Dataset Information

Preoperative electroencephalographic alpha-power changes with eyes opening are associated with postoperative attention impairment and inattention-related delirium severity.

ABSTRACT:

Background

In the eyes-closed, awake condition, EEG oscillatory power in the alpha band (7-13 Hz) dominates human spectral activity. With eyes open, however, EEG alpha power substantially decreases. Less alpha attenuation with eyes opening has been associated with inattention; thus, we analysed whether reduced preoperative alpha attenuation with eyes opening is associated with postoperative inattention, a delirium-defining feature.

Methods

Preoperative awake 32-channel EEG was recorded with eyes open and eyes closed in 71 non-neurological, noncardiac surgery patients aged ≥ 60 years. Inattention and other delirium features were assessed before surgery and twice daily after surgery until discharge. Eyes-opening EEG alpha-attenuation magnitude was analysed for associations with postoperative inattention, primarily, and with delirium severity, secondarily, using multivariate age- and Mini-Mental Status Examination (MMSE)-adjusted logistic and proportional-odds regression analyses.

Results

Preoperative alpha attenuation with eyes opening was inversely associated with postoperative inattention (odds ratio [OR] 0.73, 95% confidence interval [CI]: 0.57, 0.94; P=0.038). Sensitivity analyses showed an inverse relationship between alpha-attenuation magnitude and inattention chronicity, defined as 'never', 'newly', or 'chronically' inattentive (OR 0.76, 95% CI: 0.62, 0.93; P=0.019). In addition, preoperative alpha-attenuation magnitude was inversely associated with postoperative delirium severity (OR 0.79, 95% CI: 0.65, 0.95; P=0.040), predominantly as a result of the inattention feature.

Conclusions

Preoperative awake, resting, EEG alpha attenuation with eyes opening might represent a neural biomarker for risk of postoperative attentional impairment. Further, eyes-opening alpha attenuation could provide insight into the neural mechanisms underlying postoperative inattention risk.

SUBMITTER: Acker L

PROVIDER: S-EPMC10797508 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Preoperative electroencephalographic alpha-power changes with eyes opening are associated with postoperative attention impairment and inattention-related delirium severity.

Acker Leah L Wong Megan K MK Wright Mary C MC Reese Melody M Giattino Charles M CM Roberts Kenneth C KC Au Sandra S Colon-Emeric Cathleen C Lipsitz Lewis A LA Devinney Michael J MJ Browndyke Jeffrey J Eleswarpu Sarada S Moretti Eugene E Whitson Heather E HE Berger Miles M Woldorff Marty G MG

British journal of anaesthesia 20231212 1

<h4>Background</h4>In the eyes-closed, awake condition, EEG oscillatory power in the alpha band (7-13 Hz) dominates human spectral activity. With eyes open, however, EEG alpha power substantially decreases. Less alpha attenuation with eyes opening has been associated with inattention; thus, we analysed whether reduced preoperative alpha attenuation with eyes opening is associated with postoperative inattention, a delirium-defining feature.<h4>Methods</h4>Preoperative awake 32-channel EEG was rec ...[more]

PMID: 38087743

Similar Datasets

Project description:Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with development of postoperative delirium in older surgical patients. We employed a nested case–control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p<0.05). Due to the limited sample size these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identifies 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

Project description:ObjectivesDelirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a tool that can help close this diagnostic gap: the Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).DesignRetrospective cohort study.SettingSingle-center tertiary academic medical center.PatientsThree-hundred seventy-three adult patients undergoing electroencephalography to evaluate altered mental status between August 2015 and December 2019.InterventionsNone.Measurements and main resultsWe developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals. Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S). We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S (R = 0.67; p < 0.0001). For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay (correlation = 0.31 vs 0.41, respectively; p = 0.082) and inhospital mortality (area under the curve = 0.77 vs 0.81; p = 0.310). Even when restricted to noncomatose patients, E-CAM-S remained statistically similar to CAM-S in its association with length of stay (correlation = 0.37 vs 0.42, respectively; p = 0.188) and inhospital mortality (area under the curve = 0.83 vs 0.74; p = 0.112). In addition to previously appreciated spectral features, the machine learning framework identified variability in multiple measures over time as important features in electroencephalography-based prediction of delirium severity.ConclusionsThe E-CAM-S is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

Project description:BackgroundPostoperative delirium 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. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.MethodsIn this prospective observational study, we used machine learning to evaluate whether baseline (preoperative) cognitive function and resting-state EEG could be used to identify patients at risk for postoperative delirium. Preoperative resting-state EEGs and the Montreal Cognitive Assessment were collected from 85 patients (age = 73 ± 6.4 years, 12 cases of delirium) undergoing elective surgery. The model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (age = 68 ± 5.2 years, 6 cases of delirium) undergoing elective surgery.ResultsOccipital alpha powers have higher f1-score than frontal alpha powers and EEG spectral slowing in the training cohort. Occipital alpha powers were able to predict postoperative delirium with AUC, specificity and accuracy all >90%, and sensitivity >80%, in the validation cohort. Notably, models incorporating transformed alpha powers and cognitive scores outperformed models incorporating occipital alpha powers alone or cognitive scores alone.ConclusionsWhile requiring prospective validation in larger cohorts, these results suggest that strong prediction of postoperative delirium may be feasible in clinical settings using simple and widely available clinical tools. Additionally, our results suggested that the thalamocortical circuit exhibits different EEG patterns under different stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.

