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Background: Different anesthetic drugs and patient factors yield unique electroencephalogram (EEG) patterns. Yet, it is unclear how best to teach trainees to interpret EEG time series data and the corresponding spectral information for intraoperative anesthetic titration, or what effect this might have on outcomes., Methods: We developed an electronic learning curriculum (ELC) that covered EEG spectrogram interpretation and its use in anesthetic titration. Anesthesiology residents at a single academic center were randomized to receive this ELC and given spectrogram monitors for intraoperative use versus standard residency curriculum alone without intraoperative spectrogram monitors. We hypothesized that this intervention would result in lower inhaled anesthetic administration (measured by age-adjusted total minimal alveolar concentration [MAC] fraction and age-adjusted minimal alveolar concentration [aaMAC]) to patients ≥60 old during the postintervention period (the primary study outcome). To study this effect and to determine whether the 2 groups were administering similar anesthetic doses pre- versus postintervention, we compared aaMAC between control versus intervention group residents both before and after the intervention. To measure efficacy in the postintervention period, we included only those cases in the intervention group when the monitor was actually used. Multivariable linear mixed-effects modeling was performed for aaMAC fraction and hospital length of stay (LOS; a non-prespecified secondary outcome), with a random effect for individual resident. A multivariable linear mixed-effects model was also used in a sensitivity analysis to determine if there was a group (intervention versus control group) by time period (post- versus preintervention) interaction for aaMAC. Resident EEG knowledge difference (a prespecified secondary outcome) was compared with a 2-sided 2-group paired t test., Results: Postintervention, there was no significant aaMAC difference in patients cared for by the ELC group (n = 159 patients) versus control group (N = 325 patients; aaMAC difference = -0.03; 95% confidence interval [CI], -0.09 to 0.03; P =.32). In a multivariable mixed model, the interaction of time period (post- versus preintervention) and group (intervention versus control) led to a nonsignificant reduction of -0.05 aaMAC (95% CI, -0.11 to 0.01; P = .102). ELC group residents (N = 19) showed a greater increase in EEG knowledge test scores than control residents (N = 20) from before to after the ELC intervention (6-point increase; 95% CI, 3.50-8.88; P < .001). Patients cared for by the ELC group versus control group had a reduced hospital LOS (median, 2.48 vs 3.86 days, respectively; P = .024)., Conclusions: Although there was no effect on mean aaMAC, these results demonstrate that this EEG-ELC intervention increased resident knowledge and raise the possibility that it may reduce hospital LOS., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)

Background: Some older adults show exaggerated responses to drugs that act on the brain. The brain's response to anesthetic drugs is often measured clinically by processed electroencephalogram (EEG) indices. Thus, we developed a processed EEG-based measure of the brain's resistance to volatile anesthetics and hypothesized that low scores on it would be associated with postoperative delirium risk., Methods: We defined the Duke Anesthesia Resistance Scale (DARS) as the average bispectral index (BIS) divided by the quantity (2.5 minus the average age-adjusted end-tidal minimum alveolar concentration [aaMAC] inhaled anesthetic fraction). The relationship between DARS and postoperative delirium was analyzed in 139 older surgical patients (age ≥65) from Duke University Medical Center (n = 69) and Mt Sinai Medical Center (n = 70). Delirium was assessed by geriatrician interview at Duke, and by research staff utilizing the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) instrument at Mt Sinai. We examined the relationship between DARS and delirium and used the Youden index to identify an optimal low DARS threshold (for delirium risk), and its associated 95% bootstrap confidence bounds. We used multivariable logistic regression to examine the relationship between low DARS and delirium risk., Results: The relationship between DARS and delirium risk was nonlinear, with higher delirium risk at low DARS scores. A DARS threshold of 28.755 maximized the Youden index for the association between low DARS and delirium, with bootstrap 95% confidence bounds of 26.18 and 29.80. A low DARS (<28.755) was associated with increased delirium risk in multivariable models adjusting for site (odds ratio [OR] [95% confidence interval {CI}] = 4.30 [1.89-10.01]; P = .001), or site-plus-patient risk factors (OR [95% CI] = 3.79 [1.63-9.10]; P = .003). These associations with postoperative delirium risk remained significant when using the 95% bootstrap confidence bounds for the low DARS threshold (P < .05 for all). Further, a low DARS (<28.755) was associated with delirium risk after accounting for opioid, midazolam, propofol, phenylephrine, and ketamine dosage as well as site (OR [95% CI] = 4.21 [1.80-10.16]; P = .002). This association between low DARS and postoperative delirium risk after controlling for these other medications remained significant (P < .05) when using either the lower or the upper 95% bootstrap confidence bounds for the low DARS threshold., Conclusions: These results demonstrate that an intraoperative processed EEG-based measure of lower brain anesthetic resistance (ie, low DARS) is independently associated with increased postoperative delirium risk in older surgical patients., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)