{
  "abstract": "Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT).Results Machine learning predicted the presence of CAD on CCTA (area under the curve (AUC) 0.80, 95% CI 0.74 to 0.85) better than the 10-year cardiovascular risk score alone (AUC 0.75, 95% CI 0.70, 0.81, p=0.004). The most important features in this model were the 10-year cardiovascular risk score, age, sex, total cholesterol and an abnormal ETT. In contrast, the second model used to predict an increased LAP burden performed similarly to the 10-year cardiovascular risk score (AUC 0.75, 95% CI 0.70 to 0.80 vs AUC 0.72, 95% CI 0.66 to 0.77, p=0.08) with the most important features being the 10-year cardiovascular risk score, age, body mass index and total and high-density lipoprotein cholesterol concentrations.Conclusion Machine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.",
  "authors": [
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Michelle Claire Williams"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Alan R M Guimaraes"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Muchen Jiang"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK",
        "Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland"
      ],
      "name": "Jacek Kwieciński"
    },
    {
      "affiliations": [
        "King’s College London, London, UK"
      ],
      "name": "Jonathan R Weir-McCall"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK",
        "Christchurch Heart Institute, University of Otago Christchurch, Christchurch, South Island, New Zealand"
      ],
      "name": "Philip D Adamson"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Nicholas L Mills"
    },
    {
      "affiliations": [
        "University of Glasgow, Glasgow, Glasgow, UK"
      ],
      "name": "Giles H Roditi"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Edwin J R van Beek"
    },
    {
      "affiliations": [
        "Royal Brompton and Harefield NHS Foundation Trust, London, London, UK",
        "School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK"
      ],
      "name": "Edward Nicol"
    },
    {
      "affiliations": [
        "Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA"
      ],
      "name": "Daniel S Berman"
    },
    {
      "affiliations": [
        "Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA"
      ],
      "name": "Piotr J Slomka"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "Marc R Dweck"
    },
    {
      "affiliations": [
        "British Heart Foundation Centre for Research Excellence, The University of Edinburgh, Edinburgh, Scotland, UK"
      ],
      "name": "David E Newby"
    },
    {
      "affiliations": [
        "Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA"
      ],
      "name": "Damini Dey"
    }
  ],
  "title": "Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial",
  "uid": "0f3f0df7-5541-5174-998c-44bf4e1d8d11"
}