Intra-operative electroencephalogram frontal alpha-band spectral analysis and postoperative delirium in cardiac surgery: A prospective cohort study

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.

Association between intraoperative electroencephalograph complexity index and postoperative delirium in elderly patients undergoing orthopedic surgery: a prospective cohort study

PURPOSE

The primary method for predicting POD (postoperative confusion) relies on the analysis of clinical features. Brain activity complexity is a promising factor associated with the state of consciousness. The aim of this study was to investigate the role of EEG (electroencephalography) complexity changes in predicting POD in elderly patients undergoing orthopedic surgery.

METHODS

From January 2024 to August 2024, 289 elderly patients undergoing orthopedic surgery were recruited at the Second Affiliated Hospital of Nanjing Medical University. Intraoperative EEG data from patients were collected and then EEG nonlinear features were extracted by MATLAB custom scripts. The logistic regression and CNN (convolutional neural networks) were used to explore the predictive effect of nonlinear features on POD from both static and dynamic perspectives.

RESULTS

Low permutation Lempel-Ziv complexity (PLZC) among the EEG nonlinear features emerged as an independent risk factor for POD [OR = 0.210; 95% CI (0.050-0.850); p = 0.029]. Receiver operating characteristic curve (ROC) analysis revealed a poor area under the curve of 0.615 (95% CI 0.517-0.711) for PLZC in predicting POD. After the inclusion of temporal factors, the ROC analysis indicated that the EEG nonlinear indices had a moderate predictive effect on POD [AUC = 0.701; (95% CI 0.541-0.862)].

CONCLUSIONS

EEG nonlinear feature indices may be effective biomarkers for POD and could help predict POD in elderly patients undergoing orthopedic surgery.

Anesthesia-induced electroencephalogram oscillations and perioperative outcomes in older adults undergoing cardiac surgery

BACKGROUND

Electroencephalogram oscillations during general anesthesia may change as a function of cognitive and physical health. This study aimed to characterize associations between anesthesia-induced oscillations and postoperative outcomes in cardiac surgery patients over 60 years.

METHODS

This was a prespecified secondary data analysis from the Minimizing Intensive Care Unit Dysfunction with Dexmedetomidine-induced Sleep (MINDDS) study. Participants were admitted from home for elective cardiac surgery with cardiopulmonary bypass. The primary outcome was postoperative delirium obtained using the Confusion Assessment Method. Secondary outcomes were non-home discharge and 30-day readmission. The exposure of interest was alpha power measured during the maintenance phase of isoflurane-general anesthesia. Confounding cognitive and physical health variables were collected.

RESULTS

Of 394 participants in the MINDDS study, 302 had analyzable electroencephalograms. The incidence of postoperative delirium was 11.1 %. Odds of postoperative delirium decreased by 14 % for every decibel increase in alpha power (OR 0.86, 95 % CI: 0.78 to 0.95; P = 0.004). This finding was not significant in adjusted analysis (ORadj 0.92, 95 % CI: 0.81 to 1.03; P = 0.154). Non-home discharge setting findings were not associated with alpha power. The odds of 30-day readmission decreased by 20 % for every decibel increase in alpha power (ORadj 0.80, 95 % CI: 0.71 to 0.91; P < 0.001). Findings were conserved in exploratory and sensitivity analyses.

CONCLUSIONS

In this study anesthesia-induced oscillations were associated with postoperative outcomes; however, these were not independently associated with delirium or discharge disposition after considering preoperative cognitive and physical health. These oscillations were robustly associated with 30-day readmission however, which may help anesthesiologists identify high-risk patients, offering benefits beyond the operating room.

CLINICAL TRIAL REGISTRATION

Registration Number: NCT02856594.

