{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:20:07Z","timestamp":1788326407364,"version":"build-2803163510"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019003","name":"Johns Hopkins Department of Anesthesiology and Critical Care Medicine","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019003","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Acute brain injury (ABI) is a major cause of mortality and morbidity during extracorporeal membrane oxygenation (ECMO), yet early diagnosis remains challenging because neuroimaging is often impractical in critically ill patients. We evaluated whether quantitative electroencephalography (qEEG) combined with machine learning could identify ABI and predict mortality in patients receiving ECMO. Methods: Consecutive adult ECMO patients who underwent a standardized neuromonitoring protocol with continuous EEG during sedation interruption were retrospectively analyzed. Quantitative EEG features and clinical variables were extracted and used to train multiple machine-learning classifiers with leave-one-subject-out cross-validation. Results: Fifty-seven patients were included (mean age 56 years; 54% male), including 41 supported with venoarterial ECMO, 15 with venovenous ECMO, and one with venoarterial-venous ECMO. ABI occurred in 21 patients (37%), of whom 70% had ischemic injury. Models incorporating qEEG achieved higher point estimates than those using clinical variables alone for ABI detection (best area under the curve (AUC) 0.769, 95% confidence interval (CI) 0.638\u20130.883, vs. 0.681), although the difference did not reach statistical significance. Frontal theta power and interhemispheric asymmetry were the EEG features most strongly associated with ABI. qEEG features also carried prognostic information for 30-day mortality (best AUC 0.864). Conclusions: Machine-learning analysis of continuous qEEG acquired during standardized sedation interruption may provide a noninvasive bedside approach for identifying ECMO patients at increased risk of ABI and short-term mortality and may help prioritize urgent neuroimaging and neurological intervention.<\/jats:p>","DOI":"10.3390\/jcm15176761","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:45:13Z","timestamp":1788241513000},"page":"6761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Critical Care Monitoring in Comatose Patients Receiving ECMO: A Cohort Study of Machine Learning on Quantitative EEG for Diagnosing Acute Brain Injury and Prognosticating Mortality"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5967-584X","authenticated-orcid":false,"given":"Mingfeng","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8933-9822","authenticated-orcid":false,"given":"Jeffrey B.","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Neuroscience Critical Care, 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