A Processed Electroencephalogram-Based Brain Anesthetic Resistance Index Is Associated With Postoperative Delirium in Older Adults: A Dual Center Study

Impact of Age on the Occurrence of Processed Electroencephalographic Burst Suppression

BACKGROUND

Patient age is assumed to be an important risk factor for the occurrence of burst suppression, yet this has still to be confirmed by large datasets.

METHODS

In this single-center retrospective analysis at a university hospital, the electronic patient records of 38,628 patients (≥18 years) receiving general anesthesia between January 2016 and December 2018 were analyzed. Risk factors for burst suppression were evaluated using univariate and multivariable analysis. We measured the incidence of burst suppression as indicated by the burst suppression ratio (BSR) of the Entropy Module, the maximum and mean BSR values, relative burst suppression duration, mean volatile anesthetic concentrations, and mean age-adjusted minimum alveolar concentrations (aaMAC) at burst suppression, and cases of potentially misclassified burst suppression episodes. Analyses were done separately for the total anesthesia period, as well as for the Induction and Maintenance phase. The association with age was evaluated using linear and polynomial fits and by calculating correlation coefficients.

RESULTS

Of the 54,266 patients analyzed, 38,628 were included, and 19,079 patients exhibited episodes with BSR >0. Patients with BSR >0 were significantly older, and age had the highest predictive power for BSR >0 (area under the receiving operating characteristic [AUROC] = 0.646 [0.638-0.654]) compared to other patient or procedural factors. The probability of BSR >0 increased linearly with patient age (ρ = 0.96-0.99) between 1.9% and 9.8% per year. While maximal and mean BSR showed a nonlinear relationship with age, relative burst suppression duration also increased linearly during maintenance (ρ = 0.83). Further, episodes potentially indicating burst suppression that were not detected by the Entropy BSR algorithm also became more frequent with age. Volatile anesthetic concentrations sufficient to induce BSR >0 were negatively correlated with age (sevoflurane: ρ = -0.71), but remained close to an aaMAC of 1.0.

CONCLUSIONS

The probability of burst suppression during general anesthesia increases linearly with age in adult patients, while lower anesthetic concentrations induce burst suppression with increasing patient age. Simultaneously, algorithm-based burst suppression detection appears to perform worse in older patients. These findings highlight the necessity to further enhance EEG application and surveillance strategies in anesthesia.

Prediction of Post-Operative Delirium in Older Adults from Preoperative Cognition and Alpha Power from Resting-State EEG

Postoperative Delirium (POD) is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. The development of new tools to identify individuals at high risk for POD could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and POD-related complications. In this study, we used machine learning techniques to evaluate whether baseline (pre-operative) cognitive function and resting-state electroencephalography could be used to identify patients at risk for POD. Pre-operative resting-state EEGs and the Montreal Cognitive Assessment (MoCA) were collected from 85 patients (age = 73 ± 6.4 years) undergoing elective surgery, 12 of whom subsequently developed POD. The model with the highest f1-score for predicting delirium, a linear-discriminant analysis (LDA) model incorporating MoCA scores and occipital alpha-band EEG features, was subsequently validated in an independent, prospective cohort of 51 older adults (age ≥ 60) undergoing elective surgery, 6 of whom developed POD. The LDA-based model, with a total of 7 features, was able to predict POD with area under the receiver operating characteristic curve, specificity and accuracy all >90%, and sensitivity > 80%, in the validation cohort. Notably, models incorporating both resting-state EEG and MoCA scores outperformed those including either EEG or MoCA alone. While requiring prospective validation in larger cohorts, these results suggest that prediction of POD with high accuracy may be feasible in clinical settings using simple and widely available clinical tools.

Highlights

Predict postoperative delirium using pre-operative EEG alpha power and MoCA scores.Prediction performance improves over cognitive assessment alone.ROC-AUC, specificity, accuracy >90%, and sensitivity > 80%, in a validation cohort.Abnormalities in baseline EEG are a risk factor for postoperative delirium.

Electroencephalographic Measures of Delirium in the Perioperative Setting: A Systematic Review

Postoperative delirium (POD) is frequent in older adults and is associated with adverse cognitive and functional outcomes. In the last several decades, there has been an increased interest in exploring tools that easily allow the early recognition of patients at risk of developing POD. The electroencephalogram (EEG) is a widely available tool used to understand delirium pathophysiology, and its use in the perioperative setting has grown exponentially, particularly to predict and detect POD. We performed a systematic review to investigate the use of EEG in the pre-, intra-, and postoperative settings. We identified 371 studies, and 56 met the inclusion criteria. A range of techniques was used to obtain EEG data, from limited 1-4 channel setups to complex 256-channel systems. Power spectra were often measured preoperatively, yet the outcomes were inconsistent. During surgery, the emphasis was primarily on burst suppression (BS) metrics and power spectra, with a link between the frequency and timing of BS, and POD. The EEG patterns observed in POD aligned with those noted in delirium in different contexts, suggesting a reduction in EEG activity. Further research is required to investigate preoperative EEG indicators that may predict susceptibility to delirium.

