Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium.

OBJECTIVE: This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. METHODS: This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. RESULTS: An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61). CONCLUSIONS: RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SIGNIFICANCE: Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.

[Electroencephalography and connectivity in delirium]. / Eeg en innovatieve behandeling van verminderde hersenconnectiviteit bij delirium.

BACKGROUND: Delirium is associated with neurophysiological changes that can be identified with quantitative EEG analysis techniques (qEEG). AIM: To provide an overview of studies on neurophysiological changes in delirium using various qEEG analysis techniques. METHOD: Literature review. RESULTS: In delirium, there is an increase in delta and theta activity but a decrease in activity in the alpha frequency band. Additionally, there is a decrease in functional connectivity and efficiency of the brain network in the alpha frequency band. CONCLUSION: Delirium is characterized by diffuse slowing of the EEG, reduced functional connectivity, and decreased efficiency of the brain network. Improved functional connectivity could be a new approach to treat delirium.

Family participation in physiotherapy-related tasks of critically ill patients: A mixed methods systematic review.

PURPOSE: Providing an overview of studies on family participation in physiotherapy-related tasks of critically ill patients, addressing two research questions (RQ): 1) What are the perceptions of patients, relatives, and staff about family participation in physiotherapy-related tasks? and 2) What are the effects of interventions of family participation in physiotherapy-related tasks? MATERIAL AND METHODS: Qualitative, quantitative and mixed-methods articles were identified using PubMed, Embase and CINAHL. Studies reporting on family participation in physiotherapy-related tasks of adult critically ill patients were included. A convergent segregated approach for mixed-methods reviews was used. RESULTS: Eighteen articles were included; 13 for RQ1, and 5 for RQ2. The included studies were quantitative, qualitative and mixed-method, including between 8 and 452 participants. The descriptive studies exhibit a general appreciation for involvement of relatives in physiotherapy-related tasks, although most of the studies reported on family involvement in general care and incorporated diverse physiotherapy-related tasks. One study explored the effectiveness of family participation on a rehabilitation outcome and showed that the percentage of patients mobilizing three times a day increased. CONCLUSION: Positive attitudes were observed among patients, their relatives and staff towards family participation in physiotherapy-related tasks of critically ill patients. However, limited research has been done into the effect of interventions containing family participation in physiotherapy-related tasks.

fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study.

Delirium, the clinical expression of acute encephalopathy, is a common neuropsychiatric syndrome that is related to poor outcomes, such as long-term cognitive impairment. Disturbances of functional brain networks are hypothesized to predispose for delirium. The aim of this study in non-delirious elderly individuals was to investigate whether predisposing risk factors for delirium are associated with fMRI network characteristics that have been observed during delirium. As predisposing risk factors, we studied age, alcohol misuse, cognitive impairment, depression, functional impairment, history of transient ischemic attack or stroke, and physical status. In this multicenter study, we included 554 subjects and analyzed resting-state fMRI data from 222 elderly subjects (63% male, age range: 65-85 year) after rigorous motion correction. Functional network characteristics were analyzed and based on the minimum spanning tree (MST). Global functional connectivity strength, network efficiency (MST diameter) and network integration (MST leaf fraction) were analyzed, as these measures were altered during delirium in previous studies. Linear regression analyses were used to investigate the relation between predisposing delirium risk factors and delirium-related fMRI characteristics, adjusted for confounding and multiple testing. Predisposing risk factors for delirium were not associated with delirium-related fMRI network characteristics. Older age within our elderly cohort was related to global functional connectivity strength (ß = 0.182, p < 0.05), but in the opposite direction than hypothesized. Delirium-related functional network impairments can therefore not be considered as the common mechanism for predisposition for delirium.

Predisposition for delirium and EEG characteristics.

