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

Author:

Al’Aref Subhi J1ORCID,Maliakal Gabriel1,Singh Gurpreet1ORCID,van Rosendael Alexander R1,Ma Xiaoyue2,Xu Zhuoran1ORCID,Alawamlh Omar Al Hussein1,Lee Benjamin1ORCID,Pandey Mohit1,Achenbach Stephan3,Al-Mallah Mouaz H4ORCID,Andreini Daniele5,Bax Jeroen J6ORCID,Berman Daniel S7,Budoff Matthew J8,Cademartiri Filippo9,Callister Tracy Q10,Chang Hyuk-Jae11,Chinnaiyan Kavitha12,Chow Benjamin J W13,Cury Ricardo C14,DeLago Augustin15,Feuchtner Gudrun16ORCID,Hadamitzky Martin17,Hausleiter Joerg18,Kaufmann Philipp A19,Kim Yong-Jin20,Leipsic Jonathon A21,Maffei Erica22,Marques Hugo23,Gonçalves Pedro de Araújo23,Pontone Gianluca5,Raff Gilbert L12,Rubinshtein Ronen24,Villines Todd C25,Gransar Heidi7,Lu Yao2,Jones Erica C1,Peña Jessica M1,Lin Fay Y1ORCID,Min James K1,Shaw Leslee J1

Affiliation:

1. Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine and NewYork-Presbyterian Hospital, New York, NY, USA

2. Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA

3. Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremburg, Germany

4. Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, TX, USA

5. Centro Cardiologico Monzino, IRCCS Milan, Italy

6. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands

7. Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA

8. Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA

9. Cardiovascular Imaging Center, SDN IRCCS, Naples, Italy

10. Tennessee Heart and Vascular Institute, Hendersonville, TN, USA

11. Division of Cardiology, Severance Cardiovascular Hospital and Severance Biomedical Science Institute, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea

12. Department of Cardiology, William Beaumont Hospital, Royal Oak, MI, USA

13. Department of Medicine and Radiology, University of Ottawa, ON, Canada

14. Department of Radiology, Miami Cardiac and Vascular Institute, Miami, FL, USA

15. Capitol Cardiology Associates, Albany, NY, USA

16. Department of Radiology, Medical University of Innsbruck, Innsbruck, Austria

17. Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany

18. Medizinische Klinik I der Ludwig-Maximilians-Universität München, Munich, Germany

19. Department of Nuclear Medicine, University Hospital, Zurich, Switzerland and University of Zurich, Switzerland

20. Seoul National University Hospital, Seoul, South Korea

21. Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada

22. Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy

23. UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal

24. 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

25. Division of Cardiovascular Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, VA, USA

Abstract

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.

Funder

National Institute of Health

Michael Wolk Foundation

NIH

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine

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