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Open Access Publications from the University of California

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

  • Author(s): Motwani, Manish
  • Dey, Damini
  • Berman, Daniel S
  • Germano, Guido
  • Achenbach, Stephan
  • Al-Mallah, Mouaz H
  • Andreini, Daniele
  • Budoff, Matthew J
  • Cademartiri, Filippo
  • Callister, Tracy Q
  • Chang, Hyuk-Jae
  • Chinnaiyan, Kavitha
  • Chow, Benjamin JW
  • Cury, Ricardo C
  • Delago, Augustin
  • Gomez, Millie
  • Gransar, Heidi
  • Hadamitzky, Martin
  • Hausleiter, Joerg
  • Hindoyan, Niree
  • Feuchtner, Gudrun
  • Kaufmann, Philipp A
  • Kim, Yong-Jin
  • Leipsic, Jonathon
  • Lin, Fay Y
  • Maffei, Erica
  • Marques, Hugo
  • Pontone, Gianluca
  • Raff, Gilbert
  • Rubinshtein, Ronen
  • Shaw, Leslee J
  • Stehli, Julia
  • Villines, Todd C
  • Dunning, Allison
  • Min, James K
  • Slomka, Piotr J
  • et al.

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 patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS). Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation. Seven hundred and forty-five patients died during 5-year follow-up. Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 0.79 vs. FRS: 0.61, SSS: 0.64, SIS: 0.64, DI: 0.62; P< 0.001). Conclusions:Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.

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