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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\u2009=\u20090.645, specificity\u2009=\u20090.692), indicating moderate predictive performance. Including demographic, clinical, and laboratory variables improved performance (AUC\u2009=\u20090.825, sensitivity\u2009=\u20090.613, specificity\u2009=\u20090.795). The model analyzing EEG features before and after delirium diagnosis achieved the highest accuracy (AUC\u2009=\u20090.950, sensitivity and specificity\u2009=\u20090.818), 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.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s11357-025-01809-0","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T16:53:06Z","timestamp":1753289586000},"page":"2617-2645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach"],"prefix":"10.1007","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8131-6570","authenticated-orcid":false,"given":"Ana","family":"Viegas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiana P.","family":"Von Rekowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u00faben","family":"Ara\u00fajo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu\u00eds","family":"Ramalhete","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"In\u00eas Menezes","family":"Cordeiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Manita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Viana-Baptista","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula","family":"Macedo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu\u00eds","family":"Bento","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"1809_CR1","doi-asserted-by":"publisher","first-page":"414","DOI":"10.62116\/MSJ.2021.30.6.414","volume":"30","author":"MA Mulkey","year":"2021","unstructured":"Mulkey MA, Olson DM, Misrahi S, Hardin SR. 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