Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial
Machine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.
Authors
- A Guimarães (ORCID: https://orcid.org/0009-0009-0554-4529)
- Philip D Adamson (ORCID: https://orcid.org/0000-0002-6177-956X)
- Giles Roditi (ORCID: https://orcid.org/0000-0001-9393-7764)
- Nicholas L. Mills (ORCID: https://orcid.org/0000-0003-0533-7991)
- Michelle Claire Williams (ORCID: https://orcid.org/0000-0003-3556-2428)
- Damini Dey (ORCID: https://orcid.org/0000-0003-2236-6970)
- Jonathan Richard Weir-McCall (ORCID: https://orcid.org/0000-0001-5842-842X)
- Jacek Kwieciński (ORCID: https://orcid.org/0000-0001-8202-6359)
- Edward David Nicol (ORCID: https://orcid.org/0000-0001-9280-787X)
- David E. Newby (ORCID: https://orcid.org/0000-0001-7971-4628)
- Piotr J. Slomka (ORCID: https://orcid.org/0000-0002-6110-938X)
- Daniel S. Berman
- Edwin J.R. van Beek
- Muchen Jiang
Institutions
- Cedars-Sinai Medical Center (US)
- King's College London (GB)
- Royal Brompton & Harefield NHS Foundation Trust (GB)
- British Heart Foundation (GB)
- New Zealand Brain Research Institute (NZ)
- Christchurch Clinical Studies Trust (NZ)
- Institute of Cardiology (PL)
- University of Glasgow (GB)
Publication Details
- Journal
- Open Heart
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1136/openhrt-2025-003162
- Citations
- 2
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Israel Cancer Research Fund
- AstraZeneca
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- Wellcome Trust
- Scottish Government
- British Heart Foundation
- Siemens Healthineers
- Chief Scientist Office, Scottish Government Health and Social Care Directorate