TY - JOUR
T1 - Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography
T2 - Analysis from the CONFIRM registry
AU - Al'Aref, Subhi J.
AU - Maliakal, Gabriel
AU - Singh, Gurpreet
AU - van Rosendael, Alexander R.
AU - Ma, Xiaoyue
AU - Xu, Zhuoran
AU - Al Hussein Alawamlh, Omar
AU - Lee, Benjamin
AU - Pandey, Mohit
AU - Achenbach, Stephan
AU - Al-Mallah, Mouaz H.
AU - Andreini, Daniele
AU - Bax, Jeroen J.
AU - Berman, Daniel S.
AU - Budoff, Matthew J.
AU - Cademartiri, Filippo
AU - Callister, Tracy Q.
AU - Chang, Hyuk Jae
AU - Chinnaiyan, Kavitha
AU - Chow, Benjamin J.W.
AU - Cury, Ricardo C.
AU - DeLago, Augustin
AU - Feuchtner, Gudrun
AU - Hadamitzky, Martin
AU - Hausleiter, Joerg
AU - Kaufmann, Philipp A.
AU - Kim, Yong Jin
AU - Leipsic, Jonathon A.
AU - Maffei, Erica
AU - Marques, Hugo
AU - de Araújo Gonçalves, Pedro
AU - Pontone, Gianluca
AU - Raff, Gilbert L.
AU - Rubinshtein, Ronen
AU - Villines, Todd C.
AU - Gransar, Heidi
AU - Lu, Yao
AU - Jones, Erica C.
AU - Peña, Jessica M.
AU - Lin, Fay Y.
AU - Min, James K.
AU - Shaw, Leslee J.
N1 - Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2020/1/14
Y1 - 2020/1/14
N2 - 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 The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent >_64 detector row and results 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 þ CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score þ 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.
AB - 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 The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent >_64 detector row and results 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 þ CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score þ 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.
KW - Coronary artery calcium score
KW - Coronary artery disease
KW - Coronary computed tomography angiography
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85077944743&partnerID=8YFLogxK
U2 - 10.1093/eurheartj/ehz565
DO - 10.1093/eurheartj/ehz565
M3 - Article
C2 - 31513271
AN - SCOPUS:85077944743
SN - 0195-668X
VL - 41
SP - 359
EP - 367
JO - European Heart Journal
JF - European Heart Journal
IS - 3
ER -