[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127759-en":3,"doc-seo-127759-105":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},127759,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods","COVID-19 mortality risk models often rely on small or unrepresentative samples and methodological constraints, limiting reliable decision-making at hospital entry. This study uses an international cohort dataset of over 800,000 hospitalized COVID-19 patients to build a cost-sensitive gradient-boosted machine learning model predicting pulmonary embolism and death at admission. Logistic regression, Cox proportional hazards, and Shapley values identify key predictors, achieving AUROC of 75.9% and 74.2% for PE and all-cause mortality on a diverse held-out test set, with further UK and Spain validation.","University of Groningen  \nAt-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods  \nMazankowski Heart Institute; ISARIC Characterisation Group; Mesinovic, Munib; Wong, Xin Ci; Rajahram, Giri Shan; Citarella, Barbara Wanjiru; Peariasamy, Kalaiarasu M. ; van Someren Greve, Frank; Olliaro, Piero; Merson, Laura  \nPublished in: Scientific Reports  \nDOI:  \n10.1038/s41598-024-63212-7  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nMazankowski Heart Institute, ISARIC Characterisation Group, Mesinovic, M. , Wong, X. C. , Rajahram, G. S. , Citarella, B. W. , Peariasamy, K. M. , van Someren Greve, F. , Olliaro, P. , Merson, L. , Clifton, L. , & Kartsonaki, C. (2024) . At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods. Scientific Reports, 14(1), Article 16387. [https://doi.org/10.1038/s41598-024-63212-7](https://doi.org/10.1038/s41598-024-63212-7)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-01-2026  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAt‑admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID‑19 using statistical and machine learning methods  \nMunib Mesinovic1*, Xin Ci Wong2, Giri Shan Rajahram3, Barbara Wanjiru Citarella4, Kalaiarasu M. Peariasamy2, Frank van Someren Greve5, Piero Olliaro4, Laura Merson4, Lei Clifton6, Christiana Kartsonaki6 & ISARIC Characterisation Group*  \nBy September 2022, more than 600 million cases of SARS‑CoV‑2 infection have been reported globally, resulting in over 6.5 million deaths. COVID‑19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID‑19 patients as one of the most severe preventable complications ofCOVID‑19. Early recognition can help provide life‑saving targeted anti‑coagulation therapy right at admission. Using a dataset of more than 800,000 COVID‑19 patients from an international cohort, we propose a cost‑sensitive gradient‑boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to iden","cbCaitUcla24giVV","https://ap.wps.com/l/cbCaitUcla24giVV","pdf",5931170,1,44,"English","en",105,"# Clinical background\n## Data-driven prediction approach\n## Model evaluation and key predictors","[{\"question\":\"What does the study predict at admission for hospitalized COVID-19 patients?\",\"answer\":\"It predicts the occurrence of pulmonary embolism and all-cause mortality directly at hospital admission using an international cohort dataset.\"},{\"question\":\"How were key predictors for pulmonary embolism and death identified?\",\"answer\":\"The study uses logistic regression, Cox proportional hazards models, and Shapley values to determine important predictors for PE and mortality.\"},{\"question\":\"What performance did the prediction models achieve on the held-out test set?\",\"answer\":\"On a diverse held-out test set, the PE model reached a test AUROC of 75.9% with 67.5% sensitivity, and mortality achieved a test AUROC of 74.2% with 72.7% sensitivity.\"}]","At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods | 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does the study predict at admission for hospitalized COVID-19 patients?","Question",{"text":76,"@type":77},"It predicts the occurrence of pulmonary embolism and all-cause mortality directly at hospital admission using an international cohort dataset.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were key predictors for pulmonary embolism and death identified?",{"text":81,"@type":77},"The study uses logistic regression, Cox proportional hazards models, and Shapley values to determine important predictors for PE and mortality.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance did the prediction models achieve on the held-out test set?",{"text":85,"@type":77},"On a diverse held-out test set, the PE model reached a test AUROC of 75.9% with 67.5% sensitivity, and mortality achieved a test AUROC of 74.2% with 72.7% 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