Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Journal: Journal of the American Heart Association
Published Date: Feb 22, 2020

Abstract

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all <0.001; statistical model, 0.81 [0.75-0.87], =0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP.

Authors

  • Donghee Han
    Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, Republic of Korea.
  • Kranthi K Kolli
    Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, USA.
  • Subhi J Al'Aref
    Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, USA.
  • Lohendran Baskaran
  • Alexander R van Rosendael
    Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.
  • Heidi Gransar
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Daniele Andreini
    Division of Cardiology and Cardiac Imaging, IRCCS Ospedale Galeazzi - Sant'Ambrogio Milan, Italy.
  • Matthew J Budoff
    Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Torrance, CA, USA. mbudoff@labiomed.org.
  • Filippo Cademartiri
    Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy.
  • Kavitha Chinnaiyan
    Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA.
  • Jung Hyun Choi
    Pusan University Hospital, Busan, South Korea.
  • Edoardo Conte
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Hugo Marques
    UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal.
  • Pedro de Araújo Gonçalves
    UNICA Unit of Cardiovascular Imaging Hospital da Luz Lisboa Portugal.
  • Ilan Gottlieb
    Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil.
  • Martin Hadamitzky
    School of Medicine and Health, Institute for Cardiovascular Radiology and Nuclear Medicine, German Heart Center Munich, TUM University Hospital, Technical University of Munich, Munich, Germany.
  • Jonathon A Leipsic
    Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, CA, USA.
  • Erica Maffei
    Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy.
  • Gianluca Pontone
    Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
  • Gilbert L Raff
    Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA.
  • Sangshoon Shin
    Ewha Womans University Seoul Hospital Seoul South Korea.
  • Yong-Jin Kim
    Seoul National University Hospital, Seoul, South Korea.
  • Byoung Kwon Lee
    Division of Cardiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Eun Ju Chun
    Department of Radiology, Seoul National University Bundang Hospital, Seoul, Republic of Korea.
  • Ji Min Sung
    Integrative Research Center for Cerebrovascular and Cardiovascular diseases, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea.
  • Sang-Eun Lee
    Department of Urology, Seoul National University Bundang Hospital, Seongnam, South Korea - selee@snubh.org.
  • Renu Virmani
    Department of Pathology, CVPath Institute, Gaithersburg, Maryland, United States of America.
  • Habib Samady
    Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, United States of America.
  • Peter Stone
  • Jagat Narula
  • Daniel S Berman
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Jeroen J Bax
    Departments of Cardiology, Heart Lung Centre, Leiden University Medical Center, Leiden, The Netherlands.
  • Leslee J Shaw
    Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA.
  • Fay Y Lin
    Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.
  • James K Min
    3 Department of Radiology, Weill Cornell Medicine , New York, New York.
  • Hyuk-Jae Chang
    Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic Of Korea.

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