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Machine learning to predict cardiac events in asymptomatic individuals

  • Michelle C. Williams
    Correspondence
    Corresponding author. BHF Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16SUF, UK.
    Affiliations
    BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

    Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
    Search for articles by this author
  • David E. Newby
    Affiliations
    BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

    Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
    Search for articles by this author
      Identifying which patients are at risk of cardiac events is an important goal and enables the application of interventions to prevent future cardiovascular events. Current cardiovascular risk scores incorporate clinical features and biochemical measures to stratify patients into risk groups [
      • Goff Jr., D.C.
      • Lloyd-Jones D.M.
      • Bennett G.
      • et al.
      2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines.
      ,
      • Authors/Task Force M.
      • Piepoli M.F.
      • Hoes A.W.
      • et al.
      2016 European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European association for cardiovascular prevention & rehabilitation (EACPR).
      ]. However, they have a number of limitations, including differences when applied to populations in different countries [
      • Pennells L.
      • Kaptoge S.
      • Wood A.
      • et al.
      Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.
      ], overestimation of risk in contemporary multi-ethnic cohorts [
      • DeFilippis A.P.
      • Young R.
      • Carrubba C.J.
      • et al.
      An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort.
      ] and underestimation of risk in women [
      • Sedlak T.
      • Herscovici R.
      • Cook-Wiens G.
      • et al.
      Predicted versus observed major adverse cardiac event risk in women with evidence of ischemia and No obstructive coronary artery disease: a report from WISE (Women's Ischemia Syndrome Evaluation).
      ]. A variety of novel blood biomarkers have been assessed for their ability to predict cardiovascular events, including those related to inflammation, extracellular matrix remodelling, myocardial injury and repair, oxidative stress, neurohumoral response and lipid regulation. Coronary artery calcification identified on computed tomography can further improve risk stratification [
      • Vliegenthart R.
      • Oudkerk M.
      • Hofman A.
      • et al.
      Coronary calcification improves cardiovascular risk prediction in the elderly.
      ,
      • Budoff M.J.
      • Shaw L.J.
      • Liu S.T.
      • et al.
      Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients.
      ] by identifying the presence of underlying coronary artery disease itself. However, to date, the optimum method to combine all of these overlapping measures to predict individual cardiovascular risk has not been established.

      Keywords

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