Patient Characteristics as Predictors of Image Quality and Diagnostic Accuracy of MDCT Compared With Conventional Coronary Angiography for Detecting Coronary Artery Stenoses: CORE-64 Multicenter International Trial

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  • Marc Dewey
    Department of Radiology, Charité, Universitätsmedizin Berlin Medical School, Humboldt-Universität und Freie Univartment ersität zu Berlin, Berlin, Germany.
  • Andrea L. Vavere
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Armin Arbab-Zadeh
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Julie M. Miller
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Leonardo Sara
    University of Sao Paulo, InCor Heart Institute, Sao Paulo, Brazil.
  • Christopher Cox
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
  • Ilan Gottlieb
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Kunihiro Yoshioka
    Department of Radiology, Iwate Medical University, Morioka, Japan.
  • Narinder Paul
    Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada.
  • John Hoe
    Medi-Rad Associates Ltd., CT Centre, Mount Elizabeth Hospital, Singapore.
  • Albert de Roos
    Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Albert C. Lardo
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Joao A. Lima
    Department of Cardiology, Johns Hopkins University, Baltimore, MD.
  • Melvin E. Clouse
    Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 1 Deaconess Rd., WCC 308, Boston, MA 02215.

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The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis.Two hundred ninety-one patients with a coronary artery calcification (CAC) score ofor=600 Agatston units (214 men and 77 women; mean age, 59.3+/-10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (or=50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves.Increasing body mass index (BMI) (odds ratio [OR]=0.89, p0.001), increasing heart rate (OR=0.90, p0.001), and the presence of breathing artifact (OR=4.97, por=0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR=0.58, p=0.04). At a vessel level, CAC score (10 Agatston units) (OR=1.03, p=0.012) and patient age (OR=1.02, p=0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p=0.08).Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

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