Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

Eisenberg E, McElhinney PA, Commandeur F, Chen X, Cadet S, Goeller M, Razipour A, Gransar H, Cantu S, Miller RJ, Slomka PJ, Wong ND, Rozanski A, Achenbach S, Tamarappoo BK, Berman DS, Dey D (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Circulation: Cardiovascular Imaging American Heart Association

Book Volume: 13

Pages Range: e009829-

Journal Issue: 2

DOI: 10.1161/CIRCIMAGING.119.009829

Abstract

BACKGROUND: Epicardial adipose tissue (EAT) volume (cm3) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography. METHODS: Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction. RESULTS: At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01-1.04]; 1.25 [1.19-1.30]; and 1.35 [1.07-1.68], P<0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; P=0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm3 and coronary artery calcium ≥100 AU and was highest in subjects with both (P<0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers. CONCLUSIONS: Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.

Authors with CRIS profile

Stephan Achenbach Lehrstuhl für Innere Medizin II

Involved external institutions

Cedars-Sinai Medical Center US United States (USA) (US) University of California Irvine US United States (USA) (US)

How to cite

APA:

Eisenberg, E., McElhinney, P.A., Commandeur, F., Chen, X., Cadet, S., Goeller, M.,... Dey, D. (2020). Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects. Circulation: Cardiovascular Imaging, 13(2), e009829-. https://dx.doi.org/10.1161/CIRCIMAGING.119.009829

MLA:

Eisenberg, Evann, et al. "Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects." Circulation: Cardiovascular Imaging 13.2 (2020): e009829-.

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