Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

AIMS: Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. METHODS AND RESULTS: The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All…

• DF
  Dalio Foundation
• DC
  Dowager Countess Eleanor Peel Trust
• NR
  National Research Foundation

  Award: 2012027176
• NR
  National Research Foundation of Korea

  Award: 2012027176
• NH
  National Heart, Lung, and Blood Institute

  Awards: R01-2R01HL089765, R01-HL115150, R01-HL111141, R01-HL118019