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

Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJW, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim YJ, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ (2017)

Publication Type: Journal article

Publication year: 2017

Journal

Book Volume: 38

Pages Range: 500-507

Journal Issue: 7

DOI: 10.1093/eurheartj/ehw188

Abstract

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.

Authors with CRIS profile

Involved external institutions

Cedars-Sinai Medical Center United States (USA) (US) King Abdullah International Medical Research Center (KAIMRC) Saudi Arabia (SA) Harbor–UCLA Medical Center United States (USA) (US) Université de Montréal Canada (CA) Tennessee Heart & Vascular United States (USA) (US) Yonsei University Health System (YUHS) Korea, Republic of (KR) Beaumont Health System United States (USA) (US) Baptist Health South Florida United States (USA) (US) Capitol Cardiology Associates United States (USA) (US) Cornell University United States (USA) (US) Deutsches Herzzentrum München Germany (DE) Ludwig-Maximilians-Universität (LMU) Germany (DE) Medizinische Universität Innsbruck Austria (AT) Universitätsspital Zürich (USZ) Switzerland (CH) University of British Columbia Canada (CA) Hospital da Luz Portugal (PT) Emory University United States (USA) (US) Walter Reed Army Medical Center (WRAMC) United States (USA) (US) Università degli studi di Milano Italy (IT) University of Ottawa Canada (CA) Seoul National University (SNU) / 서울대학교 Korea, Republic of (KR) Istituto di ricovero e cura a carattere scientifico (IRCCS) Italy (IT) Technion - Israel Institute of Technology Israel (IL) Duke University Medical Center United States (USA) (US) New York Presbyterian Hospital United States (USA) (US)

How to cite

APA:

Motwani, M., Dey, D., Berman, D.S., Germano, G., Achenbach, S., Al-Mallah, M.H.,... Slomka, P.J. (2017). Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. European Heart Journal, 38(7), 500-507. https://doi.org/10.1093/eurheartj/ehw188

MLA:

Motwani, Manish, et al. "Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis." European Heart Journal 38.7 (2017): 500-507.

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