Background: Postoperative delirium is a common complication associated with increased morbidity and mortality, longer hospital stays, and greater health care expenditures. Intraoperative electroencephalogram (EEG) slowing has been associated previously with postoperative delirium, but the relationship between intraoperative EEG suppression and postoperative delirium has not been investigated.
Methods: In this observational cohort study, 727 adult patients who received general anesthesia with planned intensive care unit admission were included. Duration of intraoperative EEG suppression was recorded from a frontal EEG channel (FP1 to F7). Delirium was assessed twice daily on postoperative days 1 through 5 with the Confusion Assessment Method for the intensive care unit. Thirty days after surgery, quality of life, functional independence, and cognitive ability were measured using the Veterans RAND 12-item survey, the Barthel index, and the PROMIS Applied Cognition-Abilities-Short Form 4a survey.
Results: Postoperative delirium was observed in 162 (26%) of 619 patients assessed. When we compared patients with no EEG suppression with those divided into quartiles based on duration of EEG suppression, patients with more suppression were more likely to experience delirium (χ(4) = 25, P < 0.0001). This effect remained significant after we adjusted for potential confounders (odds ratio for log(EEG suppression) 1.22 [99% confidence interval, 1.06-1.40, P = 0.0002] per 1-minute increase in suppression). EEG suppression may have been associated with reduced functional independence (Spearman partial correlation coefficient -0.15, P = 0.02) but not with changes in quality of life or cognitive ability. Predictors of EEG suppression included greater end-tidal volatile anesthetic concentration and lower intraoperative opioid dose.
Conclusions: EEG suppression is an independent risk factor for postoperative delirium. Future studies should investigate whether anesthesia titration to minimize EEG suppression decreases the incidence of postoperative delirium. This is a substudy of the Systematic Assessment and Targeted Improvement of Services Following Yearlong Surgical Outcomes Surveys (SATISFY-SOS) surgical outcomes registry (NCT02032030).
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http://dx.doi.org/10.1213/ANE.0000000000000989 | DOI Listing |
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Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
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