OBJECTIVE: Delirium is associated with increased electroencephalography (EEG) delta activity, decreased connectivity strength and decreased network integration. To improve our understanding of development of delirium, we studied whether non-delirious individuals with a predisposition for delirium also show these EEG abnormalities. METHODS: Elderly subjects (N = 206) underwent resting-state EEG measurements and were assessed on predisposing delirium risk factors, i.e. older age, alcohol misuse, cognitive impairment, depression, functional impairment, history of stroke and physical status. Delirium-related EEG characteristics of interest were relative delta power, alpha connectivity strength (phase lag index) and network integration (minimum spanning tree leaf fraction). Linear regression analyses were used to investigate the relation between predisposing delirium risk factors and EEG characteristics that are associated with delirium, adjusting for confounding and multiple testing. RESULTS: Functional impairment was related to a decrease in connectivity strength (adjusted R2 = 0.071, ß = 0.201, p < 0.05). None of the other risk factors had significant influence on EEG delta power, connectivity strength or network integration. CONCLUSIONS: Functional impairment seems to be associated with decreased alpha connectivity strength. Other predisposing risk factors for delirium had no effect on the studied EEG characteristics. SIGNIFICANCE: Predisposition for delirium is not consistently related to EEG characteristics that can be found during delirium.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis.

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

Delirium detection using relative delta power based on 1-minute single-channel EEG: a multicentre study.

BACKGROUND: Delirium is frequently unrecognised. EEG shows slower frequencies (i.e. below 4 Hz) during delirium, which might be useful in improving delirium recognition. We studied the discriminative performance of a brief single-channel EEG recording for delirium detection in an independent cohort of patients. METHODS: In this prospective, multicentre study, postoperative patients aged ≥60 yr were included (n=159). Before operation and during the first 3 postoperative days, patients underwent a 5-min EEG recording, followed by a video-recorded standardised cognitive assessment. Two or, in case of disagreement, three delirium experts classified each postoperative day based on the video and chart review. Relative delta power (1-4 Hz) was based on 1-min artifact-free EEG. The diagnostic value of the relative delta power was evaluated by the area under the receiver operating characteristic curve (AUROC), using the expert classification as the gold standard. RESULTS: Experts classified 84 (23.3%) postoperative days as either delirium or possible delirium, and 276 (76.7%) non-delirium days. The AUROC of the relative EEG delta power was 0.75 [95% confidence interval (CI) 0.69-0.82]. Exploratory analysis showed that relative power from 1 to 6 Hz had significantly higher AUROC (0.78, 95% CI 0.72-0.84, P=0.014). CONCLUSIONS: Delirium/possible delirium can be detected in older postoperative patients based on a single-channel EEG recording that can be automatically analysed. This objective detection method with a continuous scale instead of a dichotomised outcome is a promising approach for routine detection of delirium. CLINICAL TRIAL REGISTRATION: NCT02404181.

Outcome in Patients with Isolated Moderate to Severe Traumatic Brain Injury.

INTRODUCTION: Traumatic brain injury (TBI) remains a major cause of death. Withdrawal of life-sustaining treatment (WLST) can be initiated if there is little anticipated chance of recovery to an acceptable quality of life. The aim of this study was firstly to investigate WLST rates in patients with moderate to severe isolated TBI and secondly to assess outcome data in the survivor group. MATERIAL AND METHODS: A retrospective cohort study was performed. Patients aged ≥ 18 years with moderate or severe isolated TBI admitted to the ICU of a single academic hospital between 2011 and 2015 were included. Exclusion criteria were isolated spinal cord injury and referrals to and from other hospitals. Gathered data included demographics, mortality, cause of death, WLST, and Glasgow Outcome Scale (GOS) score after three months. Good functional outcome was defined as GOS > 3. RESULTS: Of 367 patients, 179 patients were included after applying inclusion and exclusion criteria. 55 died during admission (33%), of whom 45 (82%) after WLST. Patients undergoing WLST were older, had worse neurological performance at presentation, and had more radiological abnormalities than patients without WLST. The decision to withdraw life-sustaining treatment was made on the day of admission in 40% of patients. In 33% of these patients, this decision was made while the patient was in the Emergency Department. 71% of survivors had a good functional outcome after three months. No patient left hospital with an unresponsive wakefulness syndrome (UWS) or suffered from UWS after three months. One patient died within three months of discharge. CONCLUSION: In-hospital mortality in isolated brain injured patients was 33%. The vast majority died after a decision to withdraw life-sustaining treatment. None of the patients were discharged with an unresponsive wakefulness syndrome.

Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review.

BACKGROUND: Intraoperative hypotension is a common side effect of general anaesthesia and might lead to inadequate organ perfusion. It is unclear to what extent hypotension during noncardiac surgery is associated with unfavourable outcomes. METHODS: We conducted a systematic search in PubMed, Embase, Web of Science, and CINAHL, and classified the quality of retrieved articles according to predefined adapted STROBE and CONSORT criteria. Reported strengths of associations from high-quality studies were classified into end-organ specific injury risks, such as acute kidney injury, myocardial injury, and stroke, and overall organ injury risks for various arterial blood pressure thresholds. RESULTS: We present an overview of 42 articles on reported associations between various absolute and relative intraoperative hypotension definitions and their associations with postoperative adverse outcomes after noncardiac surgery. Elevated risks of end-organ injury were reported for prolonged exposure (≥10 min) to mean arterial pressures <80 mm Hg and for shorter durations <70 mm Hg. Reported risks increase with increased durations for mean arterial pressures <65-60 mm Hg or for any exposure <55-50 mm Hg. CONCLUSIONS: The reported associations suggest that organ injury might occur when mean arterial pressure decreases <80 mm Hg for ≥10 min, and that this risk increases with blood pressures becoming progressively lower. Given the retrospective observational design of the studies reviewed, reflected by large variability in patient characteristics, hypotension definitions and outcomes, solid conclusions on which blood pressures under which circumstances are truly too low cannot be drawn. We provide recommendations for the design of future studies. CLINICAL REGISTRATION NUMBER: (PROSPERO ID). CRD42013005171.

Size matters: Grey matter brain reserve predicts executive functioning in the elderly.

Preserved executive functioning (EF) is crucial for daily functioning in the elderly and it appears to predict dementia development. We sought to clarify the role of atrophy-corrected cortical grey matter (GM) volume as a potential brain reserve (BR) marker for EF in the elderly. In total, 206 pre-surgical subjects (72.50 ±â€¯4.95 years; mean MMSE score 28.50) were investigated. EF was primarily assessed using the Trail Making Test B (TMT B). Global/ lobar GM volumes were acquired with T1 MP-RAGE. Adjusting for key covariates including a brain atrophy index (i.e. brain parenchymal fraction), multiple linear regression analysis was used to study associations of GM volumes and TMT B. All GM volumes - most notably of global GM - were significantly associated with TMT B independently of GM atrophy (ß = -0.201 to -0.275, p = 0.001-0.012). Using atrophy-corrected GM volume as an estimate of maximal GM size in youth may serve as a BR predictor for cognitive decline in future studies investigating BR in the elderly.

Personalized risk prediction of postoperative cognitive impairment - rationale for the EU-funded BioCog project.

Postoperative cognitive impairment is among the most common medical complications associated with surgical interventions - particularly in elderly patients. In our aging society, it is an urgent medical need to determine preoperative individual risk prediction to allow more accurate cost-benefit decisions prior to elective surgeries. So far, risk prediction is mainly based on clinical parameters. However, these parameters only give a rough estimate of the individual risk. At present, there are no molecular or neuroimaging biomarkers available to improve risk prediction and little is known about the etiology and pathophysiology of this clinical condition. In this short review, we summarize the current state of knowledge and briefly present the recently started BioCog project (Biomarker Development for Postoperative Cognitive Impairment in the Elderly), which is funded by the European Union. It is the goal of this research and development (R&D) project, which involves academic and industry partners throughout Europe, to deliver a multivariate algorithm based on clinical assessments as well as molecular and neuroimaging biomarkers to overcome the currently unsatisfying situation.