Differential effects of sevoflurane and desflurane on frontal intraoperative electroencephalogram dynamics associated with postoperative delirium

STUDY OBJECTIVE

Intraoperative electroencephalogram (EEG) patterns associated with postoperative delirium (POD) development have been studied, but the differences in EEG recordings between sevoflurane- and desflurane-induced anesthesia have not been clarified. We aimed to distinguish the EEG characteristics of sevoflurane and desflurane in relation to POD development.

DESIGN AND PATIENTS

We collected frontal four-channel EEG data during the maintenance of anesthesia from 148 elderly patients who received sevoflurane (n = 77) or desflurane (n = 71); 30 patients were diagnosed with delirium postoperatively. The patients were divided into four subgroups based on anesthetics and delirium status: sevoflurane delirium (n = 17), sevoflurane non-delirium (n = 60), desflurane delirium (n = 13), and desflurane non-delirium (n = 58). We compared spectral power, coherence, and pairwise phase consistency (PPC) between sevoflurane and desflurane, and between non-delirium and delirium groups for each anesthetic.

MAIN RESULTS

In patients without POD, the sevoflurane non-delirium group exhibited higher EEG spectral power across 8.5-35 Hz (99.5% CI bootstrap analysis) and higher PPC from alpha to gamma bands (p < 0.005) compared to the desflurane non-delirium group. Conversely, in patients with POD, no significant EEG differences were observed between the sevoflurane and desflurane delirium groups. For the sevoflurane-induced patients, the sevoflurane delirium group had significantly lower power within 7.5-31.5 Hz (99.5% CI bootstrap analysis), reduced coherence over 8.9-23.8 Hz (99.5% CI bootstrap analysis), and lower PPC values in the alpha band (p < 0.005) compared with the sevoflurane non-delirium group. For the desflurane-induced patients, there were no significant differences in the EEG patterns between delirium and non-delirium groups.

CONCLUSIONS

In normal patients without POD, sevoflurane demonstrates a higher power spectrum and prefrontal connectivity than desflurane. Furthermore, reduced frontal alpha power, coherence, and connectivity of intraoperative EEG could be associated with an increased risk of POD. These intraoperative EEG characteristics associated with POD are more noticeable in sevoflurane-induced anesthesia than in desflurane-induced anesthesia.

Update of the European Society of Anaesthesiology and Intensive Care Medicine evidence-based and consensus-based guideline on postoperative delirium in adult patients

Postoperative delirium (POD) remains a common, dangerous and resource-consuming adverse event but is often preventable. The whole peri-operative team can play a key role in its management. This update to the 2017 ESAIC Guideline on the prevention of POD is evidence-based and consensus-based and considers the literature between 01 April 2015, and 28 February 2022. The search terms of the broad literature search were identical to those used in the first version of the guideline published in 2017. POD was defined in accordance with the DSM-5 criteria. POD had to be measured with a validated POD screening tool, at least once per day for at least 3 days starting in the recovery room or postanaesthesia care unit on the day of surgery or, at latest, on postoperative day 1. Recent literature confirmed the pathogenic role of surgery-induced inflammation, and this concept reinforces the positive role of multicomponent strategies aimed to reduce the surgical stress response. Although some putative precipitating risk factors are not modifiable (length of surgery, surgical site), others (such as depth of anaesthesia, appropriate analgesia and haemodynamic stability) are under the control of the anaesthesiologists. Multicomponent preoperative, intra-operative and postoperative preventive measures showed potential to reduce the incidence and duration of POD, confirming the pivotal role of a comprehensive and team-based approach to improve patients' clinical and functional status.

The clinical value of the Duke Anesthesia Resistance Scale in predicting postoperative delirium after hip fracture surgery: a retrospective study

Aim

This study aims to investigate the clinical value of the Duke Anesthesia Resistance Scale (DARS) in predicting postoperative delirium (POD) after hip fracture surgery.

