[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121843-en":3,"doc-seo-121843-105":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},121843,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning - Original Research","Carotid endarterectomy (CEA) reduces stroke risk but carries notable perioperative danger, and reliable outcome prediction remains limited. Using a National Surgical Quality Improvement Program targeted vascular database, researchers analyzed 38,853 CEA patients (2011–2021) with 36 preoperative variables to predict 30-day major adverse cardiovascular events. A set of machine learning models, evaluated with cross-validation and calibration measures, achieved strong discrimination with the best-performing XGBoost model.","Downloaded from [http://ahajournals.org by on March 27](http://ahajournals.org by on March 27), 2024  \nJournal of the American Heart Association  \nORIGINAL RESEARCH  \n\n| Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning\u003Cbr>Ben Li  , MD; Raj Verma, MD(c); Derek Beaton, PhD; Hani Tamim, PhD; Mohamad A. Hussain  , MD, PhD; Jamal J. Hoballah, MD, MBA; Douglas S. Lee  , MD, PhD; Duminda N. Wijeysundera  , MD, PhD;\u003Cbr>Charles de Mestral, MD, PhD; Muhammad Mamdani, MPH, MA, PharmD; Mohammed Al-Omran , MD, MSc\u003Cbr>BACKGROUND: Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA.\u003Cbr>METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death) . The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4 .3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90–0.92) . In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60–0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status.\u003Cbr>CONCLUSIONS: The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.\u003Cbr>Key Words: carotid endarterectomy ■ machine learning ■ major adverse cardiovascular event ■ prediction |  |\n| --- | --- |\n| Carotid endarterectomy (CEA) is the surgical man\u003Cbr>agement option for carotid artery stenosis, which is responsible for approximately one-third of ischemic strokes worldwide.1 Given that CEA is a major vascular operation, the procedure carries a significant risk of complications.2 Schermerhorn and colleagues | showed that 30-day major adverse cardiovascular events (MACE) occur in >7% of high-risk patients undergoing CEA.3 To balance the benefits and risks of this intervention, the Society for Vascular Surgery (SVS) recommends maintaining a perioperative stroke or death rate of \u003C6% and \u003C3% in symptomatic and |\n\nCorrespondence to: Mohammed Al-Omran, MD, MSc, FRCSC, Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-074, Bond Wing, Toronto, ON M5B 1W8, Canada. [Email: mohammed.al-omran@unityhealth.to](Email: mohammed.al-omran@unityhealth.to)  \nThis work was presented in part at the Society for Vascular Surgery Annual Meeting, June 14-17, 2023.  \nThis article was sent to Neel S. Singhal, MD, PhD, Associate Editor, for review by expert referees, editorial decision, and final disposition.  \nSupplemental Mat","cbCaih4ZlSVGeA9t","https://ap.wps.com/l/cbCaih4ZlSVGeA9t","pdf",606132,1,11,"English","en",105,"# Background\n# Methods and Results\n# Conclusions\n# Clinical Perspective","[{\"question\":\"What outcome did the study predict after carotid endarterectomy?\",\"answer\":\"The primary outcome was 30-day major adverse cardiovascular events, a composite of stroke, myocardial infarction, or death.\"},{\"question\":\"Which machine learning model performed best in predicting the 30-day outcome?\",\"answer\":\"XGBoost was the best-performing model, reaching an area under the receiver operating characteristic curve of 0.91.\"},{\"question\":\"How can the resulting tools help in clinical practice?\",\"answer\":\"The models may guide risk-mitigation strategies for patients being considered for CEA by improving prediction of short-term postoperative outcomes.\"}]","Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning - Original Research | PDF",1785807179,28,{"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-major-adverse-cardiovascular-events-following-carotid-endarterectomy-using-machine-learning-original-research","",{"@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-major-adverse-cardiovascular-events-following-carotid-endarterectomy-using-machine-learning-original-research/121843/",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 outcome did the study predict after carotid endarterectomy?","Question",{"text":75,"@type":76},"The primary outcome was 30-day major adverse cardiovascular events, a composite of stroke, myocardial infarction, or death.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best in predicting the 30-day outcome?",{"text":80,"@type":76},"XGBoost was the best-performing model, reaching an area under the receiver operating characteristic curve of 0.91.",{"name":82,"@type":73,"acceptedAnswer":83},"How can the resulting tools help in clinical practice?",{"text":84,"@type":76},"The models may guide risk-mitigation strategies for patients being considered for CEA by improving prediction of short-term postoperative outcomes.","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"]