[Recognising postoperative delirium in the elderly]. / Postoperatief delirium herkennen bij ouderen.

OBJECTIVE: To determine the degree of agreement between delirium experts on the diagnosis of delirium based on exactly the same information, and to assess the sensitivity of delirium screening methods used by clinical nurses. DESIGN: Prospective observational longitudinal study. METHOD: Older patients (≥ 60 years) who underwent major surgery were included. During the first three days after surgery they had a standardised cognitive screening test which was recorded on video. Two delirium experts independently evaluated these videos and the information from the patient records. They classified the patients as having 'no delirium', 'possible delirium' or 'delirium'. If there was disagreement, a third expert was consulted. The final classification, based on consensus of two or three delirium experts, was compared with the result of the delirium screening carried out by the clinical nurses. RESULTS: A total of 167 patients were included and 424 postoperative classifications were obtained. The agreement between the experts was 0.61 (95% confidence interval (CI): 0.53-0.68), based on Cohen's kappa. In 89 (21.0%) of the postoperative classifications there was no agreement between the experts and a third expert was consulted. The nurses using the delirium screening tools recognised 32% of the cases that had been classified as delirium by the experts. CONCLUSION: There was considerable disagreement between the classifications of individual delirium experts, based on exactly the same information, indicating the difficulty of the diagnosis. Furthermore, the sensitivity of the delirium screening tools used by the clinical nurses was poor. Further research should focus on the development of objective methods for recognising delirium.

Association between delirium and cognitive change after cardiac surgery.

BACKGROUND: Previous studies provide inconsistent data on whether postoperative delirium (POD) is a risk factor for postoperative cognitive decline (POCD). We thus investigated the relationship between POD and cognitive change after cardiac surgery and assessed the relationship between preoperative cognitive domain scores and POD. METHODS: Postoperative delirium was assessed with the Confusion Assessment Method (CAM) adapted for the intensive care unit and the conventional CAM accompanied by chart review. Cognitive function was assessed with a neuropsychological test battery before elective cardiac surgery and 1 month and 1 yr afterwards. Cognitive change was calculated using the Reliable Change Index (RCI). Multiple linear regression was used to adjust for confounding. RESULTS: Of the 184 patients who completed baseline assessment, 23 (12.5%) developed POD. At 1 month, the decline in cognitive performance was worse in patients with POD [median composite RCI -1.00, interquartile range (IQR) -1.67 to 0.28] than in patients without POD (RCI -0.04, IQR -0.70 to 0.63, P =0.02). At 1 yr, both groups showed cognitive improvement on average compared with baseline (POD patients median composite RCI 0.25, IQR -0.42 to 1.31, vs non-POD patients RCI 0.92, IQR 0.18-1.53; P =0.08). Correction for differences in age and level of education did not change the results. Patients with POD performed less well than patients without POD on the preoperative Trailmaking test part A ( P =0.03). CONCLUSIONS: Postoperative delirium is independently associated with cognitive decline 1 month after surgery, but cognitive performance generally recovers in 1 yr. Patients with a predisposition to POD can be identified before surgery by worse performance in an attention task. CLINICAL TRIAL REGISTRATION: NCT00293592.

Delirium in critically ill patients.

