title: Relationship of epicardial fat volume from noncontrast CT with impaired myocardial flow reserve by positron emission tomography.
abstract: BACKGROUND
  Impaired myocardial flow reserve (MFR) is a marker of coronary vascular dysfunction with prognostic significance.
  
  
  OBJECTIVE
  We aimed to investigate the relationship between epicardial fat volume (EFV) measured from noncontrast CT and impaired MFR derived from rest-stress Rb-82 positron emission tomography (PET).
  
  
  METHODS
  We retrospectively studied 85 consecutive patients without known coronary artery disease who underwent rest-stress Rb-82 myocardial PET/CT and were subsequently referred for invasive coronary angiography. EFV was computed from noncontrast CT by validated software and indexed to body surface area (EFVi, cm3/m2). Global stress and rest MFR were automatically derived from PET. Patient age, sex, cardiovascular risk factors, coronary calcium score (CCS), and EFVi were combined by boosted ensemble machine learning algorithm into a novel composite risk score, using 10-fold cross-validation, to predict impaired global MFR (MFR ≤2.0) by PET.
  
  
  RESULTS
  Patients with impaired MFR (44 of 85; 52%) were older (71 vs. 65 years; P = .03) and had higher frequency of CCS (≥400; P = .02) with significantly higher EFVi (63.1 ± 20.4 vs. 51.3 ± 14.1 cm3/m2; P = .003). On multivariate logistic regression (with age, sex, number of risk factors, CCS, and EFVi), EFVi was the only independent predictor of impaired MFR (odds ratio, 7.39; P = .02). The machine learning composite risk score significantly improved risk reclassification of impaired MFR compared to CCS or EFVi alone (integrated discrimination improvement = 0.19; P = .007 and IDI = 0.22; P = .002, respectively).
  
  
  CONCLUSIONS
  Increased EFVi and composite risk score combining EFVi and CCS significantly improve identification of impaired global MFR by PET.
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Yuka
authors.initials: Y
authors.lastname: Otaki
authors.affiliation: Department of Cardiology, University of Erlangen, Erlangen, Germany.
authors.forename: Michaela
authors.initials: M
authors.lastname: Hell
authors.affiliation: Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.affiliation: Department of Cardiology, University of Erlangen, Erlangen, Germany.
authors.forename: Annika
authors.initials: A
authors.lastname: Schuhbaeck
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Heidi
authors.initials: H
authors.lastname: Gransar
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Brandi
authors.initials: B
authors.lastname: Huber
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Ryo
authors.initials: R
authors.lastname: Nakazato
authors.affiliation: Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Sean W
authors.initials: SW
authors.lastname: Hayes
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Louise E J
authors.initials: LE
authors.lastname: Thomson
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Friedman
authors.affiliation: Department of Cardiology, University of Erlangen, Erlangen, Germany.
authors.forename: Stephan
authors.initials: S
authors.lastname: Achenbach
authors.affiliation: Department of Imaging, Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.affiliation: Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA. Electronic address: Damini.Dey@cshs.org.
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D050154:Adiposity; D000368:Aged; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005260:Female; D053805:Fractional Flow Reserve, Myocardial; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D009929:Organ Size; D010496:Pericardium / Q000000981:diagnostic imaging*; D011379:Prognosis; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Coronary calcium scoring; Coronary vascular dysfunction; Epicardial fat volume; Myocardial flow reserve; Noncontrast CT