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Additive value of epicardial adipose tissue quantification to coronary CT angiography–derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia

  • Cardiac
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Abstract

Objectives

Epicardial adipose tissue (EAT) from coronary CT angiography (CCTA) is strongly associated with coronary artery disease (CAD). We investigated the additive value of EAT volume to coronary plaque quantification and CT-derived fractional flow reserve (CT-FFR) to predict lesion-specific ischemia.

Methods

Patients (n = 128, 60.6 ± 10.5 years, 61% male) with suspected CAD who had undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. EAT volume and plaque measures were derived from CCTA using a semi-automatic software approach, while CT-FFR was calculated using a machine learning algorithm. The predictive value and discriminatory power of EAT volume, plaque measures, and CT-FFR to identify ischemic CAD were assessed using invasive FFR as the reference standard.

Results

Fifty-five of 152 lesions showed ischemic CAD by invasive FFR. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in lesions with and without hemodynamic significance (all p < 0.05). Multivariate analysis revealed predictive value for lesion-specific ischemia of these parameters: EAT volume (OR 2.93, p = 0.021), CCTA ≥ 50% (OR 4.56, p = 0.002), and CT-FFR (OR 6.74, p < 0.001). ROC analysis demonstrated incremental discriminatory value with the addition of EAT volume to plaque measures alone (AUC 0.84 vs. 0.62, p < 0.05). CT-FFR (AUC 0.89) showed slightly superior performance over EAT volume with plaque measures (AUC 0.84), however without significant difference (p > 0.05).

Conclusions

EAT volume is significantly associated with ischemic CAD. The combination of EAT volume with plaque quantification demonstrates a predictive value for lesion-specific ischemia similar to that of CT-FFR. Thus, EAT may aid in the identification of hemodynamically significant coronary stenosis.

Key Points

• CT-derived EAT volume quantification demonstrates high discriminatory power to identify lesion-specific ischemia.

• EAT volume shows incremental diagnostic performance over CCTA-derived plaque measures in detecting lesion-specific ischemia.

• A combination of plaque measures with EAT volume provides a similar discriminatory value for detecting lesion-specific ischemia compared to CT-FFR.

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Abbreviations

ACE:

Angiotensin-converting enzyme

AI:

Artificial intelligence

ASCVD:

Atherosclerotic cardiovascular disease

AUC:

Area under the curve

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CT-FFR:

CT-derived fractional flow reserve

EAT:

Epicardial adipose tissue

HU:

Hounsfield unit

ICA:

Invasive coronary angiography

ICC:

Interclass correlation coefficient

IDI:

Integrated discrimination improvement

MDCT:

Multidetector computed tomography

ML:

Machine learning

NPV:

Negative predictive value

NRI:

Net reclassification improvement

OR:

Odds ratio

PPV:

Positive predictive value

ROC:

Receiver operating characteristics

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Authors and Affiliations

  1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA

    Verena Brandt, Josua Decker, U. Joseph Schoepf, Akos Varga-Szemes, Tilman Emrich, Gilberto Aquino, Richard R. Bayer 2nd, Landin Carson, Allison Sullivan, Lauren Ellis, Philipp L. von Knebel Doeberitz, Ullrich Ebersberger & Christian Tesche

  2. Department of Cardiology and Angiology, Robert-Bosch-Hospital, Stuttgart, Germany

    Verena Brandt & Raffi Bekeredjian

  3. Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany

    Josua Decker

  4. Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany

    Tilman Emrich

  5. German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Mainz, Germany

    Tilman Emrich

  6. Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany

    Philipp L. von Knebel Doeberitz

  7. Kardiologie MVZ München-Nord, Munich, Germany

    Ullrich Ebersberger

  8. Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany

    Ullrich Ebersberger & Christian Tesche

  9. Department of Cardiology, Clinic Augustinum Munich, Munich, Germany

    Christian Tesche

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  1. Verena Brandt
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  2. Josua Decker
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  13. Raffi Bekeredjian
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  14. Christian Tesche
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Corresponding author

Correspondence to U. Joseph Schoepf.

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Guarantor

The scientific guarantor of this publication is Prof. U. Joseph Schoepf.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

No funding was received. Dr. Schoepf receives institutional research support and/or honoraria for speaking and consulting from Bayer, Bracco, Elucid BioImaging, General Electric, Guerbet, HeartFlow Inc., Keya Medical, and Siemens Healthineers. Dr. Tesche has received speaker’s fees from Siemens Healthineers and HeartFlow Inc. Dr. Varga-Szemes receives institutional research and travel support from Siemens Healthineers and is a consultant for Bayer and Elucid BioImaging. Dr. Emrich receives travel support and speaker fee from Siemens Healthineers. The other authors have no conflicts of interest to disclose.

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Brandt, V., Decker, J., Schoepf, U.J. et al. Additive value of epicardial adipose tissue quantification to coronary CT angiography–derived plaque characterization and CT fractional flow reserve for the prediction of lesion-specific ischemia. Eur Radiol 32, 4243–4252 (2022). https://doi.org/10.1007/s00330-021-08481-w

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  • DOI: https://doi.org/10.1007/s00330-021-08481-w

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