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.

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 α 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 α band (t = -2.65; P = 0.01). δ 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 α band functional connectivity, decreased path length, and increased δ 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.

Delirium detection based on monitoring of blinks and eye movements.

OBJECTIVE: To investigate whether delirious patients differ from nondelirious patients with regard to blinks and eye movements to explore opportunities for delirium detection. METHODS: Using a single-center, observational study in a tertiary hospital in the Netherlands, we studied 28 delirious elderly and 28 age- and gender-matched (group level) nondelirious elderly, postoperative cardiac surgery patients. Patients were evaluated for delirium by a geriatrician, psychiatrist, or neurologist using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria. Blinks were automatically extracted from electro-oculograms and eye movements from electroencephalography recordings using independent component analysis. The number and duration of eye movements and blinks were compared between patients with and without delirium, based on the classification of the delirium experts described above. RESULTS: During eyes-open registrations, delirious patients showed, compared with nondelirious patients, a significant decrease in the number of blinks per minute (median: 12 [interquartile range {IQR}: 5-18] versus 18 [IQR: 8-25], respectively; p = 0.02) and number of vertical eye movements per minute (median: 1 [IQR: 0-13] versus 15 [IQR: 2-54], respectively; p = 0.01) as well as an increase in the average duration of blinks (median: 0.5 [IQR: 0.36-0.95] seconds versus 0.34 [IQR: 0.23-0.53] seconds, respectively; p <0.01). During eyes-closed registrations, the average duration of horizontal eye movements was significantly increased in delirious patients compared with patients without delirium (median: 0.41 [IQR: 0.15-0.75] seconds versus 0.08 [IQR: 0.06-0.22] seconds, respectively; p <0.01). CONCLUSION: Spontaneous eye movements and particularly blinks appear to be affected in delirious patients, which holds promise for delirium detection.

Cognitive impairment after intensive care unit admission: a systematic review.

PURPOSE: There is increasing evidence that critical illness and treatment in an intensive care unit (ICU) may result in significant long-term morbidity. The purpose of this systematic review was to summarize the current literature on long-term cognitive impairment in ICU survivors. METHODS: PubMed/MEDLINE, CINAHL, Cochrane Library, PsycINFO and Embase were searched from January 1980 until July 2012 for relevant articles evaluating cognitive functioning after ICU admission. Publications with an adult population and a follow-up duration of at least 2 months were eligible for inclusion in the review. Studies in cardiac surgery patients or subjects with brain injury or cardiac arrest prior to ICU admission were excluded. The main outcome measure was cognitive functioning. RESULTS: The search strategy identified 1,128 unique studies, of which 19 met the selection criteria and were included. Only one article compared neuropsychological test performance before and after ICU admission. The 19 studies that were selected reported a wide range of cognitive impairment in 4-62 % of the patients after a follow-up of 2-156 months. CONCLUSION: The results of most studies of the studies reviewed suggest that critical illness and ICU treatment are associated with long-term cognitive impairment. Due to the complexity of defining cognitive impairment, it is difficult to standardize definitions and to reach consensus on how to categorize neurocognitive dysfunction. Therefore, the magnitude of the problem is uncertain.