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

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.

Desflurane is risk factor for postoperative delirium in older patients' independent from intraoperative burst suppression duration.

Background: Postoperative Delirium (POD) is the most frequent neurocognitive complication after general anesthesia in older patients. The development of POD is associated with prolonged periods of burst suppression activity in the intraoperative electroencephalogram (EEG). The risk to present burst suppression activity depends not only on the age of the patient but is also more frequent during propofol anesthesia as compared to inhalative anesthesia. The aim of our study is to determine, if the risk to develop POD differs depending on the anesthetic agent given and if this correlates with a longer duration of intraoperative burst suppression. Methods: In this secondary analysis of the SuDoCo trail [ISRCTN 36437985] 1277 patients, older than 60 years undergoing general anesthesia were included. We preprocessed and analyzed the raw EEG files from each patient and evaluated the intraoperative burst suppression duration. In a logistic regression analysis, we assessed the impact of burst suppression duration and anesthetic agent used for maintenance on the risk to develop POD. Results: 18.7% of patients developed POD. Burst suppression duration was prolonged in POD patients (POD 27.5 min ± 21.3 min vs. NoPOD 21.4 ± 16.2 min, p < 0.001), for each minute of prolonged intraoperative burst suppression activity the risk to develop POD increased by 1.1% (OR 1.011, CI 95% 1.000-1.022, p = 0.046). Burst suppression duration was prolonged under propofol anesthesia as compared to sevoflurane and desflurane anesthesia (propofol 32.5 ± 20.3 min, sevoflurane 17.1 ± 12.6 min and desflurane 20.1 ± 16.0 min, p < 0.001). However, patients receiving desflurane anesthesia had a 1.8fold higher risk to develop POD, as compared to propofol anesthesia (OR 1.766, CI 95% 1.049-2.974, p = 0.032). Conclusion: We found a significantly increased risk to develop POD after desflurane anesthesia in older patients, even though burst suppression duration was shorter under desflurane anesthesia as compared to propofol anesthesia. Our finding might help to explain some discrepancies in studies analyzing the impact of burst suppression duration and EEG-guided anesthesia on the risk to develop POD.

Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures.

Objective: In older patients receiving general anesthesia, postoperative delirium (POD) is the most frequent form of cerebral dysfunction. Early identification of patients at higher risk to develop POD could provide the opportunity to adapt intraoperative and postoperative therapy. We, therefore, propose a machine learning approach to predict the risk of POD in elderly patients, using routine intraoperative electroencephalography (EEG) and clinical data that are readily available in the operating room. Methods: We conducted a retrospective analysis of the data of a single-center study at the Charité-Universitätsmedizin Berlin, Department of Anesthesiology [ISRCTN 36437985], including 1,277 patients, older than 60 years with planned surgery and general anesthesia. To deal with the class imbalance, we used balanced ensemble methods, specifically Bagging and Random Forests and as a performance measure, the area under the ROC curve (AUC-ROC). We trained our models including basic clinical parameters and intraoperative EEG features in particular classical spectral and burst suppression signatures as well as multi-band covariance matrices, which were classified, taking advantage of the geometry of a Riemannian manifold. The models were validated with 10 repeats of a 10-fold cross-validation. Results: Including EEG data in the classification resulted in a robust and reliable risk evaluation for POD. The clinical parameters alone achieved an AUC-ROC score of 0.75. Including EEG signatures improved the classification when the patients were grouped by anesthetic agents and evaluated separately for each group. The spectral features alone showed an AUC-ROC score of 0.66; the covariance features showed an AUC-ROC score of 0.68. The AUC-ROC scores of EEG features relative to patient data differed by anesthetic group. The best performance was reached, combining both the EEG features and the clinical parameters. Overall, the AUC-ROC score was 0.77, for patients receiving Propofol it was 0.78, for those receiving Sevoflurane it was 0.8 and for those receiving Desflurane 0.73. Applying the trained prediction model to an independent data set of a different clinical study confirmed these results for the combined classification, while the classifier on clinical parameters alone did not generalize. Conclusion: A machine learning approach combining intraoperative frontal EEG signatures with clinical parameters could be an easily applicable tool to early identify patients at risk to develop POD.

Frequency cluster formation and slow oscillations in neural populations with plasticity.

We report the phenomenon of frequency clustering in a network of Hodgkin-Huxley neurons with spike timing-dependent plasticity. The clustering leads to a splitting of a neural population into a few groups synchronized at different frequencies. In this regime, the amplitude of the mean field undergoes low-frequency modulations, which may contribute to the mechanism of the emergence of slow oscillations of neural activity observed in spectral power of local field potentials or electroencephalographic signals at high frequencies. In addition to numerical simulations of such multi-clusters, we investigate the mechanisms of the observed phenomena using the simplest case of two clusters. In particular, we propose a phenomenological model which describes the dynamics of two clusters taking into account the adaptation of coupling weights. We also determine the set of plasticity functions (update rules), which lead to multi-clustering.