[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127209-en":3,"doc-seo-127209-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127209,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction","Patients with occlusion myocardial infarction (OMI) presenting without ST-elevation on ECG are increasingly common and carry a poor prognosis, yet initial triage lacks accurate tools to identify them for immediate reperfusion. This observational cohort study developed and externally validated machine learning models for ECG-based OMI diagnosis using 7,313 consecutive patients across multiple sites. The derived OMI risk score improved rule-in and rule-out performance and, combined with emergency clinicians’ judgment, reclassified one in three chest pain patients while supporting mechanistic ECG feature interpretation.","nature medicine  \nArticle [https://doi.org/10.1038/s41591-023-02396-3](https://doi.org/10.1038/s41591-023-02396-3)  \nMachine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction  \nReceived: 24 January 2023  \nAccepted: 11 May 2023  \n\n| Published online: 29 June 2023 |\n| --- |\n|  Check for updates |\n\nSalah S. Al-Zaiti  1,2,3,4 , Christian Martin-Gill  2,5, Jessica K. Zègre-Hemsey  6, Zeineb Bouzid  3, Ziad Faramand7, Mohammad O. Alrawashdeh  8,9,  \nRichard E. Gregg10, Stephanie Helman1, Nathan T. Riek  3,  \nKarina Kraevsky-Phillips  1, Gilles Clermont11, MuratAkcakaya3,  \nSusan M. Sereika1, Peter Van Dam12, Stephen W. Smith  13,14,  \nYochai Birnbaum  15, Samir Saba4,5, Ervin Sejdic16,17 & Clifton W. Callaway2,5  \nPatients with occlusion myocardial infarction (OMI) and noST-elevation on presenting electrocardiogram (ECG) are increasing in numbers.  \nThese patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validatedan intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.  \nThe electrocardiogram (ECG) diagnosis of acute coronary syndrome (ACS) in patients with acute chest pain is a longstanding challenge in clinical practice1–4. Guidelines primarily focus on ST-segment elevation (STE) for discerning patients with ST-elevation myocardial infarction  \n(STEMI) versus other forms of ACS5–8. A biomarker-driven approach is recommended in the absence of STEon the presenting ECG. This diagnostic paradigm has two important limitations. First, around 24–35% of patients with non-STEMI have total coronary occlusion, referred  \n1Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA. 2Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA. 3Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA. 4Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA. 5University of Pittsburgh Medical Center, Pittsburgh, PA, USA. 6School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 7Department of Emergency Medicine, Northeast Georgia Health System, Gainesville, GA, USA. 8School of Nursing, Jordan University of Science and Technology, Irbid, Jordan. 9Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. 10Advanced Algorithm Development Center, Philips Healthcare, Cambridge, MA, USA. 11Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA. 12Division of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands. 13Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA. 14Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA. 15Division of Cardiology, Baylor College of Medicine, Houston, TX, USA. 16Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada. 17Artificial Intelligence for Health Outcomes at Research & Innovation, North York General Hospital, Toronto, ON, Canada. e-mail: [ssa33@pitt.edu](ssa33@pitt.edu)  \nModel bui lding & va l idat ion Sample selection  \nPittsburgh E","cbCaimUcR17JVeru","https://ap.wps.com/l/cbCaimUcR17JVeru","pdf",7902420,1,24,"English","en",105,"# Study background\n## Clinical limitations of current ECG and biomarker approaches\n## Need for improved early identification\n# Model development and validation\n## Cohort and sample selection\n## Training, internal testing, and external validation\n# Clinical impact and interpretability","[{\"question\":\"Why is occlusion myocardial infarction (OMI) hard to detect at triage?\",\"answer\":\"Patients with OMI often show no ST-elevation on the presenting ECG, making ECG-based identification challenging. Biomarkers such as troponin also cannot distinguish OMI until later peak levels.\"},{\"question\":\"How was the machine learning model for OMI diagnosis validated?\",\"answer\":\"The study derived models using 7,313 consecutive patients and performed external validation across multiple clinical sites. The results were benchmarked against practicing clinicians and other interpretation systems.\"},{\"question\":\"What practical benefit did the OMI risk score provide in emergency care?\",\"answer\":\"The risk score improved rule-in and rule-out accuracy and, when combined with clinicians’ judgment, helped correctly reclassify one in three patients presenting with chest pain.\"}]","Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction | PDF",1785937543,60,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-for-ecg-diagnosis-and-risk-stratification-of-occlusion-myocardial-infarction","",{"@graph":36,"@context":86},[37,54,69],{"@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-for-ecg-diagnosis-and-risk-stratification-of-occlusion-myocardial-infarction/127209/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is occlusion myocardial infarction (OMI) hard to detect at triage?","Question",{"text":76,"@type":77},"Patients with OMI often show no ST-elevation on the presenting ECG, making ECG-based identification challenging. Biomarkers such as troponin also cannot distinguish OMI until later peak levels.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the machine learning model for OMI diagnosis validated?",{"text":81,"@type":77},"The study derived models using 7,313 consecutive patients and performed external validation across multiple clinical sites. The results were benchmarked against practicing clinicians and other interpretation systems.",{"name":83,"@type":74,"acceptedAnswer":84},"What practical benefit did the OMI risk score provide in emergency care?",{"text":85,"@type":77},"The risk score improved rule-in and rule-out accuracy and, when combined with clinicians’ judgment, helped correctly reclassify one in three patients presenting with chest pain.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":29,"slug":109},5,"Comic","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":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":107,"slug":138},19,"General","general"]