Contribution of intraoperative electroencephalogram suppression to frailty-associated postoperative delirium: mediation analysis of a prospective surgical cohort

Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram

BACKGROUND

Burst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH.

MATERIALS AND METHODS

We retrospectively collected 540 patients and analyzed the data from 418 patients. Univariate analysis and backward stepwise logistic regression were used to select risk factors to develop a dynamic nomogram model, and then we developed a web calculator to visualize the process of prediction. The performance of the nomogram was evaluated in terms of discrimination, calibration, and clinical utility.

RESULTS

According to the receiver operating characteristic (ROC) analysis, the nomogram showed good discriminative ability (AUC = 0.933) and the Hosmer-Lemeshow goodness-of-fit test demonstrated the nomogram had good calibration (p = 0.0718). Age, Clinical Frailty Scale (CFS) score, midazolam dose, propofol induction dose, total area under the hypotensive threshold of mean arterial pressure (MAP_AUT), and cerebrovascular diseases were the independent risk predictors of BS and used to construct nomogram. The web-based dynamic nomogram calculator was accessible by clicking on the URL: https://eegbsnomogram.shinyapps.io/dynnomapp/ or scanning a converted Quick Response (QR) code.

CONCLUSIONS

Incorporating two distinctive new risk factors, frailty and IOH, we firstly developed a visualized nomogram for accurately predicting BS in non-cardiac surgery patients. The model is expected to guide clinical decision-making and optimize anesthesia management.

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.

Intraoperative electroencephalogram features related to frailty in older patients: an exploratory prospective observational study

Frailty is an independent risk factor for the increased incidence of postoperative delirium (POD). To date, the effect of frailty on intraoperative electroencephalogram (EEG) changes remains unexplored. The present study, an exploratory analysis of a prospective cohort study, aimed to investigate the differences in EEG characteristics between frail and robust patients. This prospective observational study was conducted between December 2020 and November 2021. The preoperative frailty status was assessed using the FRAIL scale. The patients' baseline (before anesthesia) and intraoperative EEG data were collected using a brain function monitor. Finally, 20 robust and 26 frail older patients scheduled for elective spinal surgery or transurethral prostatectomy under propofol-based general anesthesia were included in the final analysis. Baseline and intraoperative EEG spectrogram and power spectra were compared between the frail and robust groups. No differences were observed in baseline EEG between the frail and robust groups. When the intraoperative EEG spectral parameters were compared, the alpha peak frequency (10.56 ± 0.49 vs. 10.14 ± 0.36 Hz, P = 0.002) and alpha peak, delta, theta, alpha, and beta powers were lower in the frail group. After adjusting for age, Charlson Comorbidity Index (CCI), and mini-mental state examination (MMSE) score, the FRAIL score was still negatively associated with total, delta, theta, alpha, and beta powers. Frail patients had reduced EEG (0-30 Hz) power after the induction of propofol-based general anesthesia. After adjusting for age, CCI, and MMSE score, frail patients still showed evidence of reduced δ, θ, α, and β power.

The Association of Preoperative Diabetes With Postoperative Delirium in Older Patients Undergoing Major Orthopedic Surgery: A Prospective Matched Cohort Study

BACKGROUND

Postoperative delirium (POD) is a common form of postoperative brain dysfunction, especially in the elderly. However, its risk factors remain largely to be determined. This study aimed to investigate whether (1) preoperative diabetes is associated with POD after elective orthopedic surgery and (2) intraoperative frontal alpha power is a mediator of the association between preoperative diabetes and POD.

METHODS

This was a prospective matched cohort study of patients aged 60 years or more, with a preoperative diabetes who underwent elective orthopedic surgery. Nondiabetic patients were matched 1:1 to diabetic patients in terms of age, sex, and type of surgery. Primary outcome was occurrence of POD, assessed using the 3-minute Diagnostic Confusion Assessment Method (3D-CAM) once daily from 6 pm to 8 pm during the postoperative days 1-7 or until discharge. Secondary outcome was the severity of POD which was assessed for all participants using the short form of the CAM-Severity. Frontal electroencephalogram (EEG) was recorded starting before induction of anesthesia and lasting until discharge from the operating room. Intraoperative alpha power was calculated using multitaper spectral analyses. Mediation analysis was used to estimate the proportion of the association between preoperative diabetes and POD that could be explained by intraoperative alpha power.

RESULTS

A total of 138 pairs of eligible patients successfully matched 1:1. After enrollment, 6 patients in the diabetes group and 4 patients in the nondiabetes group were excluded due to unavailability of raw EEG data. The final analysis included 132 participants with preoperative diabetes and 134 participants without preoperative diabetes, with a median age of 68 years and 72.6% of patients were female. The incidence of POD was 16.7% (22/132) in patients with preoperative diabetes vs 6.0% (8/134) in patients without preoperative diabetes. Preoperative diabetes was associated with increased odds of POD after adjustment of age, sex, body mass index, education level, hypertension, arrhythmia, coronary heart disease, and history of stroke (odds ratio, 3.2; 95% confidence interval [CI], 1.4-8.0; P = .009). The intraoperative alpha power accounted for an estimated 20% (95% CI, 2.6-60%; P = .021) of the association between diabetes and POD.

CONCLUSIONS

This study suggests that preoperative diabetes is associated with an increased risk of POD in older patients undergoing major orthopedic surgery, and that low intraoperative alpha power partially mediates such association.