Quantitative electroencephalography predicts postoperative delirium in adult cardiac surgical patients from a prospective observational study

The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) in the the onset of postoperative delirium (POD) remains an area of inquiry. We aim to determine whether qEEG could assist in the diagnosis of early POD in cardiac surgery patients. We prospectively studied a cohort of cardiac surgery patients undergoing qEEG for evaluation of altered mental status. Delirium was assessed with the Confusion Assessment Method for the intensive care unit (CAM-ICU). The qEEG were interpreted by clinician, and reports were reviewed to identify features such as amplitude-integrated EEG (aEEG), relative band energy in ɑ/β/θ/δ frequencies, α variability and spectral entropy. The raw EEG was also preprocessed offline for nonlinear analysis including Multi-scale Entropy analysis (MSE) and Detrended Fluctuation Analysis (DFA). Linear regression was performed to quantify associations among EEG findings, delirium, and clinical outcomes. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the qEEG as POD prediction index. Meanwhile, a comprehensive comparison of dynamic complexity across time scales and DFA exponent α was conducted between the non-delirium and delirium groups. Among those recruited initially (n = 64), 60 patients were evaluated and 29 patients (48.3%) met delirium criteria. When comparing delirious and non-delirious participants, significant differences were found in terms of age (p = 0.03), APACHE II scores (p = 0.004), lactate (p = 0.03), and hospital days (p = 0.048). Multivariate regression analysis revealed that the first quartile (Q1) and fourth quartile (Q4) of peak or valley value of F3-P3/F4-P4 derivation (for example, Q1 of peak value for F3-P3 derivation: OR 12.4, 95% CI 1.72-89.76, p = 0.012) showed a higher association with the incidence of POD. ROC analysis demonstrated qEEG could predict POD with high sensitivity and specificity, yielding an overall good accuracy. For instance, the peak value of F3-P3 derivation (the area under the curve of 0.81), as a predictor of POD showed a sensitivity of 90% and specificity pf 72% (p < 0.001). Furthermore, the MSE curves indicated that the non-delirium group exhibited higher complexity values at fine scales, while the delirium group had significantly higher complexity at coarse scales. The DFA comparison results revealed that long-term fractal exponent alpha2 values were higher in delirium patients than in non-delirium patients, with significant differences observed at the F4-P4 electrodes (p = 0.04). The qEEG can reliably predict delirium after heart cardiac surgery. It is helpful for clinicians to early diagnose and manage these patients.Trial registration: Clinical Trials.gov Identifier, NCT03351985. Registered 1 December 2017.

Brain health: A concern for anaesthesiologists and intensivists

Damage to the brain can have disastrous and long-lasting consequences. The European Society of Anaesthesiology and Intensive Care (ESAIC) is aware of the importance of taking good care of the brain, both of patients and of anaesthesia and intensive care unit (ICU) caregivers, and has organised a complete learning track on brain health to bring this concern to the attention of practitioners. This learning track included an online Focus Meeting on Brain Health (November 25, 2023). We here provide readers with a digest of the information that was delivered during that meeting in an opinion paper driven by the authors' own reading of the literature. It is divided according to the meeting's sessions, including how to improve the health of an injured brain, how to keep a young or old brain healthy, how to keep a healthy adult brain unimpaired, how monitoring can impact brain health in the operating room and in the intensive care unit, and how to keep the anaesthesia and ICU caregivers' brain healthy. Each part is a brief and focused summary. The main delivered messages are that the management of injured brain patients involves an adequate choice of sedation, adequate brain monitoring, and focused attention to specific points depending on the underlying pathology; that several measures can be undertaken to protect the brain of the very young needing anaesthesia; that it is possible to detect older patients at risk of postoperative neurocognitive disorders, and that dedicated perioperative management by a multidisciplinary expert team may improve their outcomes; that apparently healthy adult brains may suffer during anaesthesia; that the electroencephalogram may track peri-operative brain dysfunction, and that female patients should be given special care in this respect; that multimodal brain monitoring helps to detect pathological processes and to maintain brain homeostasis; and that burnout in anaesthesiologists can be effectively fought using personal, organisational, managerial and legal approaches.

Clinical investigation into risk factors for delirium post-cardiac surgery and its implications for nursing intervention guided by behavior change theory

BACKGROUND

This study explores the factors contributing to the occurrence of delirium following cardiac surgery and devises nursing strategies rooted in behavior change theory.

METHODS

A cohort of 320 cardiac surgery patients was selected, categorized into two groups: 93 cases where postoperative delirium (POD) was anticipated, and 227 cases where it was not. Preoperative, intraoperative, and postoperative factors of POD were scrutinized using single-factor analysis, while binary logistic regression analysis was employed to pinpoint risk factors.

RESULTS

Among the 320 patients, 93 displayed POD symptoms post-surgery, yielding an incidence of 29.06%. Preoperative univariate analysis disclosed significant differences in gender, age, smoking, hypertension, and diabetes (P < 0.05). Intraoperatively, significant differences were noted in the American Society of Anesthesiologists (ASA) anesthesia grade (II, III, and IV), surgery time, cardiopulmonary bypass duration, and aortic occlusion duration (P < 0.05). Post-surgery, significant differences were observed in the duration of Intensive Care Unit (ICU) stay, mechanical ventilation time, and visual analogue scale (VAS) scores (P < 0.05). Multivariate Logistic regression identified surgery time (OR = 2.334, P < 0.001), ICU admission duration (OR = 1.457, P < 0.001), mechanical ventilation time (OR = 1.235, P = 0.004), and VAS scores (OR = 2.986, P < 0.001) as independent risk factors for POD. ROC curve analysis indicated higher sensitivity and specificity in predicting POD with surgery time, ICU stay duration, mechanical ventilation time, and VAS scores.

CONCLUSION

Irrespective of the surgical intervention type, surgery time, ICU stay duration, mechanical ventilation time, and VAS scores are recognized as risk factors for POD in cardiac surgery patients. Hence, continuous patient monitoring and early intervention tailored to specific risk factors are essential in clinical practice to mitigate POD incidence.

A Novel Preoperative Electroencephalogram-Derived Index to Predict Early Postoperative Delirium in Elderly Patients After Hip Fracture Surgeries: Development of a Prediction Model

Introduction

It is appealing to accurately predict postoperative delirium (POD) before surgeries. In this study, it was hypothesized that a novel electroencephalogram-derived index, the delirium index (DELi), could extract latent information regarding the predisposing factors of POD preoperatively. This study was aimed at developing a concise model that incorporated this DELi score to predict the early POD of elderly patients after hip surgeries.

Materials and Methods

Elderly patients scheduled for elective hip fracture surgeries were prospectively enrolled in a tertiary care hospital from November 2020 to June 2022. DELi scores and patient characteristics (age, sex, types of fracture and surgery, the time interval between fracture and surgery, cognitive function assessed using the Montreal Cognitive Assessment (MoCA), and frailty status assessed using the FRAIL scale) were collected preoperatively as candidate predictors. POD diagnosed using the confusion assessment method (CAM) was the outcome. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors. Then, these predictors were entered into a backward logistical regression analysis to develop a prediction model. Discrimination, calibration and clinical utility were validated using the bootstrapping method.

Results

All data (144 qualified patients of 170) were used for development. POD was observed in 71 patients (49.3%). Preoperative DELi scores predicted early POD (the area under the curve (AUC) = .786, 95% confidence interval (CI): .712, .860, in internal validation). A nomogram with MoCA, FRAIL scale and DELi score was constructed with excellent discrimination (AUC = .920, 95% CI: .876, .963, in internal validation), accredited calibration (P = .733, Hosmer‒Lemeshow test), and a wide range of threshold probabilities (5% to 95%).

Conclusions

Preoperative DELi scores predicted the early POD of elderly patients after hip surgeries. A concise prediction model was developed and demonstrated excellent discrimination.

