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Al'Arefilb, Subhi J.; Maliakal, Gabriel; Singh, Gurpreet; Rosendael, Alexander R. van; Ma, Xiaoyue; Xu, Zhuoran; Alawamlh, Omar Al Hussein; Lee, Benjamin; Pandey, Mohit; Achenbach, Stephan; Al-Mallah, Mouaz H.; Andreini, Daniele; Bax, Jeroen J.; Berman, Daniel S.; Budoff, Matthew J.; Cademartiri, Filippo; Canister, Tracy Q.; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J. W.; Cury, Ricardo C.; DeLago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Jörg; Kaufmann, Philipp A.; Kim, Yong-Jin; Leipsic, Jonathon A.; Maffei, Erica; Marques, Hugo; Goncalves, Pedro de Araujo; Pontone, Gianluca; Raff, Gilbert L.; Rubinshtein, Ronen; Villines, Todd C.; Gransar, Heidi; Lu, Yao; Jones, Erica C.; Pena, Jessica M.; Lin, Fay Y.; Min, James K. and Shaw, Leslee 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. In: European Heart Journal, Vol. 41, No. 3: pp. 359-367

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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 [earning (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.

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