{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T07:59:43Z","timestamp":1780905583784,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100011597","name":"Conselleria de Sanitat Universal i Salut P\u00fablica","doi-asserted-by":"publisher","award":["Programa INVESTIGO (INVEST\/2022\/298)"],"award-info":[{"award-number":["Programa INVESTIGO (INVEST\/2022\/298)"]}],"id":[{"id":"10.13039\/501100011597","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["ICMx"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While acute brain dysfunction (ABD, i.e., delirium and coma) is associated with significantly increased morbidity in critically ill patients, it presents with great heterogeneity that poses a challenge for management and prognostication. While machine learning may be promising for subgroup identification, this approach has not yet been applied to COVID-19 patients with ABD. The aim of our study was to identify distinct clusters among critically ill patients with COVID-19 based on ICU admission data and evaluate their association with clinical outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed an international multicenter database (COVID-D study) of critically ill adult patients with COVID-19 during the first pandemic wave and ABD using clinical features on day 1 of admission as input variables. We applied unsupervised machine learning in a pilot attempt to discover clusters of ABD patients. Hierarchical clustering was performed with a bootstrap-based robustness assessment after dimensionality reduction. Clusters were analyzed for differences in neurological outcomes, mechanical ventilation, and survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We analyzed 1,631 critically ill COVID-19 patients with ABD, identifying four reproducible clusters with distinct clinical and neurological profiles. Cluster 1 (\"mild respiratory failure,\u201d n\u2009=\u2009335) had the most favorable outcomes, with the shortest duration of delirium (4.13\u00a0days) and mechanical ventilation. Cluster 2 (\"moderate ARDS,\" n\u2009=\u2009508) showed a comparable delirium incidence but the longest duration (5.18\u00a0days). Cluster 3 (\"early severe ARDS,\" n\u2009=\u2009161) included patients who underwent prone positioning and mechanical ventilation early from the day of admission, with higher rates of coma (100%), including persistent coma (27.3%). Cluster 4 (\"late severe ARDS,\" n\u2009=\u2009475) represented severely ill patients with the longest coma duration (11.2\u00a0days) and the lowest delirium-free and coma-free (DFCF) days (4.74), in relation to deep and prolonged sedation. Despite the wide range of ABD durations across four groups, no significantly different 28-day mortality (23.6\u201338.0, p\u2009&gt;\u20090.78), ICU (15.8\u201319.2\u00a0days range, p\u2009=\u20090.154) and hospital (22.5\u201326.7\u00a0days range, p\u2009=\u20090.259) length of stay were observed among clusters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This pilot analysis of ICU admission data from the first COVID-19 wave suggests the existence of clinically distinct clusters among patients with acute brain dysfunction. Differences were observed in the type and duration of delirium and coma, though these did not translate into differences in 28-day survival. This exploratory work may support targeted delirium prevention strategies, but prospective studies are required to determine its clinical utility in modern ICU settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40635-026-00922-4","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T07:41:05Z","timestamp":1780904465000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Acute brain dysfunction clusters in COVID-19: a pilot machine learning-based analysis of the COVID-D cohort"],"prefix":"10.1186","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7914-7635","authenticated-orcid":false,"given":"Nekane","family":"Romero-Garc\u00eda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00edctor","family":"Montosa-i-Mic\u00f3","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Fern\u00e1ndez-Narro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan M.","family":"Garc\u00eda-G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Hurtado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Passariello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio Silvio","family":"Taccone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Robba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rameela","family":"Raman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onur M.","family":"Orun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pratik","family":"Pandharipande","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenda T.","family":"Pun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E. 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The IRB of Vanderbilt University Medical Center approved the study on 4\/23\/2020 (Ms. Maya Alexandra Salberg BS, CIM); then, ethical approval was also obtained at each site from local regulatory boards before data collection. At the centre where this secondary analysis was performed, the Ethical Committee of the Hospital Cl\u00ednico Universitario de Valencia approved the study under IRB number 2020\/150 (April 30th 2020, Dr. Palmero de la Cruz). The requirement for written informed consent was waived since data were deidentified and collected retrospectively.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}