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

 Manish Motwani  ;  Damini Dey  ;  Daniel S. Berman  ;  Guido Germano  ;  Stephan Achenbach  ;  Mouaz H. Al-Mallah  ;  Daniele Andreini  ;  Matthew J. Budoff  ;  Filippo Cademartiri  ;  Tracy Q. Callister  ;  Hyuk-Jae Chang  ;  Kavitha Chinnaiyan  ;  Benjamin J.W. Chow  ;  Ricardo C. Cury  ;  Augustin Delago  ;  Millie Gomez  ;  Heidi Gransar  ;  Martin Hadamitzky  ;  Joerg Hausleiter  ;  Niree Hindoyan  ;  Gudrun Feuchtner  ;  Philipp A. Kaufmann  ;  Yong-Jin Kim  ;  Jonathon Leipsic  ;  Fay Y. Lin  ;  Erica Maffei  ;  Hugo Marques  ;  Gianluca Pontone  ;  Gilbert Raff  ;  Ronen Rubinshtein  ;  Leslee J. Shaw  ;  Julia Stehli  ;  Todd C. Villines  ;  Allison Dunning  ;  James K. Min  ;  Piotr J. Slomka 
 EUROPEAN HEART JOURNAL, Vol.38(7) : 500-507, 2017 
Journal Title
Issue Date
Cause of Death ; Computed Tomography Angiography ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/mortality* ; Feasibility Studies ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Prospective Studies ; Registries ; Risk Factors
Coronary CT angiography ; Coronary artery disease ; Machine learning ; Prognosis
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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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
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