[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119587-en":3,"doc-seo-119587-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119587,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial - study abstract","Machine learning using routine clinical characteristics was evaluated to predict coronary CT angiography (CCTA) findings and support more efficient resource use. Data from 1,769 SCOT-HEART trial participants trained and tested XGBoost models with 10-fold cross-validation and grid-search hyperparameter selection. One model predicted coronary artery disease (CAD) presence and outperformed the 10-year cardiovascular risk score; a second model predicted increased low-attenuation coronary plaque (LAP) burden but performed similarly and was not improved by clinical inputs alone.","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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Aug. 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","cbCaiftbjLooMPug","https://ap.wps.com/l/cbCaiftbjLooMPug","pdf",1145742,1,10,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion","[{\"question\":\"How were the machine learning models trained and tested in the SCOT-HEART study?\",\"answer\":\"Models were trained and tested using SCOT-HEART trial data from 1,769 patients, with 10-fold cross-validation and grid-search hyperparameter selection.\"},{\"question\":\"Which model predicted coronary artery disease (CAD) presence, and how did it perform?\",\"answer\":\"The CAD model predicted presence on CCTA and showed better discrimination than the 10-year cardiovascular risk score, with a higher AUC (0.80 vs 0.75, p=0.004).\"},{\"question\":\"Could clinical factors improve prediction of increased low-attenuation coronary plaque (LAP) burden?\",\"answer\":\"No. The LAP burden model performed similarly to the 10-year cardiovascular risk score and did not show meaningful improvement using clinical factors alone.\"}]","Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial - study abstract | PDF",1785725146,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-to-predict-high-risk-coronary-artery-disease-on-ct-in-the-scot-heart-trial-study-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-predict-high-risk-coronary-artery-disease-on-ct-in-the-scot-heart-trial-study-abstract/119587/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How were the machine learning models trained and tested in the SCOT-HEART study?","Question",{"text":75,"@type":76},"Models were trained and tested using SCOT-HEART trial data from 1,769 patients, with 10-fold cross-validation and grid-search hyperparameter selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model predicted coronary artery disease (CAD) presence, and how did it perform?",{"text":80,"@type":76},"The CAD model predicted presence on CCTA and showed better discrimination than the 10-year cardiovascular risk score, with a higher AUC (0.80 vs 0.75, p=0.004).",{"name":82,"@type":73,"acceptedAnswer":83},"Could clinical factors improve prediction of increased low-attenuation coronary plaque (LAP) burden?",{"text":84,"@type":76},"No. The LAP burden model performed similarly to the 10-year cardiovascular risk score and did not show meaningful improvement using clinical factors alone.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]