Arendina W. van der Kooi

The attributable mortality of delirium in critically ill patients: prospective cohort study

Objective To determine the attributable mortality caused by delirium in critically ill patients. Design Prospective cohort study. Setting 32 mixed bed intensive care unit in the Netherlands, January 2011 to July 2013. Participants 1112 consecutive adults admitted to an intensive care unit for a minimum of 24 hours. Exposures Trained observers evaluated delirium daily using a validated protocol. Logistic regression and competing risks survival analyses were used to adjust for baseline variables and a marginal structural model analysis to adjust for confounding by evolution of disease severity before the onset of delirium. Main outcome measure Mortality during admission to an intensive care unit. Results Among 1112 evaluated patients, 558 (50.2%) developed at least one episode of delirium, with a median duration of 3 days (interquartile range 2-7 days). Crude mortality was 94/558 (17%) in patients with delirium compared with 40/554 (7%) in patients without delirium (P<0.001). Delirium was significantly associated with mortality in the multivariable logistic regression analysis (odds ratio 1.77, 95% confidence interval 1.15 to 2.72) and survival analysis (subdistribution hazard ratio 2.08, 95% confidence interval 1.40 to 3.09). However, the association disappeared after adjustment for time varying confounders in the marginal structural model (subdistribution hazard ratio 1.19, 95% confidence interval 0.75 to 1.89). Using this approach, only 7.2% (95% confidence interval −7.5% to 19.5%) of deaths in the intensive care unit were attributable to delirium, with an absolute mortality excess in patients with delirium of 0.9% (95% confidence interval −0.9% to 2.3%) by day 30. In post hoc analyses, however, delirium that persisted for two days or more remained associated with a 2.0% (95% confidence interval 1.2% to 2.8%) absolute mortality increase. Furthermore, competing risk analysis showed that delirium of any duration was associated with a significantly reduced rate of discharge from the intensive care unit (cause specific hazard ratio 0.65, 95% confidence interval 0.55 to 0.76). Conclusions Overall, delirium prolongs admission in the intensive care unit but does not cause death in critically ill patients. Future studies should focus on episodes of persistent delirium and its long term sequelae rather than on acute mortality. Trial registration Clinicaltrials.gov NCT01905033.

Delirium Detection Using EEG: What and How to Measure

BACKGROUND Despite its frequency and impact, delirium is poorly recognized in postoperative and critically ill patients. EEG is highly sensitive to delirium but, as currently used, it is not diagnostic. To develop an EEG-based tool for delirium detection with a limited number of electrodes, we determined the optimal electrode derivation and EEG characteristic to discriminate delirium from nondelirium. METHODS Standard EEGs were recorded in 28 patients with delirium and 28 age- and sex-matched patients who had undergone cardiothoracic surgery and were not delirious, as classified by experts using Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. The first minute of artifact-free EEG data with eyes closed as well as with eyes open was selected. For each derivation, six EEG parameters were evaluated. Using Mann-Whitney U tests, all combinations of derivations and parameters were compared between patients with delirium and those without. Corresponding P values, corrected for multiple testing, were ranked. RESULTS The largest difference between patients with and without delirium and highest area under the receiver operating curve (0.99; 95% CI, 0.97-1.00) was found during the eyes-closed periods of the EEG, using electrode derivation F8-Pz (frontal-parietal) and relative δ power (median [interquartile range (IQR)] for delirium, 0.59 [IQR, 0.47-0.71] and for nondelirium, 0.20 [IQR, 0.17-0.26]; P = .0000000000018). With a cutoff value of 0.37, it resulted in a sensitivity of 100% (95% CI, 100%-100%) and specificity of 96% (95% CI, 88%-100%). CONCLUSIONS In a homogenous population of nonsedated patients who had undergone cardiothoracic surgery, we observed that relative δ power from an eyes-closed EEG recording with only two electrodes in a frontal-parietal derivation can distinguish among patients who have delirium and those who do not.

