol 56:1897–1907

    Article  PubMed  Google Scholar 

  3. Moussa ID, Mohananey D, Saucedo J et al (2020) Trends and outcomes of restenosis after coronary stent implantation in the United States. J Am Coll Cardiol 76:1521–1531

    Article  PubMed  Google Scholar 

  4. Niccoli G, Montone RA, Sabato V, Crea F (2018) Role of allergic inflammatory cells in coronary artery disease. Circulation 138:1736–1748

    Article  PubMed  CAS  Google Scholar 

  5. Borovac JA, D’Amario D, Vergallo R et al (2019) Neoatherosclerosis after drug-eluting stent implantation: a novel clinical and therapeutic challenge. Eur Heart J Cardiovasc Pharmacother 5:105–116

    Article  PubMed  Google Scholar 

  6. Shlofmitz E, Iantorno M, Waksman R (2019) Restenosis of drug-eluting stents: a new classification system based on disease mechanism to guide treatment and state-of-the-art review. Circ Cardiovasc Interv 12:e007023

    Article  PubMed  CAS  Google Scholar 

  7. Yi M, Wu L, Ke X (2022) Prognostic value of high-sensitivity C-reactive protein in in-stent restenosis: a meta-analysis of clinical trials. J Cardiovasc Dev Dis 9:247

  8. Yousuf O, Mohanty BD, Martin SS et al (2013) High-sensitivity C-reactive protein and cardiovascular disease: a resolute belief or an elusive link? J Am Coll Cardiol 62:397–408

    Article  PubMed  CAS  Google Scholar 

  9. Antonopoulos AS, Margaritis M, Coutinho P et al (2015) Adiponectin as a link between type 2 diabetes and vascular NADPH oxidase activity in the human arterial wall: the regulatory role of perivascular adipose tissue. Diabetes 64:2207–2219

    Article  PubMed  CAS  Google Scholar 

  10. Margaritis M, Antonopoulos AS, Digby J et al (2013) Interactions between vascular wall and perivascular adipose tissue reveal novel roles for adiponectin in the regulation of endothelial nitric oxide synthase function in human vessels. Circulation 127:2209–2221

    Article  PubMed  CAS  Google Scholar 

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

  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 392:929–939

    Article  PubMed  PubMed Central  Google Scholar 

  13. Goeller M, Tamarappoo BK, Kwan AC et al (2019) Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging 20:636–643

    Article  PubMed  PubMed Central  Google Scholar 

  14. 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 3:858–863

    Article  PubMed  PubMed Central  Google Scholar 

  15. Oikonomou EK, Williams MC, Kotanidis CP et al (2019) A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J 40:3529–3543

    Article  PubMed  PubMed Central  Google Scholar 

  16. Abbara S, Blanke P, Maroules CD et al (2016) SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: a report of the society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr 10:435–449

    Article  PubMed  Google Scholar 

  17. Pan J, Lu Z, Zhang J, Li M, Wei M (2013) Angiographic patterns of in-stent restenosis classified by computed tomography in patients with drug-eluting stents: correlation with invasive coronary angiography. Eur Radiol 23:101–107

    Article  PubMed  Google Scholar 

  18. Li Y, Yu M, Li W, Lu Z, Wei M, Zhang J (2018) Third generation dual-source CT enables accurate diagnosis of coronary restenosis in all size stents with low radiation dose and preserved image quality. Eur Radiol 28:2647–2654

    Article  PubMed  Google Scholar 

  19. 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

    Article  PubMed  Google Scholar 

  20. Pencina MJ, D’Agostino RB, Pencina KM, Janssens AC, Greenland P (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481

    Article  PubMed  PubMed Central  Google Scholar 

  21. Toutouzas K, Colombo A, Stefanadis C (2004) Inflammation and restenosis after percutaneous coronary interventions. Eur Heart J 25:1679–1687

    Article  PubMed  CAS  Google Scholar 

  22. Madjid M, Willerson JT, Casscells SW (2006) Intracoronary thermography for detection of high-risk vulnerable plaques. J Am Coll Cardiol 47:C80-85

