Association of electroencephalogram trajectories during emergence from anaesthesia with delirium in the postanaesthesia care unit: an early sign of postoperative complications

Abstract

Background: Postoperative delirium is associated with an increased risk of morbidity and mortality, especially in the elderly. Delirium in the postanaesthesia care unit (PACU) could predict adverse clinical outcomes.

Methods: We investigated a potential link between intraoperative EEG patterns and PACU delirium as well as an association of PACU delirium with perioperative outcomes, readmission and length of hospital stay. The risk factors for PACU delirium were also explored. Data were collected from 626 patients receiving general anaesthesia for procedures that would not interfere with frontal EEG recording.

Results: Of the 626 subjects enrolled, 125 tested positive for PACU delirium. Whilst age, renal failure, and pre-existing neurological disease were associated with PACU delirium in the univariable analysis, the multivariable analysis revealed the importance of information derived from the EEG, anaesthetic technique, anaesthesia duration, and history of stroke or neurodegenerative disease. The occurrence of EEG burst suppression during maintenance [odds ratio (OR)=1.86 (1.13-3.05)] and the type of EEG emergence trajectory may be predictive of PACU delirium. Specifically, EEG emergence trajectories lacking significant spindle power were strongly associated with PACU delirium, especially in cases that involved ketamine or nitrous oxide [OR=6.51 (3.00-14.12)]. Additionally, subjects with PACU delirium were at an increased risk for readmission [OR=2.17 (1.13-4.17)] and twice as likely to stay >6 days in the hospital.

Conclusions: Specific EEG patterns were associated with PACU delirium. These findings provide valuable information regarding how the brain reacts to surgery and anaesthesia that may lead to strategies to predict PACU delirium and identify key areas of investigation for its prevention.

Keywords: EEG; delirium; general anaesthesia, complications; intraoperative monitoring; neurocognitive disorders; postoperative outcome; recovery room.

Published by Elsevier Ltd.

Figures

Fig 1

Summary of subject enrolment. Subjects were recruited at four sites. The number of excluded subjects is displayed in the red boxes. Median age is shown with 25th and 75th percentiles.

Fig 2

Different emergence trajectories. Emergence from anaesthesia can take different trajectories. The trajectory used as reference (1, dark green) was the most favourable (lowest odds of PACU delirium). It started with a delta-dominant slow-wave anaesthesia (ddSWA) EEG pattern, followed by an episode of spindle-dominant slow-wave anaesthesia (sdSWA) EEG and non-slow-wave anaesthesia (nSWA) EEG, before returning to the awake state. Trajectories 2 and 6 that also contained episodes of sdSWA EEG had an odds ratio around 2 (light green) of being at risk for PACU delirium. The trajectories with higher risk (i.e. an odds ratio of around 4 or 8 are depicted in red colours). Trajectory 7 is not presented in this plot, because this category pools all ‘other’ trajectories that could not be clearly assigned to Trajectories 1–6.

Fig 3

Spectrograms and assigned stages for two exemplary patients. Spectrograms of two representative patients are in the top two panels. Start of emergence begins at 600 s in both examples, and return of responsiveness to verbal stimulation is at the end of the traces. The bottom two panels are hypnograms showing the corresponding progression of the EEG through various stages during emergence (nSWA, non-slow-wave anaesthesia; sdSWA, spindle-dominant slow-wave anaesthesia; ddSWA, delta-dominant slow-wave anaesthesia), as described. The patient on the left (29 yr of age) transitions from a delta-dominant state to a period of spindle dominance before entering a non-slow-wave state before waking (i.e. Trajectory 1). In contrast, the patient depicted on the right (75 yr of age) remains in a delta-dominant state right until return of responsiveness (i.e. Trajectory 4).

Fig 4

Bar plots of the EEG trajectory (1–7) compared with (A) presence and absence of PACU delirium (PACU-D), (B) presence and absence of burst suppression, and (C) PACU-D and burst suppression relationship. (A) Patients with emergence trajectories 1, 2, and 6 have the lowest PACU-D (red) to no PACU-D (blue) ratio, and those with trajectories 3, 4, and 5 have the highest PACU-D to no PACU-D ratio. (B) Black indicates the number of patients with burst suppression during maintenance; white indicates the number of patients without burst suppression. (C) This plot is a combination of data presented in (A) and (B). Blue and red indicate the number of patients without PACU-D (blue) and with PACU-D (red). Individual patients who exhibited burst suppression during maintenance are indicated with black bars. No significant interaction between maintenance burst suppression and emergence trajectory (P=0.591) was observed.

Fig 5

Adjusted odds ratios from multivariable logistic regression. Odds ratios and [95% confidence intervals (CIs)] calculated from the model described in Methods and Supplementary information of the relevant EEG and non-EEG parameters. *Odds ratios adjusted for interaction with another co-variate. Legend quantities are odds ratios. See text for an explanation of confounding regarding spine surgery.

Supplementary Fig S1

Flow chart of the multivariate model design. The parameters chosen for the multivariate model were selected according to previous findings from the literature, significant results in the univariate analysis, and the EEG parameter burst suppression during maintenance and emergence trajectories. Some parameters could fit in multiple categories (e.g. age: literature and univariate analysis, or burst suppression during maintenance: EEG and univariate analysis).

Supplementary Fig S2

Observed and predicted frequencies of PACU-D were not significantly different based on the results of the Hosmer–Lemeshow goodness-of-fit test (HL statistic=7.44; degrees of freedom=8; P=0.491). PACU-D, PACU delirium.

Supplementary Fig S3

Using the multivariate model to estimate likelihood of PACU-D in our patient sample.