Meta-analysis and Megadata in Electroencephalogram-Based Techniques for Delirium Prevention

Reducing Perioperative Neurocognitive Disorders (PND) Through Depth of Anesthesia Monitoring: A Critical Review

General anesthesia has been administered for over 150 years, and in that time, has become progressively safer. Improvements in outcomes have been driven by multiple advances, including the use of non-invasive monitors to assess cardiovascular and respiratory status. More recent advances have included the development and use of monitors to measure neurologic status by means of “processed” electroencephalography (pEEG), wherein the frontal EEG signal is analyzed by proprietary algorithms to produce a dimensionless number (scaled from 0 to 100), wherein low values are associated with deepening levels of sedation that progresses to loss of consciousness. Such monitors have been shown to enable anesthetic titration so as to expedite emergence and early recovery, and their use is advocated for the prevention of intraoperative awareness in the setting of administration of total intravenous anesthesia and neuromuscular blockade. Whether their use can minimize, or prevent, longer term adverse events is a matter of debate. In this narrative review of the most recent literature, we provide an assessment on the use of pEEG monitors in the prevention of a notable, and important, postoperative adverse outcome – delirium – in elderly patients. As we will discuss, the existing data do not support its routine use for the prevention of postoperative delirium in this, or any other, patient population.

Age-Related EEG Features of Bursting Activity During Anesthetic-Induced Burst Suppression

Electroencephalographic (EEG) Burst Suppression (BSUPP) is a discontinuous pattern characterized by episodes of low voltage disrupted by bursts of cortical synaptic activity. It can occur while delivering high-dose anesthesia. Current research suggests an association between BSUPP and the occurrence of postoperative delirium in the post-anesthesia care unit (PACU) and beyond. We investigated burst micro-architecture to further understand how age influences the neurophysiology of this pharmacologically-induced state. We analyzed a subset of EEG recordings (n = 102) taken from a larger data set previously published. We selected the initial burst that followed a visually identified “silent second,” i.e., at least 1 s of iso-electricity of the EEG during propofol induction. We derived the (normalized) power spectral density [(n)PSD], the alpha band power, the maximum amplitude, the maximum slope of the EEG as well as the permutation entropy (PeEn) for the first 1.5 s of the initial burst of each patient. In the old patients >65 years, we observed significantly lower (p < 0.001) EEG power in the 1–15 Hz range. In general, their EEG contained a significantly higher amount of faster oscillations (>15 Hz). Alpha band power (p < 0.001), EEG amplitude (p = 0.001), and maximum EEG slope (p = 0.045) all significantly decreased with age, whereas PeEn increased (p = 0.008). Hence, we can describe an age-related change in features during EEG burst suppression. Sub-group analysis revealed no change in results based on pre-medication. These EEG changes add knowledge to the impact of age on cortical synaptic activity. In addition to a reduction in EEG amplitude, age-associated burst features can complicate the identification of excessive anesthetic administration in patients under general anesthesia. Knowledge of these neurophysiologic changes may not only improve anesthesia care through improved detection of burst suppression but might also provide insight into changes in neuronal network organization in patients at risk for age-related neurocognitive problems.