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Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome

  • Philipp L. von Knebel Doeberitz
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
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  • Carlo N. De Cecco
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, Georgia
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  • U. Joseph Schoepf
    Correspondence
    Corresponding author: Tel: (843) 876-7146; fax: (843) 876-3157.
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina
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  • Moritz H. Albrecht
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
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  • Marly van Assen
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Center for Medical Imaging North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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  • Domenico De Santis
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza”, Rome, Italy
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  • Jeffrey Gaskins
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
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  • Simon Martin
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
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  • Maximilian J. Bauer
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
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  • Ullrich Ebersberger
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Kardiologie MVZ München-Nord, Munich, Germany

    Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany
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  • Dante A. Giovagnoli
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
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  • Akos Varga-Szemes
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
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  • Richard R. Bayer 2nd,
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

    Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina
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  • Stefan O. Schönberg
    Affiliations
    Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
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  • Christian Tesche
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina

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

    Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany
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      This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.
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