Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2025
The Predictive Power of Intraoperative EEG and Clinical Characteristics for Postoperative Delirium Following Cardiac Surgery.
СтатьяНазвание и аннотация: Перевод в очереди
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Wiredu Kwame, Sun Haoqi, Boncompte Gonzalo, Westover M Brandon, Pedemonte Juan C, Akeju Oluwaseun
Аннотация
INTRODUCTION: Postoperative delirium is common and associated with poor postoperative outcomes. However, the predictive power of intraoperative electroencephalogram (EEG) features for postoperative delirium has not yet been well studied.
METHODS: Intraoperative EEG data from 261 patients who underwent major cardiac surgery were analyzed. Cases were identified using the Confusion Assessment Method. Predictive analytics for delirium outcome were performed using (1) only clinical data, (2) only EEG data, and (3) a combined list of important features from the first two stages.
RESULTS: Eleven percentage of participants experienced postoperative delirium. The patients were generally older and had lower physical and cognitive function. EEG models were found to be highly specific but less sensitive in identifying delirium cases. The combined EEG-clinical model performed comparably to the clinical-only model (AUC = 80%) but outperformed the EEG-only model (AUC = 56%). After adjusting for clinical covariates, only interhemispheric mutual information remained significantly associated with delirium (OR = 2.29, p = 0.03), with a positive correlation with delirium severity (ρ = 0.18, P ≤ 0.01).
CONCLUSIONS: This study enhances our understanding of delirium neurophysiology by emphasizing the role of intraoperative EEG as a marker of brain vulnerability. Although EEG may not constitute a standalone biomarker of delirium, it holds promise for delirium risk stratification.
METHODS: Intraoperative EEG data from 261 patients who underwent major cardiac surgery were analyzed. Cases were identified using the Confusion Assessment Method. Predictive analytics for delirium outcome were performed using (1) only clinical data, (2) only EEG data, and (3) a combined list of important features from the first two stages.
RESULTS: Eleven percentage of participants experienced postoperative delirium. The patients were generally older and had lower physical and cognitive function. EEG models were found to be highly specific but less sensitive in identifying delirium cases. The combined EEG-clinical model performed comparably to the clinical-only model (AUC = 80%) but outperformed the EEG-only model (AUC = 56%). After adjusting for clinical covariates, only interhemispheric mutual information remained significantly associated with delirium (OR = 2.29, p = 0.03), with a positive correlation with delirium severity (ρ = 0.18, P ≤ 0.01).
CONCLUSIONS: This study enhances our understanding of delirium neurophysiology by emphasizing the role of intraoperative EEG as a marker of brain vulnerability. Although EEG may not constitute a standalone biomarker of delirium, it holds promise for delirium risk stratification.