[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128073-en":3,"doc-seo-128073-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128073,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department - a machine learning approach","Hemoglobin variation induced by fluid shift across the intestinal barrier has been proposed as a marker to detect hydrostatic pulmonary oedema, yet its value in emergency department practice remains insufficiently established. An observational retrospective monocentric study recorded Hb at ED presentation and after 4 to 8 hours, computed absolute and relative ΔHb, and compared HPO versus non-HPO definitions. A machine learning model using ΔHb and baseline characteristics produced strong discrimination and identified ΔHb as the most important predictor for reliable HPO detection.","Gavelli etal. International Journal of Emergency Medicine (2024) 17:114  \n[https://doi.org/10.1186/s12245-024-00698-y](https://doi.org/10.1186/s12245-024-00698-y)  \nInternational Journal of Emergency Medicine  \nRESEARCH Open Access  \nDecrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department – a machine learning approach  \nFrancesco Gavelli 1,2,3*, Luigi Mario Castello 1,2, Xavier Monnet3, Danila Azzolina4, Ilaria Nerici 1,2, Simona Priora 1,2, Valentina Giai Via 1,2, Matteo Bertoli 1,2, Claudia Foieni 1,2, Michela Beltrame 1,2, Mattia Bellan 1, Pier Paolo Sainaghi 1, Nello De Vita 1, Filippo Patrucco 1, Jean-Louis Teboul3 and Gian Carlo Avanzi 1,2  \nAbstract  \nBackground Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO) . However, its use in the emergency department (ED) setting still needs to be determined.  \nMethods In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1) . ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics.  \nResults Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p \u003C 0.001) . ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8 .2 [-11 . 2 to-5 . 6] vs. 0.6 [-2 .1 to 3 . 3]%) and absolute (-1.0 [-1.4 to-0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p \u003C 0.001) . A relative ΔHbT1-T0 of-5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0 .896–0. 906] . Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction.  \nConclusions ΔHbT1-T0 in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO.  \nKeywords Pulmonary edema, Lung water, Haemoconcentration, Haemodilution, Augmented intelligence  \n*Correspondence: Francesco Gavelli [francesco.gavelli@uniupo.it](francesco.gavelli@uniupo.it)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/l](creativecommons.org/l)icenses/by-nc-nd/4.0/.  \nGavelli et al. International J","cbCaiuQSMRW2GL2a","https://ap.wps.com/l/cbCaiuQSMRW2GL2a","pdf",1346052,2,1,9,"English","en",105,"# Background\n# Methods\n## Study design and patient evaluation\n## Machine learning model\n# Results\n# Conclusions","[{\"question\":\"What clinical problem does this study address?\",\"answer\":\"The study targets reliable identification of hydrostatic pulmonary oedema in patients presenting to the emergency department with acute dyspnoea, where differential diagnosis is often difficult.\"},{\"question\":\"How is haemoconcentration assessed in this research?\",\"answer\":\"Haemoglobin values are measured at ED presentation (T0) and again after 4 to 8 hours (T1), and the change (absolute and relative ΔHb between T1 and T0) is used to evaluate HPO detection.\"},{\"question\":\"What role does the machine learning approach play?\",\"answer\":\"Machine learning is used to build a predictive tool for HPO by incorporating ΔHb as a key covariate together with baseline patient characteristics.\"}]","Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department - a machine learning approach | PDF",1785944671,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"decrease-of-haemoconcentration-reliably-detects-hydrostatic-pulmonary-oedema-in-dyspnoeic-patients-in-the-emergency-department-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/decrease-of-haemoconcentration-reliably-detects-hydrostatic-pulmonary-oedema-in-dyspnoeic-patients-in-the-emergency-department-a-machine-learning-approach/128073/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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},"What clinical problem does this study address?","Question",{"text":76,"@type":77},"The study targets reliable identification of hydrostatic pulmonary oedema in patients presenting to the emergency department with acute dyspnoea, where differential diagnosis is often difficult.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is haemoconcentration assessed in this research?",{"text":81,"@type":77},"Haemoglobin values are measured at ED presentation (T0) and again after 4 to 8 hours (T1), and the change (absolute and relative ΔHb between T1 and T0) is used to evaluate HPO detection.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does the machine learning approach play?",{"text":85,"@type":77},"Machine learning is used to build a predictive tool for HPO by incorporating ΔHb as a key covariate together with baseline patient characteristics.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]