| Autor: |
Shawniqua, Williams Roberson, Naureen A, Azeez, Jenna N, Fulton, Kevin C, Zhang, Aaron X T, Lee, Fei, Ye, Pratik, Pandharipande, Nathan E, Brummel, Mayur B, Patel, E Wesley, Ely |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Clinical Neurophysiology. 146:40-48 |
| ISSN: |
1388-2457 |
| DOI: |
10.1016/j.clinph.2022.11.012 |
| Popis: |
To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics.Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99).We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients.Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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