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Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach

2025-07Artigo científico Acesso aberto
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datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorViegas, Ana
dc.contributor.authorVon Rekowski, Cristiana P.
dc.contributor.authorAraújo, Rúben
dc.contributor.authorRamalhete, Luís
dc.contributor.authorCordeiro, Inês Menezes
dc.contributor.authorManita, Manuel
dc.contributor.authorViana-Baptista, Miguel
dc.contributor.authorMacedo, Paula
dc.contributor.authorBento, Luís
dc.date.accessioned2025-12-17T14:40:34Z
dc.date.available2025-12-17T14:40:34Z
dc.date.issued2025-07
dc.descriptionOpen access funding provided by FCT|FCCN (b-on). This research was funded by the project grant DSAIPA/DS/0117/2020, supported by FCT – Fundação para a Ciência e Tecnologia, I.P.
dc.description.abstractDelirium 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. Subsequently, 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 highest 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 increased theta activity identified as a key predictor. However, their moderate AUC, sensitivity, and specificity highlight the need for further refinement.eng
dc.identifier.citationViegas A, Von Rekowski CP, Araújo R, Ramalhete L, Cordeiro IM, Manita M, et al. Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach. Geroscience. 2025 Jul 23. Epub ahead of print.
dc.identifier.doi10.1007/s11357-025-01809-0
dc.identifier.issn2509-2723
dc.identifier.urihttp://hdl.handle.net/10400.21/22353
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Science and Business Media LLC
dc.relation.hasversionhttps://link.springer.com/article/10.1007/s11357-025-01809-0
dc.relation.ispartofGeroScience
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCOVID-19
dc.subjectDelirium
dc.subjectEEG
dc.subjectICU
dc.subjectMachine learning
dc.subjectSARS-CoV-2
dc.titlePredicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approacheng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleGeroScience
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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