[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121437-en":3,"doc-seo-121437-105":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":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},121437,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Predicting Delirium in Critically Ill COVID-19 Patients Using EEG-Derived Data - A Machine Learning Approach","Delirium is a severe, common complication in critically ill patients, especially those infected with SARS‑CoV‑2, driving higher morbidity and mortality. Prompt identification of patients at risk is essential for timely intervention and better outcomes. This prospective observational cohort study investigates whether electroencephalography (EEG) combined with machine learning models can predict delirium in critically ill SARS‑CoV‑2 patients. A stepwise approach starts with independent assessment of predictive EEG variables, then builds multivariable ML models to improve discrimination and interpret key EEG features.","GeroScience  \n[https://doi.org/10.1007/s11357-025-01809-0](https://doi.org/10.1007/s11357-025-01809-0)  \nPredicting delirium in critically Ill COVID‑19 patients using EEG‑derived data: a machine learning approach  \nAna Viegas · Cristiana P. Von Rekowski · Rúben Araújo ·  \nLuís Ramalhete · Inês Menezes Cordeiro · Manuel Manita ·  \nMiguel Viana‑Baptista · Paula Macedo · Luís Bento  \nReceived: 29 May 2025 / Accepted: 14 July 2025  \n© The Author(s) 2025  \nAbstract Delirium is a severe and common complication among critically ill patients, particularly those with SARS-CoV-2 infection, contributing to increased morbidity and mortality. Early identification of at-risk patients is crucial for timely intervention and improved outcomes. This prospective observational cohort study explores the potential of electroencephalography (EEG) combined with machine learning (ML) models for predicting delirium in critically ill patients with SARS-CoV-2 infection. A stepwise modeling approach was applied, starting with the independent analysis of specific EEG variables to assess their predictive value.  \nSupplementary Information The online version contains supplementary material available at [https://doi](https://doi). org/10.1007/s11357-025-01809-0.  \nA. Viegas (*) · C. P. Von Rekowski · R. Araújo ·  \nL. Ramalhete · M. Viana-Baptista · P. Macedo · L. Bento NMS – NOVA Medical School, FCM – Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal e-mail: [a2020449@nms.unl.pt](a2020449@nms.unl.pt)  \nA. Viegas · C. P. Von Rekowski · R. Araújo · L. Bento CHRC – Comprehensive Health Research Centre, Universidade NOVA de Lisboa, Campo Dos Mártires da Pátria 130, 1150-082 Lisbon, Portugal  \nA. Viegas  \nESTeSL – Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Avenida D. João II, Lote 4.69.01, Parque das Nações, 1990-096 Lisbon, Portugal  \nSubsequently, three ML models were developed using data from 70 patients (31 with delirium, 39 without): two relied solely on EEG data, while the third integrated demographic, clinical, laboratory, and EEG data. An additional model analyzed EEG data before and after delirium diagnosis in 11 patients. Several EEG features were identified as predictors of delirium, with increased theta activity emerging as the most consistent. The best EEG-only model achieved an area under the curve (AUC) of 0.733 (sensitivity = 0.645, specificity = 0.692), indicating moderate predictive performance. Including demographic, clinical, and laboratory variables improved performance (AUC = 0.825, sensitivity = 0.613, specificity = 0.795) . The model analyzing EEG features before and after delirium diagnosis achieved the  \nA. Viegas  \nH&TRC – Health & Technology Research Center, ESTeSL – Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Avenida D. João II, Lote  \n4.69.01, Parque das Nações, 1990-096 Lisbon, Portugal  \nA. Viegas · I. M. Cordeiro · M. Manita  \nNeurosciences Area, Clinical Neurophysiology Unit, ULSSJ – Unidade Local de Saúde São José, Rua José António Serrano, 1150-199 Lisbon, Portugal  \nC. P. Von Rekowski · R. Araújo  \nISEL– Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal  \nhighest accuracy (AUC = 0.950, sensitivity and specificity = 0.818), reinforcing the value of EEG-based monitoring. EEG-based ML models show promise for predicting delirium in critically ill patients, with  \nincreased theta activity identified as a key predictor. However, their moderate AUC, sensitivity, and specificity highlight the need for further refinement.  \nGraphical Abstract  \nKeywords Delirium · EEG · COVID-19 · SARSCoV-2 infection · ICU · Machine learning  \nIntroduction  \nDelirium, an acute confusional state characterized by fluctuating disturbances in attention and cognition,  \nL. Ramalhete  \nBlood and Transplantation Center of Lisbon","cbCaigp1ONCeVjrs","https://ap.wps.com/l/cbCaigp1ONCeVjrs","pdf",1203991,1,29,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is early delirium detection important in critically ill COVID-19 patients?\",\"answer\":\"Delirium increases morbidity and mortality and delays recovery. Early identification supports timely interventions and improved outcomes.\"},{\"question\":\"How does the study use EEG data with machine learning to predict delirium?\",\"answer\":\"It applies a stepwise modeling strategy: first evaluates the predictive value of individual EEG variables, then develops ML models using EEG alone or combined with demographic, clinical, and laboratory data.\"},{\"question\":\"Which EEG feature was most consistently associated with delirium risk?\",\"answer\":\"Increased theta activity emerged as the most consistent predictor across the models.\"}]","Predicting Delirium in Critically Ill COVID-19 Patients Using EEG-Derived Data - A Machine Learning Approach | PDF",1785735656,73,{"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-delirium-in-critically-ill-covid-19-patients-using-eeg-derived-data-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-delirium-in-critically-ill-covid-19-patients-using-eeg-derived-data-a-machine-learning-approach/121437/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early delirium detection important in critically ill COVID-19 patients?","Question",{"text":75,"@type":76},"Delirium increases morbidity and mortality and delays recovery. Early identification supports timely interventions and improved outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use EEG data with machine learning to predict delirium?",{"text":80,"@type":76},"It applies a stepwise modeling strategy: first evaluates the predictive value of individual EEG variables, then develops ML models using EEG alone or combined with demographic, clinical, and laboratory data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which EEG feature was most consistently associated with delirium risk?",{"text":84,"@type":76},"Increased theta activity emerged as the most consistent predictor across the models.","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"]