Methods

A retrospective study was conducted. Clinical data were collected from the patients who had hip fracture and underwent elective total hip arthroplasty in Shaanxi Provincial People's Hospital, Third Affiliated Hospital of Xi'an Jiaotong University between January 2022 and June 2023. The Consciousness Fuzzy Assessment Scale was used to evaluate the occurrence of POD on postoperative day 3 (POD 3). The enrolled patients were divided into the POD group (n = 26) and the non-POD group (n = 125). Baseline characteristics, surgical data, postoperative information, and laboratory test results were collected. DARS scores were calculated using the minimum alveolar concentration, end-tidal concentration average (ETAC), and bispectral index (BIS). Multivariate logistic regression analysis was conducted to recognize the independent risk factors for POD after hip fracture surgery. Receiver operating characteristic (ROC) curve was plotted to evaluate the value of DARS in POD prediction.

Results

The average age of POD group was significantly higher, comparing to non-POD group (P < 0.05). DARS scores were statistically lower in the POD group compared to non-POD group (P < 0.05). Multivariate logistic regression analysis found that age and DARS scores were factors impacting post-operative delirium occurrence after hip fracture surgery (P < 0.05). ROC showed that the area under the curve for DARS in predicting POD after hip fracture surgery was 0.929 (95% CI [0.861-0.997]). The optimal cutoff value was 30. The sensitivity was 95.45%, while the specificity was 84.09%.

Conclusion

DARS score demonstrates good predictive value in hip fracture patients and is feasible in clinical practice, making it suitable for clinical application and promotion.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Incidence change of postoperative delirium after implementation of processed electroencephalography monitoring during surgery: a retrospective evaluation study

BACKGROUND

Postoperative delirium (POD) is a common complication in the elderly, which is associated with poor outcomes after surgery. Recognized as predisposing factors for POD, anesthetic exposure and burst suppression during general anesthesia can be minimized with intraoperative processed electroencephalography (pEEG) monitoring. In this study, we aimed to evaluate whether implementation of intraoperative pEEG-guided anesthesia is associated with incidence change of POD.

METHODS

In this retrospective evaluation study, we analyzed intravenous patient-controlled analgesia (IVPCA) dataset from 2013 to 2017. There were 7425 patients using IVPCA after a noncardiac procedure under general anesthesia. Patients incapable of operating the device independently, such as cognitive dysfunction or prolonged sedation, were declined and not involved in the dataset. After excluding patients who opted out within three days (N = 110) and those with missing data (N = 24), 7318 eligible participants were enrolled. Intraoperative pEEG has been implemented since July 2015. Participants having surgery after this time point had intraoperative pEEG applied before induction until full recovery. All related staff had been trained in the application of pEEG-guided anesthesia and the assessment of POD. Patients were screened twice daily for POD within 3 days after surgery by staff in the pain management team. In the first part of this study, we compared the incidence of POD and its trend from 2013 January-2015 July with 2015 July-2017 December. In the second part, we estimated odds ratios of risk factors for POD using multivariable logistic regression in case-control setting.

RESULTS

The incidence of POD decreased from 1.18 to 0.41% after the administration of intraoperative pEEG. For the age group ≧ 75 years, POD incidence decreased from 5.1 to 1.56%. Further analysis showed that patients with pEEG-guided anesthesia were associated with a lower odd of POD (aOR 0.33; 95% CI 0.18-0.60) than those without after adjusting for other covariates.

CONCLUSIONS

Implementation of intraoperative pEEG was associated with a lower incidence of POD within 3 days after surgery, particularly in the elderly. Intraoperative pEEG might be reasonably considered as part of the strategy to prevent POD in the elder population.

TRIAL REGISTRATION

Not applicable.

Delirium in older patients given propofol or sevoflurane anaesthesia for major cancer surgery: a multicentre randomised trial

BACKGROUND

Delirium is a common and disturbing postoperative complication that might be ameliorated by propofol-based anaesthesia. We therefore tested the primary hypothesis that there is less delirium after propofol-based than after sevoflurane-based anaesthesia within 7 days of major cancer surgery.

METHODS

This multicentre randomised trial was conducted in 14 tertiary care hospitals in China. Patients aged 65-90 yr undergoing major cancer surgery were randomised to either propofol-based anaesthesia or to sevoflurane-based anaesthesia. The primary endpoint was the incidence of delirium within 7 postoperative days.

RESULTS

A total of 1228 subjects were enrolled and randomised, with 1195 subjects included in the modified intention-to-treat analysis (mean age 71 yr; 422 [35%] women); one subject died before delirium assessment. Delirium occurred in 8.4% (50/597) of subjects given propofol-based anaesthesia vs 12.4% (74/597) of subjects given sevoflurane-based anaesthesia (relative risk 0.68 [95% confidence interval {CI}: 0.48-0.95]; P=0.023; adjusted relative risk 0.59 [95% CI: 0.39-0.90]; P=0.014). Delirium reduction mainly occurred on the first day after surgery, with a prevalence of 5.4% (32/597) with propofol anaesthesia vs 10.7% (64/597) with sevoflurane anaesthesia (relative risk 0.50 [95% CI: 0.33-0.75]; P=0.001). Secondary endpoints, including ICU admission, postoperative duration of hospitalisation, major complications within 30 days, cognitive function at 30 days and 3 yr, and safety outcomes, did not differ significantly between groups.

