[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128730-en":3,"doc-seo-128730-105":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},128730,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients - A Spanish Perspective","Bacterial/fungal coinfections are linked to antibiotic overuse and worse critical-care outcomes, including prolonged ICU stay and higher mortality. The study develops machine learning–based predictive models to recognize respiratory bacterial or fungal coinfection at ICU admission in patients with influenza A and SARS-CoV-2. Secondary analysis covers two prospective multicenter cohorts; multiple logistic regression and random forests identify associated factors, with performance evaluated using ROC AUC and out-of-bag validation.","antibiotics   \nArticle  \nA Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection:  \nA Spanish Perspective  \nAlejandro Rodríguez 1,2,3,4, *,†, Josep Gómez 2,5,†, Ignacio Martín-Loeches 6, Laura Claverias 1,3, Emili Díaz 7,8, Rafael Zaragoza 9, Marcio Borges-Sa 10, Frederic Gómez-Bertomeu 2,3,11,12, Álvaro Franquet 2,5, Sandra Trefler 1,3, Carlos González Garzón 13, Lissett Cortés 13, Florencia Alés 14, Susana Sancho 15, Jordi Solé-Violán 16,  \nÁngel Estella 17,18, Julen Berrueta 1,19, Alejandro García-Martínez 1,19, Borja Suberviola 20,  \nJuan J. Guardiola 21 and María Bodí 1,2,3 on behalf of GETGAG/COVID-19 SEMICYUC Working Group  \nCitation: Rodríguez, A.; Gómez, J.; Martín-Loeches, I.; Claverias, L.; Díaz, E.; Zaragoza, R.; Borges-Sa, M.; Gómez-Bertomeu, F.; Franquet, Á .; Trefler, S.; et al. A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective.  \nAntibiotics 2024, 13, 968. [https://](https://)  \n[doi.org/10.3390/antibiotics13100968](doi.org/10.3390/antibiotics13100968)[ ](doi.org/10.3390/antibiotics13100968)Academic Editor: Giovanna Travi  \nReceived: 13 September 2024  \nRevised: 9 October 2024  \nAccepted: 10 October 2024  \nPublished: 14 October 2024  \nCopyright: © 2024 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Critical Care Department, Hospital Universitari Joan XXIII, 43005 Tarragona, Spain;  \nlauraclaverias@gmail.com (L.C.); sitrefler@yahoo.es (S.T.); julen.berrueta@estudiants.urv.cat (J.B.);  \nalejgarcia.hj23.ics@gencat.cat (A.G.-M.); mbodi.hj23.ics@gencat.cat (M.B.)  \n2 Faculty of Medicine, Universitat Rovira & Virgili, 43005 Tarragona, Spain; [josep.goal@gmail.com](josep.goal@gmail.com) (J.G.);  \nffgomez.hj23 .ics@gencat.cat (F.G.-B.); afranquet.hj23 .ics@gencat.cat (Á .F.)  \n3 Pere Virgili Health Research Institute, 43005 Tarragona, Spain  \n4 Centre for Biomedical Research Network Respiratory Diseases (CIBERES), 43005 Tarragona, Spain  \n5 Technical Secretary’s Department, Hospital Universitari Joan XXIII, 43005 Tarragona, Spain  \n6 Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospita, D08 NHY1 Dublin, Ireland; [drmartinloeches@gmail.com](drmartinloeches@gmail.com)  \n7 Critical Care Department, Hospital Universitari Parc Tauli, 08208 Sabadell, Spain; [emilio.diaz.santos@gmail.com](emilio.diaz.santos@gmail.com)  \n8 Medicine Faculty, Universitat Autónoma de Barcelona, 08193 Sabadell, Spain  \n9 Critical Care Department, Hospital Dr. Peset, 46017 Valencia, Spain; zaragoza_[raf@gva.es](raf@gva.es)  \n10 Critical Care Department, Hospital Son Llatzer, 07198 Palma de Mallorca, Spain; [marcio.borges.sa1967@gmail.com](marcio.borges.sa1967@gmail.com)  \n11 Microbiology/Clinical Analysis Laboratory, Hospital Universitari de Tarragona Joan XXIII,  \n43005 Tarragona, Spain  \n12 Center for Biomedical Research in Infectious Diseases Network (CIBERINFEC), 28220 Madrid, Spain  \n13 Postgrado Medicina Crítica y Cuidado Intensivo, Facultad de Medicina, Fundación Universitari Ciencias dela Salud, Distrito Especial, Cra. 54 No.67A-80, Bogotá 111221, Colombia; [carlosmariogonzalezgarzon@hotmail.com](carlosmariogonzalezgarzon@hotmail.com) (C.G.G.); [llcortes@fucsalud.edu.co](llcortes@fucsalud.edu.co) (L.C.)  \n14 Internal Medicine Department, Hospital Dr. Alejandro Gutiérrez, Venado Tuerto S2600, Argentina; [mafloales@gmail.com](mafloales@gmail.com)  \n15 Critical Care Department, Hospital Universitrio y Politécnico La Fe, 46026 Valencia, Spain","cbCailFhF5mOU7Vj","https://ap.wps.com/l/cbCailFhF5mOU7Vj","pdf",855478,3,1,17,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results","[{\"question\":\"What is the document’s main objective?\",\"answer\":\"To develop machine learning predictive models that identify respiratory bacterial or fungal coinfections upon ICU admission in critically ill patients with influenza and SARS-CoV-2 infection.\"},{\"question\":\"Which modeling methods are used?\",\"answer\":\"The study uses multiple logistic regression (MLR) and random forest (RF) to identify factors associated with bacterial/fungal coinfection in the overall population and in influenza and COVID-19 subgroups.\"},{\"question\":\"How is model performance assessed?\",\"answer\":\"Performance is evaluated using ROC area under the curve (AUC) and out-of-bag (OOB) validation for the respective models.\"}]","A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients - A Spanish Perspective | PDF",1786002922,43,{"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},"a-machine-learning-approach-to-determine-risk-factors-for-respiratory-bacterialfungal-coinfection-in-critically-ill-patients-a-spanish-perspective","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-determine-risk-factors-for-respiratory-bacterialfungal-coinfection-in-critically-ill-patients-a-spanish-perspective/128730/",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","2026-08-06",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 is the document’s main objective?","Question",{"text":76,"@type":77},"To develop machine learning predictive models that identify respiratory bacterial or fungal coinfections upon ICU admission in critically ill patients with influenza and SARS-CoV-2 infection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which modeling methods are used?",{"text":81,"@type":77},"The study uses multiple logistic regression (MLR) and random forest (RF) to identify factors associated with bacterial/fungal coinfection in the overall population and in influenza and COVID-19 subgroups.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance assessed?",{"text":85,"@type":77},"Performance is evaluated using ROC area under the curve (AUC) and out-of-bag (OOB) validation for the respective models.","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},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"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":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]