作者
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 JW 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
发表日期
2017/2/14
期刊
European heart journal
卷号
38
期号
7
页码范围
500-507
出版商
Oxford University Press
简介
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 …
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M Motwani, D Dey, DS Berman, G Germano… - European heart journal, 2017