Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Al'Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z, Alawamlh OAH, Lee B, Pandey M, Achenbach S, Al-Mallah MH, Andreini D, Bax JJ, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJ, Cury RC, DeLago A, Feuchtner G, Hadamitzky M, Hausleiter J, Kaufmann PA, Kim YJ, Leipsic JA, Maffei E, Marques H, Gonçalves PdA, Pontone G, Raff GL, Rubinshtein R, Villines TC, Gransar H, Lu Y, Jones EC, Peña JM, Lin FY, Min JK, Shaw LJ (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 41

Pages Range: 359-367

Journal Issue: 3

DOI: 10.1093/eurheartj/ehz565

Abstract

AIMS: Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). METHODS AND RESULTS: The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. CONCLUSION: A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

Authors with CRIS profile

Involved external institutions

Weill Cornell Medicine United States (USA) (US) New York Presbyterian Hospital United States (USA) (US) Houston Methodist United States (USA) (US) Centro Cardiologico Monzino (IRCCS) Italy (IT) Leiden University Medical Center Netherlands (NL) Cedars-Sinai Medical Center United States (USA) (US) SDN Istituto di Ricerca Diagnostica e Nucleare Italy (IT) Tennessee Heart & Vascular United States (USA) (US) Yonsei University Health System (YUHS) Korea, Republic of (KR) Beaumont Health United States (USA) (US) University of Ottawa Canada (CA) Baptist Health South Florida United States (USA) (US) Capitol Cardiology Associates United States (USA) (US) Medizinische Universität Innsbruck Austria (AT) Deutsches Herzzentrum München Germany (DE) Ludwig-Maximilians-Universität (LMU) Germany (DE) Universitätsspital Zürich (USZ) Switzerland (CH) Seoul National University (SNU) / 서울대학교 Korea, Republic of (KR) University of British Columbia Canada (CA) Hospital da Luz Portugal (PT) Technion - Israel Institute of Technology Israel (IL) University of Virginia Health System United States (USA) (US)

How to cite

APA:

Al'Aref, S.J., Maliakal, G., Singh, G., van Rosendael, A.R., Ma, X., Xu, Z.,... Shaw, L.J. (2020). Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. European Heart Journal, 41(3), 359-367. https://doi.org/10.1093/eurheartj/ehz565

MLA:

Al'Aref, Subhi J., et al. "Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry." European Heart Journal 41.3 (2020): 359-367.

BibTeX: Download