Intraoperative electroencephalogram patterns as predictors of postoperative delirium in older patients: a systematic review and meta-analysis

Background

Postoperative delirium (POD) significantly affects patient outcomes after surgery, leading to increased morbidity, extended hospital stays, and potential long-term cognitive decline. This study assessed the predictive value of intraoperative electroencephalography (EEG) patterns for POD in adults.

Methods

This systematic review and meta-analysis followed the PRISMA and Cochrane Handbook guidelines. A thorough literature search was conducted using PubMed, Medline, and CENTRAL databases focusing on intraoperative native EEG signal analysis in adult patients. The primary outcome was the relationship between the burst suppression EEG pattern and POD development.

Results

From the initial 435 articles identified, 19 studies with a total of 7,229 patients were included in the systematic review, with 10 included in the meta-analysis (3,705 patients). In patients exhibiting burst suppression, the POD incidence was 22.1% vs. 13.4% in those without this EEG pattern (p=0.015). Furthermore, an extended burst suppression duration associated with a higher likelihood of POD occurrence (p = 0.016). Interestingly, the burst suppression ratio showed no significant association with POD.

Conclusions

This study revealed a 41% increase in the relative risk of developing POD in cases where a burst suppression pattern was present. These results underscore the clinical relevance of intraoperative EEG monitoring in predicting POD in older patients, suggesting its potential role in preventive strategies.

Systematic Review Registration

This study was registered on International Platform for Registered Protocols for Systematic Reviews and Meta-Analyses: INPLASY202420001, https://doi.org/10.37766/inplasy2024.2.0001.

Serum Neurofilament Light and Postoperative Delirium in Cardiac Surgery: A Preplanned Secondary Analysis of a Prospective Observational Study

BACKGROUND

Impaired cognition is a major predisposing factor for postoperative delirium, but it is not systematically assessed. Anesthesia and surgery may cause postoperative delirium by affecting brain integrity. Neurofilament light in serum reflects axonal injury. Studies evaluating the perioperative course of neurofilament light in cardiac surgery have shown conflicting results. The authors hypothesized that postoperative serum neurofilament light values would be higher in delirious patients, and that baseline concentrations would be correlated with patients' cognitive status and would identify patients at risk of postoperative delirium.

METHODS

This preplanned secondary analysis included 220 patients undergoing elective cardiac surgery with cardiopulmonary bypass. A preoperative cognitive z score was calculated after a neuropsychological evaluation. Quantification of serum neurofilament light was performed by the Simoa (Quanterix, USA) technique before anesthesia, 2 h after surgery, on postoperative days 1, 2, and 5. Postoperative delirium was assessed using the Confusion Assessment Method for Intensive Care Unit, the Confusion Assessment Method, and a chart review.

RESULTS

A total of 65 of 220 (29.5%) patients developed postoperative delirium. Delirious patients were older (median [25th percentile, 75th percentile], 74 [64, 79] vs. 67 [59, 74] yr; P < 0.001) and had lower cognitive z scores (-0.52 ± 1.14 vs. 0.21 ± 0.84; P < 0.001). Postoperative neurofilament light concentrations increased in all patients up to day 5, but did not predict delirium when preoperative concentrations were considered. Baseline neurofilament light values were significantly higher in patients who experienced delirium. They were influenced by age, cognitive z score, renal function, and history of diabetes mellitus. Baselines values were significantly correlated with cognitive z scores (r, 0.49; P < 0.001) and were independently associated with delirium whenever the patient's cognitive status was not considered (hazard ratio, 3.34 [95% CI, 1.07 to 10.4]).

CONCLUSIONS

Cardiac surgery is associated with axonal injury, because neurofilament light concentrations increased postoperatively in all patients. However, only baseline neurofilament light values predicted postoperative delirium. Baseline concentrations were correlated with poorer cognitive scores, and they independently predicted postoperative delirium whenever patient's cognitive status was undetermined.

EDITOR’S PERSPECTIVE

Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine

A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.