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Using SCOT-HEART trial data from 1,769 patients, models (XGBoost with 10-fold cross validation and grid-search hyperparameter selection) were trained to predict coronary artery disease presence and increased low-attenuation coronary plaque burden. The CAD model achieved higher discrimination than the 10-year cardiovascular risk score, while the plaque-burden model performed similarly and could not outperform clinical factors alone.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-to-predict-high-risk-coronary-artery-disease-on-ct-in-the-scot-heart-trial-article/290652/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-to-predict-high-risk-coronary-artery-disease-on-ct-in-the-scot-heart-trial-article/290652.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What clinical goal does the study target for machine learning on CCTA?","Question",{"text":112,"@type":113},"To predict CCTA findings and help guide resource utilisation by identifying coronary artery disease and an increased burden of low-attenuation coronary artery plaque from routine clinical data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the machine learning models developed and validated?",{"text":117,"@type":113},"Data from 1,769 SCOT-HEART participants were used to train and test XGBoost models with 10-fold cross validation and grid search hyperparameter selection.",{"name":119,"@type":110,"acceptedAnswer":120},"What was the main performance outcome compared with the 10-year cardiovascular risk score?",{"text":121,"@type":113},"Machine learning improved prediction of CAD presence on CCTA versus the 10-year cardiovascular risk score (AUC 0.80 vs 0.75), but did not improve prediction of increased plaque burden using clinical factors alone.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290652,1789642273,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","King’s Research Portal  \nDOI:  \n10.1136/openhrt-2025-003162  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nWilliams, M. C. , Guimaraes, A. R. M. , Jiang, M. , Kwieciński, J. , Weir-McCall, J. R. , Adamson, P. D. , Mills, N. L. , Roditi, G. H. , van Beek, E. J. R. , Nicol, E. , Berman, D. S. , Slomka, P. J. , Dweck, M. R. , Newby, D. E. , & Dey, D.(2025) . Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial. Open Heart, 12(2), Article :e003162 . [https://doi.org/10.1136/openhrt-2025-003162](https://doi.org/10.1136/openhrt-2025-003162)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. 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Mar. 2026  \nOpen access Coronary artery disease  \n Machine learning to predict high-risk  \ncoronary artery disease on CT in the SCOT-HEART trial  \nMichelle Claire Williams  ,1 Alan R M Guimaraes,1 Muchen Jiang,1 Jacek Kwieciński,1,2 Jonathan R Weir-McCall,3 Philip D Adamson  ,1,4 Nicholas L Mills,1 Giles H Roditi,5 Edwin J R van Beek,1 Edward Nicol,6,7 Daniel S Berman,8 Piotr J Slomka  ,8 Marc R Dweck  ,1 David E Newby,1 Damini Dey  9  \n► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi.org/10.1136/](https://doi.org/10.1136/)[ ](https://doi.org/10.1136/)[openhrt-2025-003162](openhrt-2025-003162)) .  \nTo cite: Williams MC, Guimaraes ARM, Jiang M, et al. Machine learning to predict high-risk coronary artery disease on CT in the SCOTHEART trial. Open Heart 2025;12:e003162 . doi:10 . 1136/ openhrt-2025-003162  \nReceived 25 July 2025 Accepted 11 August 2025  \n© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group. For numbered affiliations see end of article.  \nCorrespondence to  \nDr Michelle Claire Williams; [michelle.williams@ed.ac.uk](michelle.williams@ed.ac.uk)  \nABSTRACT  \nBackground Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.  \nMethods From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769  \npatients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection) . Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT) .  \nResults Machine lear","cbCaitWEZABtWCLV","https://ap.wps.com/l/cbCaitWEZABtWCLV","pdf",1145736,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n# What is already known on this topic\n# What this study adds","[{\"question\":\"What clinical goal does the study target for machine learning on CCTA?\",\"answer\":\"To predict CCTA findings and help guide resource utilisation by identifying coronary artery disease and an increased burden of low-attenuation coronary artery plaque from routine clinical data.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"Data from 1,769 SCOT-HEART participants were used to train and test XGBoost models with 10-fold cross validation and grid search hyperparameter selection.\"},{\"question\":\"What was the main performance outcome compared with the 10-year cardiovascular risk score?\",\"answer\":\"Machine learning improved prediction of CAD presence on CCTA versus the 10-year cardiovascular risk score (AUC 0.80 vs 0.75), but did not improve prediction of increased plaque burden using clinical factors alone.\"}]","Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial - Article | PDF",25]