Decreased Functional Connectivity and Disturbed Directionality of Information Flow in the Electroencephalography of Intensive Care Unit Patients with Delirium after Cardiac Surgery

Background:In this article, the authors explore functional connectivity and network topology in electroencephalography recordings of patients with delirium after cardiac surgery, aiming to improve the understanding of the pathophysiology and phenomenology of delirium. The authors hypothesize that disturbances in attention and consciousness in delirium may be related to alterations in functional neural interactions. Methods:Electroencephalography recordings were obtained in postcardiac surgery patients with delirium (N = 25) and without delirium (N = 24). The authors analyzed unbiased functional connectivity of electroencephalography time series using the phase lag index, directed phase lag index, and functional brain network topology using graph analysis. Results:The mean phase lag index was lower in the &agr; band (8 to 13 Hz) in patients with delirium (median, 0.120; interquartile range, 0.113 to 0.138) than in patients without delirium (median, 0.140; interquartile range, 0.129 to 0.168; P < 0.01). Network topology in delirium patients was characterized by lower normalized weighted shortest path lengths in the &agr; band (t = −2.65; P = 0.01). &dgr; Band–directed phase lag index was lower in anterior regions and higher in central regions in delirium patients than in nondelirium patients (F = 4.53; P = 0.04, and F = 7.65; P < 0.01, respectively). Conclusions:Loss of &agr; band functional connectivity, decreased path length, and increased &dgr; band connectivity directed to frontal regions characterize the electroencephalography during delirium after cardiac surgery. These findings may explain why information processing is disturbed in delirium.

Original Research: Critical CareDelirium Detection Using EEG

BACKGROUND Despite its frequency and impact, delirium is poorly recognized in postoperative and critically ill patients. EEG is highly sensitive to delirium but, as currently used, it is not diagnostic. To develop an EEG-based tool for delirium detection with a limited number of electrodes, we determined the optimal electrode derivation and EEG characteristic to discriminate delirium from nondelirium. METHODS Standard EEGs were recorded in 28 patients with delirium and 28 age- and sex-matched patients who had undergone cardiothoracic surgery and were not delirious, as classified by experts using Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. The first minute of artifact-free EEG data with eyes closed as well as with eyes open was selected. For each derivation, six EEG parameters were evaluated. Using Mann-Whitney U tests, all combinations of derivations and parameters were compared between patients with delirium and those without. Corresponding P values, corrected for multiple testing, were ranked. RESULTS The largest difference between patients with and without delirium and highest area under the receiver operating curve (0.99; 95% CI, 0.97-1.00) was found during the eyes-closed periods of the EEG, using electrode derivation F8-Pz (frontal-parietal) and relative δ power (median [interquartile range (IQR)] for delirium, 0.59 [IQR, 0.47-0.71] and for nondelirium, 0.20 [IQR, 0.17-0.26]; P = .0000000000018). With a cutoff value of 0.37, it resulted in a sensitivity of 100% (95% CI, 100%-100%) and specificity of 96% (95% CI, 88%-100%). CONCLUSIONS In a homogenous population of nonsedated patients who had undergone cardiothoracic surgery, we observed that relative δ power from an eyes-closed EEG recording with only two electrodes in a frontal-parietal derivation can distinguish among patients who have delirium and those who do not.

What Are the Opportunities for EEG-Based Monitoring of Delirium in the ICU?

Recognition of delirium in intensive care unit (ICU) patients is poor, despite the use of screening tools. Electroencephalography (EEG) with a limited number of electrodes and automatic processing may be a more sensitive approach for delirium monitoring. The authors conducted a systematic literature search on EEG characteristics that define delirium, finding 14 studies, which were predominantly conducted in elderly patients. The relative power of the theta and alpha frequency band most often (7/14 studies) distinguished delirium from non-delirium subjects. Given the feasibility for continuous EEG monitoring in ICU, EEG delirium monitoring in ICU patients is promising.

Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia

OBJECTIVE To gain insight in the underlying mechanism of reduced levels of consciousness due to hypoactive delirium versus recovery from anesthesia, we studied functional connectivity and network topology using electroencephalography (EEG). METHODS EEG recordings were performed in age and sex-matched patients with hypoactive delirium (n=18), patients recovering from anesthesia (n=20), and non-delirious control patients (n=20), all after cardiac surgery. Functional and directed connectivity were studied with phase lag index and directed phase transfer entropy. Network topology was characterized using the minimum spanning tree (MST). A random forest classifier was calculated based on all measures to obtain discriminative ability between the three groups. RESULTS Non-delirious control subjects showed a back-to-front information flow, which was lost during hypoactive delirium (p=0.01) and recovery from anesthesia (p<0.01). The recovery from anesthesia group had more integrated network in the delta band compared to non-delirious controls. In contrast, hypoactive delirium showed a less integrated network in the alpha band. High accuracy for discrimination between hypoactive delirious patients and controls (86%) and recovery from anesthesia and controls (95%) were found. Accuracy for discrimination between hypoactive delirium and recovery from anesthesia was 73%. CONCLUSION Loss of functional and directed connectivity were observed in both hypoactive delirium and recovery from anesthesia, which might be related to the reduced level of consciousness in both states. These states could be distinguished in topology, which was a less integrated network during hypoactive delirium. SIGNIFICANCE Functional and directed connectivity are similarly disturbed during a reduced level of consciousness due to hypoactive delirium and sedatives, however topology was differently affected.

Heart rate variability in intensive care unit patients with delirium

Sympathovagal balance, assessed with heart rate variability (HRV), may be altered in intensive care unit (ICU) delirium. HRV was measured in the frequency domain [low frequencies (LF)=0.04-0.15 Hz and high frequencies (HF)=0.15-0.40 Hz] with HF in normalized units (HFnu) to evaluate parasympathetic tone and LF:HF ratio for sympathovagal balance. The authors assessed 726 ICU patients and excluded patients with conditions affecting HRV. No difference could be found between patients with (N=13) and without (N=12) delirium by comparing the mean (±standard deviation) of the HFnu (75±7 versus 68±23) and the LF:HF ratio (-0.7±1.0 versus -0.1±1.1). This study suggests that autonomic function is not altered in ICU delirium.

Temperature Variability during Delirium in ICU Patients: An Observational Study

Introduction Delirium is an acute disturbance of consciousness and cognition. It is a common disorder in the intensive care unit (ICU) and associated with impaired long-term outcome. Despite its frequency and impact, delirium is poorly recognized by ICU-physicians and –nurses using delirium screening tools. A completely new approach to detect delirium is to use monitoring of physiological alterations. Temperature variability, a measure for temperature regulation, could be an interesting component to monitor delirium, but whether temperature regulation is different during ICU delirium has not yet been investigated. The aim of this study was to investigate whether ICU delirium is related to temperature variability. Furthermore, we investigated whether ICU delirium is related to absolute body temperature. Methods We included patients who experienced both delirium and delirium free days during ICU stay, based on the Confusion Assessment method for the ICU conducted by a research- physician or –nurse, in combination with inspection of medical records. We excluded patients with conditions affecting thermal regulation or therapies affecting body temperature. Daily temperature variability was determined by computing the mean absolute second derivative of the temperature signal. Temperature variability (primary outcome) and absolute body temperature (secondary outcome) were compared between delirium- and non-delirium days with a linear mixed model and adjusted for daily mean Richmond Agitation and Sedation Scale scores and daily maximum Sequential Organ Failure Assessment scores. Results Temperature variability was increased during delirium-days compared to days without delirium (βunadjusted=0.007, 95% confidence interval (CI)=0.004 to 0.011, p<0.001). Adjustment for confounders did not alter this result (βadjusted=0.005, 95% CI=0.002 to 0.008, p<0.001). Delirium was not associated with absolute body temperature (βunadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.61). This did not change after adjusting for confounders (βadjusted=-0.03, 95% CI=-0.17 to 0.10, p=0.63). Conclusions Our study suggests that temperature variability is increased during ICU delirium.

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.