CONCLUSIONS

Delirium was a third less common after propofol than sevoflurane anaesthesia in older patients having major cancer surgery. Clinicians might therefore reasonably select propofol-based anaesthesia in patients at high risk of postoperative delirium.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR-IPR-15006209) and ClinicalTrials.gov (NCT02662257).

A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients

As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.

Microstate analyses as an indicator of anesthesia-induced unconsciousness

OBJECTIVE

The objective of this study was to identify differences in electroencephalographic microstate topographies across three perioperative phases: anesthetic pre-induction, surgical anesthesia, and post-anesthesia care unit (PACU) admission.

METHODS

Whole-scalp 16-channel electroencephalographic recordings were taken throughout the perioperative period on n = 22 adult, non-cardiac surgical patients.

RESULTS

Several differences between perioperative periods were identified. Most notably, during surgical anesthesia, patients demonstrated increased mean duration and, consequently, a reduction in the occurrence of microstates when compared to both preoperative baseline and PACU admission. We also observed the presence of microstate F with propofol anesthesia during surgery, which had been previously identified with propofol infusion in laboratory settings using human volunteers. Finally, we observed inverse age effects with mean occurrence and duration of microstates, particularly during PACU recovery.

CONCLUSIONS

Microstate duration is significantly increased during surgery compared to both pre-induction and PACU recovery. These data suggest that microstate topographies may be useful in monitoring anesthetic depth.

SIGNIFICANCE

This work highlights the potential for microstate analysis in the perioperative setting. We identified distinct topographical signatures across perioperative periods and with increasing age, which is predictive of post-operative delirium.

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.

The inter-relationship between delirium and dementia: the importance of delirium prevention

Delirium and dementia are two frequent causes of cognitive impairment among older adults and have a distinct, complex and interconnected relationship. Delirium is an acute confusional state characterized by inattention, cognitive dysfunction and an altered level of consciousness, whereas dementia is an insidious, chronic and progressive loss of a previously acquired cognitive ability. People with dementia have a higher risk of developing delirium than the general population, and the occurrence of delirium is an independent risk factor for subsequent development of dementia. Furthermore, delirium in individuals with dementia can accelerate the trajectory of the underlying cognitive decline. Delirium prevention strategies can reduce the incidence of delirium and associated adverse outcomes, including falls and functional decline. Therefore, delirium might represent a modifiable risk factor for dementia, and interventions that prevent or minimize delirium might also reduce or prevent long-term cognitive impairment. Additionally, understanding the pathophysiology of delirium and the connection between delirium and dementia might ultimately lead to additional treatments for both conditions. In this Review, we explore mechanisms that might be common to both delirium and dementia by reviewing evidence on shared biomarkers, and we discuss the importance of delirium recognition and prevention in people with dementia.

In this Review, Fong and Inouye explore mechanisms that might be common to both delirium and dementia. They present delirium as a possible modifiable risk factor for dementia and discuss the importance of delirium prevention strategies in reducing this risk.

Delirium and dementia are frequent causes of cognitive impairment among older adults and have a distinct, complex and interconnected relationship.

Delirium prevention strategies have been shown to reduce not only the incidence of delirium but also the incidence of adverse outcomes associated with delirium such as falls and functional decline.

Adverse outcomes associated with delirium, such as the onset of dementia symptoms in individuals with preclinical dementia, and/or the acceleration of cognitive decline in individuals with dementia might also be delayed by the implementation of delirium prevention strategies.

Evidence regarding the association of systemic inflammatory and neuroinflammatory biomarkers with delirium is variable, possibly as a result of co-occurring dementia pathology or disruption of the blood–brain barrier.

Alzheimer disease pathology, even prior to the onset of symptoms, might have an effect on delirium risk, with potential mechanisms including neuroinflammation and gene–protein interactions with the APOE ε4 allele.

Novel strategies, including proteomics, multi-omics, neuroimaging, transcranial magnetic stimulation and EEG, are beginning to reveal how changes in cerebral blood flow, spectral power and connectivity can be associated with delirium; further work is needed to expand these findings to patients with delirium superimposed upon dementia.

Developing a Real-Time Electroencephalogram-Guided Anesthesia-Management Curriculum for Educating Residents: A Single-Center Randomized Controlled Trial

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.