Prognostic value of epicardial adipose tissue volume in combination with coronary plaque and flow assessment for the prediction of major adverse cardiac events

•CT-derived EAT assessment demonstrates high discriminatory power to predict MACE.•EAT shows superior diagnostic performance over Morise score, CCTA-derived plaque measures and CT-FFR.•A combined model of these markers demonstrated incremental MACE prediction beyond clinical risk score. The purpose...

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Published in:European journal of radiology 2022-03, Vol.148, p.110157-110157, Article 110157
Main Authors: Brandt, Verena, Bekeredjian, Raffi, Schoepf, U. Joseph, Varga-Szemes, Akos, Emrich, Tilman, Aquino, Gilberto J., Decker, Josua, Bayer, Richard R., Ellis, Lauren, Ebersberger, Ullrich, Tesche, Christian
Format: Article
Language:English
Subjects:
Adipose Tissue - diagnostic imaging
Computed Tomography Angiography - methods
Coronary Angiography - methods
Coronary artery disease
Coronary Artery Disease - diagnostic imaging
Coronary Stenosis
Epicardial adipose tissue
Female
Fractional Flow Reserve, Myocardial
Humans
Male
Predictive Value of Tests
Prognosis
Retrospective Studies
Spiral computed tomography
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Summary:•CT-derived EAT assessment demonstrates high discriminatory power to predict MACE.•EAT shows superior diagnostic performance over Morise score, CCTA-derived plaque measures and CT-FFR.•A combined model of these markers demonstrated incremental MACE prediction beyond clinical risk score. The purpose of this study was to determine whether EAT volume in combination with coronary CT angiography (CCTA)-derived plaque quantification and CT-derived fractional flow reserve (CT-FFR) has prognostic implication with major adverse cardiac events (MACE). Patients (n = 117, 58 ± 10 years, 61% male) who had previously undergone invasive coronary angiography (ICA) and CCTA were retrospectively analyzed. Follow-up was performed to record MACE. EAT volume and plaque measures were derived from non-contrast and contrast-enhanced CT images using a semi-automatic software approach, while CT-FFR was calculated using a machine-learning algorithm. The diagnostic performance to identify MACE was evaluated using univariable and multivariable Cox proportional hazards analysis and concordance (C)-indices. During a median follow-up period of 40.4 months, 19 events were registered. EAT volume, CCTA ≥ 50% stenosis, and CT-FFR were significantly different in patients developing MACE (all p 
ISSN:0720-048X
1872-7727
DOI:10.1016/j.ejrad.2022.110157