Project description:BackgroundMedications are frequently reported as both predisposing factors and inducers of delirium. This review evaluated the available evidence and determined the magnitude of risk of postoperative delirium associated with preoperative medication use.MethodsA systematic search in Medline and EMBASE was conducted using MeSH terms and keywords for postoperative delirium and medication. Studies which included patients 18 years and older who underwent major surgery were included. The methodological quality of included studies was assessed independently by two authors using the Newcastle-Ottawa quality assessment scale for cohort studies.ResultsTwenty-nine studies; 25 prospective cohort, three retrospective cohort and one post hoc analysis of RCT data were included. Only four specifically aimed to assess medicines as an independent predictor of delirium, all other studies included medicines among a number of potential predictors of delirium. Of the studies specifically testing the association with a medication class, preoperative use of beta-blockers (OR = 2.06[1.18-3.60]) in vascular surgery and benzodiazepines RR 2.10 (1.23-3.59) prior to orthopedic surgery were significant. However, evidence is from single studies only. Where medicines were included as one possible factor among many, hypnotics had a similar risk estimate to the benzodiazepine study, with one significant and one non-significant result. Nifedipine use prior to cardiac surgery was found to be significantly associated with delirium. The non-specific grouping of psychoactive medication use preoperatively was generally higher with an associated two-to-seven-fold higher risk of postoperative delirium, while only two studies included narcotics without other agents, with one significant and one non-significant result.ConclusionsThere was a limited number of high quality studies in the literature quantifying the direct association between preoperative medication use and postsurgical delirium. More studies are required to evaluate the association of specific preoperative medications on the risk of postoperative delirium so that comprehensive guidelines for medicine use prior to surgery can be developed to aid delirium prevention.Trial registrationThis systematic review has been registered on PROSPERO International prospective register of systematic reviews (Registration number: CRD42016051245 ).

Project description:ObjectiveIn 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.MethodsWe 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.ResultsIncluding 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.ConclusionA 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.

Project description:BackgroundBoth frailty and postoperative delirium (POD) are common in elective surgical patients 65 years of age and older. However, the association between preoperative frailty and POD remains difficult to characterize owing to the large number of frailty and POD assessment tools used in the literature, only a few of which are validated. Furthermore, some validated frailty tools fail to provide clear score cutoffs for distinguishing frail and nonfrail patients. We performed a meta-analysis to estimate the relationship between preoperative frailty and POD.MethodsWe searched several major databases for articles that investigated the relationship between preoperative frailty and POD in patients with mean age ≥65 years who were undergoing elective, nonemergent inpatient surgery. Inclusion criteria included articles published in English no earlier than 1999. Both preoperative frailty and POD must have been measured with validated tools using clear cutoff scores for frailty and delirium. Articles were selected and data extracted independently by 2 researchers. Risk of bias (ROBINS-I) and presence of confounders were summarized. Odds ratios (ORs) for POD associated with frailty relative to nonfrailty were computed with adjusted ORs when available. Original estimates were pooled by random effects analysis. Statistical significance was set at 2-sided P < .05.ResultsNine studies qualified for meta-analysis. The Fried score or a modified version of it was used in 5 studies. Frailty prevalence ranged from 18.6% to 56%. Delirium was assessed with the Confusion Assessment Method (CAM) or Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in 7 studies, Delirium Observation Scale in 1 study, and Intensive Care Delirium Screening Checklist in 1 study. The incidence of POD ranged from 7% to 56%. ROBINS-I risk of bias was low in 1 study, moderate in 4 studies, serious in 3 studies, and critical in 1 study. Random effects analysis (n = 794) of the OR for POD in frail versus nonfrail patients based on adjusted OR estimates was significant with an OR of 2.14 and a 95% confidence interval of 1.43-3.19. The I2 value was in the low range at 5.5, suggesting small variability from random effects. Funnel-plot analysis did not definitively support either the presence or absence of publication bias.ConclusionsThis meta-analysis provides evidence for a significant association between preoperative frailty and POD in elective surgical patients age 65 years or older.

Dataset Information

Preoperative electroencephalographic alpha-power changes with eyes opening are associated with postoperative attention impairment and inattention-related delirium severity.

Background

Methods

Results

Conclusions

Publications

Preoperative electroencephalographic alpha-power changes with eyes opening are associated with postoperative attention impairment and inattention-related delirium severity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets