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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.

Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine and NewYork-Presbyterian Hospital, New York, NY, USA. Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA. Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany. Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, TX, USA. Centro Cardiologico Monzino, IRCCS Milan, Italy. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands. Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA. Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy. Tennessee Heart and Vascular Institute, Hendersonville, TN, USA. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA. Department of Medicine and Radiology, University of Ottawa, ON, Canada. Department of Radiology, Miami Cardiac and Vascular Institute, Miami, FL, USA. Capitol Cardiology Associates, Albany, NY, USA. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria. Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany. Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany. Department of Nuclear Medicine, University Hospital, Zurich, Switzerland and University of Zurich, Switzerland. Seoul National University Hospital, Seoul, South Korea. Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada. Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy. UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal. Department of Cardiology at the Lady Davis Carmel Medical Center, The Ruth and Bruce Rappaport School of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. Division of Cardiovascular Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, VA, USA.

European heart journal. 2020;(3):359-367
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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.

Methodological quality

Publication Type : Multicenter Study ; Observational Study

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