[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122448-en":3,"doc-seo-122448-105":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},122448,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Predicting the rapid progression of coronary artery lesions in patients with acute coronary syndrome based on machine learning","Rapid coronary artery lesions are strongly associated with major adverse cardiovascular events in acute coronary syndrome. This study retrospectively analyzed clinical data from 324 patients with acute coronary syndrome who underwent percutaneous coronary intervention and used univariate and multivariate analyses to identify independent risk factors. Variables were screened with Lasso regression, nine machine-learning models were compared, and the best-performing model was validated in an external cohort. SHAP was used to interpret feature contributions, and an online prediction platform (OpRCAL) was built for clinicians.","TYPE Original Research PUBLISHED 05 September 2025 DOI 10.3389/fcvm.2025.1535406  \nEDITED BY  \nOmneya Attallah,  \nTechnology and Maritime Transport (AASTMT), Egypt  \nREVIEWED BY  \nKayode O. Kuku,  \nNational Heart, Lung, and Blood Institute (NIH), United States  \nMuhammet Fatih Aslan,  \nKaramanŏglu Mehmetbey University, Türkiye Ishak Pacal,  \nĬgdır Üniversitesi, Türkiye  \n*CORRESPONDENCE  \nSongran Yang  \n [yangsr@mail.sysu.edu.cn](yangsr@mail.sysu.edu.cn)[ ](yangsr@mail.sysu.edu.cn)Ping Hua  \n [huaping@mail.sysu.edu.cn](huaping@mail.sysu.edu.cn)[ ](huaping@mail.sysu.edu.cn)RECEIVED 27 November 2024 ACCEPTED 13 August 2025  \nPUBLISHED 05 September 2025  \nCITATION  \nGui L, Hu Y, Ouyang H, Zhuang H, Peng Y, Yang J, Cao H, Yang S and Hua P (2025) Predicting the rapid progression of coronary artery lesions in patients with acute coronary syndrome based on machine learning.  \nFront. Cardiovasc. Med. 12:1535406 .  \ndoi: 10.3389/fcvm.2025.1535406  \nCOPYRIGHT  \n© 2025 Gui, Hu, Ouyang, Zhuang, Peng, Yang, Cao, Yang and Hua. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting the rapid progression of coronary artery lesions inpatients with acute coronary syndrome based on machine learning  \nLong Gui1, Yuekang Hu1, Hua Ouyang1, Huanwei Zhuang2, Yangfei Peng1, Jun Yang1, Heshan Cao3, Songran Yang3,4* and Ping Hua1*  \n1Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China, 2Department of Cardiovascular Surgery, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, China, 3Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China, 4Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China  \nPurposes: Rapid coronary artery lesions (RCAL) are strongly linked to major adverse cardiovascular events in patients with acute coronary syndrome (ACS) . This work developed a public online prediction platform for RCAL (OpRCAL) by comparing the performance of nine machine learning models.  \nMethods: We retrospectively examined the clinical data of 324 patients with ACS who received percutaneous coronary intervention (PCI) . Using both univariate and multivariate analyses, the potential independent risk factors for RCAL were studied. Following the screening of all variables using Lasso regression, multiple machine learning models were constructed. The optimal model was then chosen and validated using an external cohort. Furthermore, to elucidate the contribution of each feature to the model, the shapley additive explanation (SHAP) values of the variables were calculated. Finally, a prediction platform for RCAL in patients with ACS following PCI was established.  \nResults: The number of coronary lesions, systolic blood pressure (SBP), Nterminal pro-brain natriuretic peptide (NT-proBNP), QRS interval, and platelet count were found as independent risk factors for RCAL. Among the nine machine learning models constructed after identifying twelve different variables using Lasso regression, the random forest (RF) model performed best in the training cohort and showed good generalization in the external test cohort, with area under curve of 0 .774 (95%CI: 0 .640–0. 909) . Finally, we constructed an online platform named OpRCAL for clinicians to predict RCALin patients with ACS following PCI based on the RF model.  \nConclusions: The RF model exhibits high accuracy and generalizability in predicting RCAL, thereby providing a valuable instrument to assist clinical decision-mak","cbCaiplcgieNqkCM","https://ap.wps.com/l/cbCaiplcgieNqkCM","pdf",880424,1,11,"English","en",105,"# Introduction\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the study trying to predict in patients with acute coronary syndrome?\",\"answer\":\"It predicts the rapid progression of coronary artery lesions (RCAL) in patients with acute coronary syndrome following percutaneous coronary intervention.\"},{\"question\":\"How were risk factors and the final prediction model determined?\",\"answer\":\"The study screened variables using Lasso regression, then built multiple machine-learning models and selected the best one based on performance, with external-cohort validation. Independent risk factors were identified using univariate and multivariate analyses.\"},{\"question\":\"Why are SHAP values mentioned in the results?\",\"answer\":\"SHAP values were calculated to explain the contribution of each feature to the selected model’s predictions, improving interpretability for clinicians.\"}]","Predicting the rapid progression of coronary artery lesions in patients with acute coronary syndrome based on machine learning | PDF",1785810689,28,{"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},"predicting-the-rapid-progression-of-coronary-artery-lesions-in-patients-with-acute-coronary-syndrome-based-on-machine-learning","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-rapid-progression-of-coronary-artery-lesions-in-patients-with-acute-coronary-syndrome-based-on-machine-learning/122448/",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","2026-08-04",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},"What is the study trying to predict in patients with acute coronary syndrome?","Question",{"text":75,"@type":76},"It predicts the rapid progression of coronary artery lesions (RCAL) in patients with acute coronary syndrome following percutaneous coronary intervention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were risk factors and the final prediction model determined?",{"text":80,"@type":76},"The study screened variables using Lasso regression, then built multiple machine-learning models and selected the best one based on performance, with external-cohort validation. Independent risk factors were identified using univariate and multivariate analyses.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are SHAP values mentioned in the results?",{"text":84,"@type":76},"SHAP values were calculated to explain the contribution of each feature to the selected model’s predictions, improving interpretability for clinicians.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]