Electroencephalogram Biomarkers from Anesthesia Induction to Identify Vulnerable Patients at Risk for Postoperative Delirium

Abstract

Background Postoperative delirium is a common complication in elderly patients undergoing anesthesia. Even though it is increasingly recognized as an important health issue, the early detection of patients at risk for postoperative delirium remains a challenge. This study aims to identify predictors of postoperative delirium by analyzing frontal electroencephalogram at propofol-induced loss of consciousness. Methods This prospective, observational single-center study included patients older than 70 yr undergoing general anesthesia for a planned surgery. Frontal electroencephalogram was recorded on the day before surgery (baseline) and during anesthesia induction (1, 2, and 15 min after loss of consciousness). Postoperative patients were screened for postoperative delirium twice daily for 5 days. Spectral analysis was performed using the multitaper method. The electroencephalogram spectrum was decomposed in periodic and aperiodic (correlates to asynchronous spectrum wide activity) components. The aperiodic component is characterized by its offset (y intercept) and exponent (the slope of the curve). Computed electroencephalogram parameters were compared between patients who developed postoperative delirium and those who did not. Significant electroencephalogram parameters were included in a binary logistic regression analysis to predict vulnerability for postoperative delirium. Results Of 151 patients, 50 (33%) developed postoperative delirium. At 1 min after loss of consciousness, postoperative delirium patients demonstrated decreased alpha (postoperative delirium: 0.3 μV2 [0.21 to 0.71], no postoperative delirium: 0.55 μV2 [0.36 to 0.74]; P = 0.019] and beta band power [postoperative delirium: 0.27 μV2 [0.12 to 0.38], no postoperative delirium: 0.38 μV2 [0.25 to 0.48]; P = 0.003) and lower spectral edge frequency (postoperative delirium: 10.45 Hz [5.65 to 15.04], no postoperative delirium: 14.56 Hz [9.51 to 16.65]; P = 0.01). At 15 min after loss of consciousness, postoperative delirium patients displayed a decreased aperiodic offset (postoperative delirium: 0.42 μV2 (0.11 to 0.69), no postoperative delirium: 0.62 μV2 [0.37 to 0.79]; P = 0.004). The logistic regression model predicting postoperative delirium vulnerability demonstrated an area under the curve of 0.73 (0.69 to 0.75). Conclusions The findings suggest that electroencephalogram markers obtained during loss of consciousness at anesthesia induction may serve as electroencephalogram-based biomarkers to identify at an early time patients at risk of developing postoperative delirium. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Postoperative Delirium and the Older Adult: Untangling the Confusion

“…Preoperatively, these patients will show reduced beta and gamma power, and during transition to unconsciousness a reduced alpha power. 35 Other EEG patterns, such as intraoperative transition to theta activity, 36 presence of burst suppression, and specific emergence trajectories, can be used as predictive markers. 37 The effects of EEGguided anesthesia and other anesthetic practices on POD remain a matter of debate but have shown promise (Table 1).…”

“…Perioperative electroencephalogram (EEG) recordings have the potential to identify individuals at risk for brain dysfunction and could be used both to guide anesthesia by recognizing patterns of brain insult and to detect patients at risk for poor neurologic outcomes. Preoperatively, these patients will show reduced beta and gamma power, and during transition to unconsciousness a reduced alpha power 35 . Other EEG patterns, such as intraoperative transition to theta activity, 36 presence of burst suppression, and specific emergence trajectories, can be used as predictive markers 37 .…”

Normative characterization of age-related periodic and aperiodic activity in resting-state clinical EEG recordings

“…Recent methodological advancements offer an apt means to decompose the EEG power spectrum into rhythmic oscillations (periodic component) and non-rhythmic fluctuations (aperiodic component). It helps to dissect complex neural signals (Leroy et al, 2022), enrich our understanding of brain function (Lendner et al, 2020), identify potential biomarkers of disease (Pollak et al, 2024), and assess the effectiveness of a therapy (Kundu et al, 2023; Salvatore et al, 2023). The strength of the approach particularly stems from its reflection of the EEG signal’s two-fold nature, encompassing both its mathematical characteristics in signal analysis and its neurophysiological correlates.…”

A Dual-Camera Eye-Tracking Platform for Rapid Real-Time Diagnosis of Acute Delirium: A Pilot Study

“…Attempts at automating the diagnosis of delirium have been made but have been fraught with difficulties - quantitive Electroencephalogram (EEG) through the use of the BiSpectral Index (BIS) system has been found to correspond to arousal [18] , [19] . However, other EEG indices have been used with good metrics [20] , [21] , [22] , [23] . The main drawback of these techniques is that the patient is instrumented and is required to be stationary for the signal to be acquired, which can be difficult as delirious patients can be agitated and combative.…”