[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121273-en":3,"doc-seo-121273-105":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},121273,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery - A Machine Learning Approach","Patient Blood Management benefits vary with each patient’s risk of needing red blood cell transfusion, motivating accurate early stratification. This retrospective cohort study developed and evaluated machine learning models to identify patients at risk in elective cardiac surgery using data from a tertiary Portuguese hospital (2018–2023). Extreme gradient boosting and neural networks were trained, with feature contributions assessed via Shapley additive explanations. Neural networks achieved accuracy of 0.735 and AUC 0.798, while XGBoost reached accuracy 0.700 and AUC 0.762, highlighting preoperative haemoglobin as the most influential variable.","Article  \nPredicting Red Blood Cell Transfusion in Elective Cardiac Surgery: A Machine Learning Approach  \nBeatriz Lau 1,*, Daniel Ramos 1, Vera Afreixo 1, Luís M. Silva 1, Ana Helena Tavares 1, Miguel Martins Felgueiras 1,2, Diana Castro Paupério 3,4 and João Firmino-Machado 3,4,5  \nAcademic Editor: Leonardo Trujillo  \nReceived: 29 December 2024  \nRevised: 14 February 2025  \nAccepted: 20 February 2025  \nPublished: 24 February 2025  \nCitation: Lau, B.; Ramos, D.; Afreixo, V.; Silva, L.M.; Tavares, A.H.;  \nFelgueiras, M.M.; Castro Paupério, D.; Firmino-Machado, J. Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery: A Machine Learning Approach. Math. Comput. Appl. 2025, 30, 22. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)mca30020022  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Centre for Research and Development in Mathematics and Applications, University of Aveiro, 3810-193 Aveiro, Portugal; [danielsramos@ua.pt](danielsramos@ua.pt) (D.R.); [vera@ua.pt](vera@ua.pt) (V.A.); [lmas@ua.pt](lmas@ua.pt) (L.M.S.); [ahtavares@ua.pt](ahtavares@ua.pt) (A.H.T.); [mfelg@ipleiria.pt](mfelg@ipleiria.pt) (M.M.F.)  \n2 Polytechnic of Leiria and Centre for Statistics and Applications, University of Leiria, 2411-901 Leiria, Portugal  \n3 Local Health Unit of Vila Nova de Gaia Espinho, 4434-502 Vila Nova de Gaia, Portugal; [diana.gomes@gmail.com](diana.gomes@gmail.com) (D.C.P.); [firmino@ua.pt](firmino@ua.pt) (J.F.-M.)  \n4 Egas Moniz Health Alliance Academic Clinical Centre, 3810-193 Aveiro, Portugal  \n5 Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal  \n* [Correspondence: beatrizlau@ua.pt](Correspondence: beatrizlau@ua.pt)  \nAbstract: The benefits of Patient Blood Management can vary depending on a patient’s risk profile for requiring a blood transfusion. The objective of this study is to develop and analyse machine learning models that can identify patients at risk of requiring red blood cell transfusion. This retrospective cohort study was conducted at a tertiary northern Portuguese hospital between 2018 and 2023 . Two machine learning algorithms, extreme gradient boosting and neural networks, were employed due to their efficiency in handling complex feature interactions. Shapley additive explanations values were analysed to assess the contribution of each feature to the predictions generated by the models. The neural network achieved an accuracy of 0.735 and an area under the receiver operating characteristic curve of 0.798 (95% CI 0.747 to 0.849) . The extreme gradient boosting model achieved an accuracy of 0.700 and an area under the receiver operating characteristic curve of 0.762 (95% CI 0.707 to 0.817) . An analysis of Shapley additive explanations values revealed that the most important variable was preoperative haemoglobin levels, which can be optimised through the Patient Blood Management approach. These machine learning models demonstrate the potential to improve the accuracy of transfusion prediction at hospital admission, despite the absence of key variables such as surgeon identity and anaemia diagnosis.  \nKeywords: machine learning; cardiac surgery; blood transfusion  \n1. Introduction  \nCardiac surgery is well established as being associated with a considerable risk of perioperative blood loss and the need for allogeneic blood transfusions. This is due to the invasive nature of the procedures themselves, the necessity for high-dose anticoagulation, and exposure to cardiopulmonary bypass [1] . Despite its common practice, an increasing body of evidence suggests that the transfusion of one or more allogeneic blood components may be associated with an increased ","cbCaif7MwPGUbhe4","https://ap.wps.com/l/cbCaif7MwPGUbhe4","pdf",1858968,1,14,"English","en",105,"# Introduction\n## Patient Blood Management in cardiac surgery\n## Motivation for transfusion risk stratification\n## Machine learning models for prediction\n# Methods\n## Study design and data source\n## Model development and evaluation","[{\"question\":\"What is the goal of the study?\",\"answer\":\"To develop and analyze machine learning models that identify patients at risk of requiring red blood cell transfusion in elective cardiac surgery.\"},{\"question\":\"Which machine learning algorithms were used?\",\"answer\":\"Extreme gradient boosting (XGBoost) and neural networks were used due to their efficiency in capturing complex feature interactions.\"},{\"question\":\"Which factor was found to be most important for predictions?\",\"answer\":\"Preoperative haemoglobin levels were identified as the most important variable, and they can be optimized through the Patient Blood Management approach.\"}]","Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery - A Machine Learning Approach | PDF",1785734839,35,{"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-red-blood-cell-transfusion-in-elective-cardiac-surgery-a-machine-learning-approach","",{"@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/predicting-red-blood-cell-transfusion-in-elective-cardiac-surgery-a-machine-learning-approach/121273/",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},"What is the goal of the study?","Question",{"text":75,"@type":76},"To develop and analyze machine learning models that identify patients at risk of requiring red blood cell transfusion in elective cardiac surgery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were used?",{"text":80,"@type":76},"Extreme gradient boosting (XGBoost) and neural networks were used due to their efficiency in capturing complex feature interactions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factor was found to be most important for predictions?",{"text":84,"@type":76},"Preoperative haemoglobin levels were identified as the most important variable, and they can be optimized through the Patient Blood Management approach.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":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":106,"slug":138},19,"General","general"]