Abstract
Physiologic signals such as the electroencephalogram (EEG) demonstrate irregular behaviors due to the interaction of multiple control processes operating over different time scales. The complexity of this behavior can be quantified using multi-scale entropy (MSE). High physiologic complexity denotes health, and a loss of complexity can predict adverse outcomes. Since postoperative delirium is particularly hard to predict, we investigated whether the complexity of preoperative and intraoperative frontal EEG signals could predict postoperative delirium and its endophenotype, inattention. To calculate MSE, the sample entropy of EEG recordings was computed at different time scales, then plotted against scale; complexity is the total area under the curve. MSE of frontal EEG recordings was computed in 50 patients ≥ age 60 before and during surgery. Average MSE was higher intra-operatively than pre-operatively (p = 0.0003). However, intraoperative EEG MSE was lower than preoperative MSE at smaller scales, but higher at larger scales (interaction p < 0.001), creating a crossover point where, by definition, preoperative, and intraoperative MSE curves met. Overall, EEG complexity was not associated with delirium or attention. In 42/50 patients with single crossover points, the scale at which the intraoperative and preoperative entropy curves crossed showed an inverse relationship with delirium-severity score change (Spearman ρ = −0.31, p = 0.054). Thus, average EEG complexity increases intra-operatively in older adults, but is scale dependent. The scale at which preoperative and intraoperative complexity is equal (i.e., the crossover point) may predict delirium. Future studies should assess whether the crossover point represents changes in neural control mechanisms that predispose patients to postoperative delirium.
Highlights
As the age of surgical patients has increased, efforts have focused on optimizing older adults’ postoperative brain health outcomes (Mahanna-Gabrielli et al, 2019) and avoiding perioperative neurocognitive disorders (PND; Evered et al, 2018) such as postoperative delirium
The complexity of these physiological signals can be quantified using techniques adapted from information theory, such as multi-scale entropy (MSE; Costa et al, 2002)
We focused on frontal EEG given the contribution of the frontal lobe to attention (Zanto and Gazzaley, 2019), the frontal location of common intraoperative EEG monitors (Chan et al, 2020), prior associations between frontal-EEG parameters and cognitive status (Giattino et al, 2017), and known anesthetic-induced agerelated frontal-EEG changes (Purdon et al, 2013)
Summary
As the age of surgical patients has increased, efforts have focused on optimizing older adults’ postoperative brain health outcomes (Mahanna-Gabrielli et al, 2019) and avoiding perioperative neurocognitive disorders (PND; Evered et al, 2018) such as postoperative delirium. Healthy organ systems are governed by networks of physiologic control mechanisms, which interact across multiple temporal and spatial scales to produce highly complex signals The complexity of these physiological signals can be quantified using techniques adapted from information theory, such as multi-scale entropy (MSE; Costa et al, 2002). Increased signal complexity, quantified by the area under the MSE as a function of scale curve, has been associated with greater resilience –, i.e., greater capacity to adapt to stressors (Gijzel et al, 2019) This relationship between signal complexity and recovery from stressors is thought to reflect an organ system’s capacity to recover from perturbations, which depends on numerous interacting physiologic responses operating over varying time scales, with fluctuating patterns of recurrence. This age-related decline in complexity is associated with an impaired ability to recover from health stressors (Lipsitz, 2004; Zhou et al, 2017; Gijzel et al, 2019)
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