    Article  PubMed  Google Scholar 

  23. Stefanadis C, Toutouzas K, Tsiamis E et al (2001) Increased local temperature in human coronary atherosclerotic plaques: an independent predictor of clinical outcome in patients undergoing a percutaneous coronary intervention. J Am Coll Cardiol 37:1277–1283

    Article  PubMed  CAS  Google Scholar 

  24. Gugliandolo E, Fusco R, Biundo F et al (2017) Palmitoylethanolamide and polydatin combination reduces inflammation and oxidative stress in vascular injury. Pharmacol Res 123:83–92

    Article  PubMed  CAS  Google Scholar 

  25. Wang R, Lu J, Yin J et al (2023) A TEMPOL and rapamycin loaded nanofiber-covered stent favors endothelialization and mitigates neointimal hyperplasia and local inflammation. Bioact Mater 19:666–677

    PubMed  CAS  Google Scholar 

  26. Park JH, Kim SW, Cha MJ et al (2018) TAK-733 inhibits inflammatory neointimal formation by suppressing proliferation, migration, and inflammation in vitro and in vivo. Exp Mol Med 50:1–12

    PubMed  PubMed Central  Google Scholar 

  27. Qiu H, Tu Q, Gao P et al (2021) Phenolic-amine chemistry mediated synergistic modification with polyphenols and thrombin inhibitor for combating the thrombosis and inflammation of cardiovascular stents. Biomaterials 269:120626

    Article  PubMed  CAS  Google Scholar 

  28. Ohyama K, Matsumoto Y, Takanami K et al (2018) Coronary adventitial and perivascular adipose tissue inflammation in patients with vasospastic angina. J Am Coll Cardiol 71:414–425

    Article  PubMed  Google Scholar 

  29. Antonopoulos AS, Antoniades C (2018) Perivascular fat attenuation index by computed tomography as a metric of coronary inflammation. J Am Coll Cardiol 71:2708–2709

    Article  PubMed  Google Scholar 

  30. Ridker PM (2016) A Test in Context: High-Sensitivity C-Reactive Protein. J Am Coll Cardiol 67:712–723

    Article  PubMed  Google Scholar 

  31. Kastrati A, Dibra A, Mehilli J et al (2006) Predictive factors of restenosis after coronary implantation of sirolimus- or paclitaxel-eluting stents. Circulation 113:2293–2300

    Article  PubMed  Google Scholar 

  32. Giustino G, Colombo A, Camaj A et al (2022) Coronary in-stent restenosis: JACC state-of-the-art review. J Am Coll Cardiol 80:348–372

    Article  PubMed  Google Scholar 

Download references

Funding

This study has received funding by the National Natural Science Foundation of China (grant no.: 82102036) and the Ministry of Science and Technology of China, National key research and development project (grant no.: 2016YFC1300403).

Author information

Authors and Affiliations

  1. Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, #167 Bei-Li-Shi Street, Beijing, People’s Republic of China

    Zhong-Fei Lu, Wei-Hua Yin, Jing-Wen Ma, Li Zhao, Yun-Qiang An & Bin Lu

  2. Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, #20 Yuhuangdingdong Street, Yantai, People’s Republic of China

    Zhong-Fei Lu

  3. Department of Radiology and Radiological Science and Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf & Sameer Abrol

  4. National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, #167 Bei-Li-Shi Street, Beijing, People’s Republic of China

    Xiao-Ming Su

  5. Department of Cardiology, Yantai Yuhuangding Hospital, Qingdao University, #20 Yuhuangdingdong Street, Yantai, People’s Republic of China

    Zhi-Cheng Xiao

Authors
  1. Zhong-Fei Lu
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  2. Wei-Hua Yin
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  4. Sameer Abrol
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  6. Li Zhao
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  9. Zhi-Cheng Xiao
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  10. Bin Lu
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Corresponding author

Correspondence to Bin Lu.

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Guarantor

The scientific guarantor of this publication is Bin Lu.

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

One of the authors (Xiao-Ming Su) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Study subjects or cohorts have not been previously reported.

Methodology

• retrospective

• prognostic study

• performed at one institution

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Lu, ZF., Yin, WH., Schoepf, U.J. et al. Prediction value of pericoronary fat attenuation index for coronary in-stent restenosis. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10527-0

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  • DOI: https://doi.org/10.1007/s00330-023-10527-0

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