Delirium is common in critically ill patients and associated with increased length of stay in the intensive care unit (ICU) and long-term cognitive impairment. The pathophysiology of delirium has been explained by neuroinflammation, an aberrant stress response, neurotransmitter imbalances, and neuronal network alterations. Delirium develops mostly in vulnerable patients (e.g., elderly and cognitively impaired) in the throes of a critical illness. Delirium is by definition due to an underlying condition and can be identified at ICU admission using prediction models. Treatment of delirium can be improved with frequent monitoring, as early detection and subsequent treatment of the underlying condition can improve outcome. Cautious use or avoidance of benzodiazepines may reduce the likelihood of developing delirium. Nonpharmacologic strategies with early mobilization, reducing causes for sleep deprivation, and reorientation measures may be effective in the prevention of delirium. Antipsychotics are effective in treating hallucinations and agitation, but do not reduce the duration of delirium. Combined pain, agitation, and delirium protocols seem to improve the outcome of critically ill patients and may reduce delirium incidence.

Intraoperative hypotension and delirium after on-pump cardiac surgery.

BACKGROUND: Delirium is a common complication after cardiac surgery and may be as a result of inadequate cerebral perfusion. We studied delirium after cardiac surgery in relation to intraoperative hypotension (IOH). METHODS: This observational single-centre, cohort study was nested in a randomized trial, on a single intraoperative dose of dexamethasone vs placebo during cardiac surgery. During the first four postoperative days, patients were screened for delirium based on the Confusion Assessment Method (CAM) for Intensive Care Unit on the intensive care unit, CAM on the ward, and by inspection of medical records. To combine depth and duration of IOH, we computed the area under the curve for four blood pressure thresholds. Logistic regression analyses were performed to investigate the association between IOH and the occurrence of postoperative delirium, adjusting for confounding and using a 99% confidence interval to correct for multiple testing. RESULTS: Of the 734 included patients, 99 patients (13%) developed postoperative delirium. The adjusted Odds Ratio for the Mean Arterial Pressure <60 mm Hg threshold was 1.04 (99% confidence interval: 0.99-1.10) for each 1000 mm Hg(2) min(2) AUC(2) increase. IOH, as defined according to the other three definitions, was not associated with postoperative delirium either. Deep and prolonged IOH seemed to increase the risk of delirium, but this was not statistically significant. CONCLUSIONS: Independent of the applied definition, IOH was not associated with the occurrence of delirium after cardiac surgery.

Multinational development and validation of an early prediction model for delirium in ICU patients.

RATIONALE: Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. PURPOSE: To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. METHODS: Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. RESULTS: In total, 2914 patients were included. Delirium incidence was 23.6%. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95% confidence interval (CI) 0.73-0.77] in the development dataset and 0.75 (95% CI 0.71-0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95% CI 0.67-0.74), for delirium that developed <2 days, to 0.81 (95% CI 0.78-0.84), for delirium that developed >6 days. CONCLUSION: Patients' delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.

Classification of daily mental status in critically ill patients for research purposes.

PURPOSE: The purpose of this study is to develop a reliable tool for daily mental status classification in intensive care unit (ICU) patients for research purposes. Secondly, to identify patients with single, 1-day episodes of delirium and to compare them with patients having more delirium days or episodes. PATIENTS AND METHODS: A 5-step algorithm was designed, which includes Richmond Agitation Sedation Scale and Confusion Assessment Method for the ICU scores from bedside nurses, initiation of delirium treatment, chart review, and the Confusion Assessment Method for the ICU administered by researchers. This algorithm was validated against a reference standard of delirium experts. Subsequently, a cohort study was performed in patients admitted to a mixed ICU. RESULTS: In 65 paired observations, the algorithm had 0.75 sensitivity and 0.85 specificity. Applying the algorithm, interobserver agreement was high with mean Fleiss κ of 0.94 (5 raters) and 0.97 (4 raters). In the cohort study, 1112 patients were included of whom 535 (48%) became delirious. Single, 1-day episodes occurred in 43% of the delirious patients, whom were characterized by lower age compared with those with more delirium days. CONCLUSIONS: The algorithm for daily mental status classification seems to be a valid tool. In a substantial proportion of patients, delirium occurs only once during ICU admission lasting only 1 day.