Prognostic roles of sleep electroencephalography pattern and circadian rhythm biomarkers in the recovery of consciousness in patients with coma: a prospective cohort study

The value of electroencephalography features in the prognostic evaluation of large hemispheric infarction patients at different time intervals

BACKGROUND

Large Hemispheric Infarction (LHI) is a devastating disease with high mortality. This study aimed to use electroencephalography (EEG) to evaluate the death risk of LHI patients and identify suitable evaluation time.

METHODS

This study retrospectively collected clinical and EEG data from 73 LHI patients, dividing them into death and survival group at discharge. EEG data was classified as 1-5 days and 6-14 days after onset according to the time intervals of cerebral edema. Regression and receiver operator characteristic curve (ROC) analysis were applied to explore the impact of temporal changes in various EEG and clinical features on death.

RESULTS

The areas under ROC curve (AUC) of death prediction for non-α frequency on non-infarct side at 6-14 days after onset was significantly higher than that at 1-5 days (p = 0.004). And there was no significant difference between the AUC of seizure activity for death prediction at 1-5 days and 6-14 days (p = 0.418). Multivariate regression analysis revealed that non-α frequency on non-infarct side and seizure activity at 6-14 days after onset were the independent risk factors for the death of LHI patients. Additionally, above two EEG features significantly improved the death predictive efficacy of clinical features in LHI patients with the integrated discrimination improvement index (IDI) of 0.174 (p = 0.015) and the net reclassification improvement (NRI) of 1.314 (p<0.001).

CONCLUSIONS

Non-α frequency on non-infarct side and seizure activity were reliable indicators for death prediction. 6-14 days after onset was the better time window for death evaluation of LHI patients through EEG.

A novel nomogram for predicting the prognosis of critically ill patients with EEG patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges

OBJECTIVES

To explore the factors associated with poor prognosis in critically ill patients with Electroencephalogram (EEG) patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and to construct a prognostic prediction model.

METHODS

This study included a total of 53 critically ill patients with EEG patterns exhibiting SIRPIDs who were admitted to the First Affiliated Hospital of Chongqing Medical University from May 2023 to March 2024. Patients were divided into two groups based on their Modified Rankin Scale (mRS) scores at discharge: good prognosis group (0-3 points) and poor prognosis group (4-6 points). Retrospective analyses were performed on the clinical and EEG parameters of patients in both groups. Logistic regression analysis was applied to identify the risk factors related to poor prognosis in critically ill patients with EEG patterns exhibiting SIRPIDs; a risk prediction model for poor prognosis was constructed, along with an individualized predictive nomogram model, and the predictive performance and consistency of the model were evaluated.

RESULTS

Multivariate logistic regression analysis revealed that APACHE II score (OR=1.217, 95 %CI=1.030∼1.438), slow frequency bands or no obvious brain electrical activity (OR=8.720, 95 %CI=1.220∼62.313), and no sleep waveforms (OR=9.813, 95 %CI=1.371∼70.223) were independent risk factors for poor prognosis in patients. A regression model established based on multivariate logistic regression analysis had an area under the curve of 0.902. The model's accuracy was 90.60 %, with a sensitivity of 92.86 % and a specificity of 89.70 %. The nomogram model, after internal validation, showed a concordance index of 0.904.

CONCLUSIONS

A high APACHE II score, EEG patterns with slow frequency bands or no obvious brain electrical activity, and no sleep waveforms were independent risk factors for poor prognosis in patients with SIRPIDs. The nomogram model constructed based on these factors had a favorably high level of accuracy in predicting the risk of poor prognosis and held certain reference and application value for clinical neurofunctional assessment and prognostic determination.

Local Neuronal Activity and the Hippocampal Functional Network Can Predict the Recovery of Consciousness in Individuals With Acute Disorders of Consciousness Caused by Neurological Injury

AIMS

There is limited research on predicting the recovery of consciousness in patients with acute disorders of consciousness (aDOC). The purpose of this study is to investigate the altered characteristics of the local neuronal activity indicated by the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) of the hippocampus network in patients with aDOC caused by neurological injury and to explore whether these characteristics can predict the recovery of consciousness.

METHODS

Thirty-seven patients with aDOC were included, all of whom completed resting-state functional magnetic resonance imaging (rsfMRI) scans. The patients were divided into two groups based on prognosis of consciousness recovery, 24 patients were in prolonged disorders of consciousness (pDOC) and 13 in emergence from minimally conscious state (eMCS) at 3 months after neurological injury. Univariable and multivariate logistic regression analyses were used to investigate the clinical indicators affecting patients' recovery of consciousness. The ALFF values and FC of the hippocampal network were compared between patients with pDOC and those with eMCS. Additionally, we employed the support vector machine (SVM) method to construct a predictive model for prognosis of consciousness based on the ALFF and FC values of the aforementioned differential brain regions. The accuracy (ACC), area under the curve (AUC), sensitivity, and specificity were used to evaluate the efficacy of the model.

RESULTS

The FOUR score at onset and the length of mechanical ventilation (MV) were found to be significant influential factors for patients who recovered to eMCS at 3 months after onset. Patients who improved to eMCS showed significantly increased ALFF values in the right calcarine gyrus, left lingual gyrus, right middle temporal gyrus, and right precuneus compared to patients in a state of pDOC. Furthermore, significant increases in FC values of the hippocampal network were observed in the eMCS group, primarily involving the right lingual gyrus and bilateral precuneus, compared to the pDOC group. The predictive model constructed using ALFF alone or ALFF combined with FC values from the aforementioned brain regions demonstrated high accuracies of 83.78% and 81.08%, respectively, with AUCs of 95% and 94%, sensitivities of 0.92 for both models, and specificities of 0.92 for both models in predicting the recovery of consciousness in patients with aDOC.

CONCLUSION

The present findings demonstrate significant differences in the local ALFF and FC values of the hippocampus network between different prognostic groups of patients with aDOC. The constructed predictive model, which incorporates ALFF and FC values, has the potential to provide valuable insights for clinical decision-making and identifying potential targets for early intervention.

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

The arousal effect of An-Gong-Niu-Huang-Wan on alcoholic-induced coma rats: A research based on EEG

ETHNOPHARMACOLOGICAL RELEVANCE

Acute alcohol intoxication is one of the leading causes of coma. A well-regarded Chinese herbal formula, known as An-Gong-Niu-Huang-Wan (AGNHW), has garnered recognition for its efficacy in treating various brain disorders associated with impaired consciousness, including acute alcohol-induced coma. Despite its clinical effectiveness, the scientific community lacks comprehensive research on the mechanistic aspects of AGNHW's impact on the electroencephalogram (EEG) patterns observed during alcohol-induced coma. Gaining a deeper understanding of AGNHW's mechanism of action in relation to EEG characteristics would hold immense importance, serving as a solid foundation for further advancing its clinical therapeutic application.

AIM OF THE STUDY

The study sought to investigate the impact of AGNHW on EEG activity and sleep EEG patterns in rats with alcoholic-induced coma.

MATERIALS AND METHODS

A rat model of alcohol-induced coma was used to examine the effects of AGNHW on EEG patterns. Male Sprague-Dawley rats were intraperitoneally injected with 32% ethanol to induce a coma, followed by treatment with AGNHW. Wireless electrodes were implanted in the cortex of the rats to obtain EEG signals. Our analysis focused on evaluating alterations in the Rat Coma Scale (RCS), as well as assessing changes in the frequency and distribution of EEG patterns, sleep rhythms, and body temperature subsequent to AGNHW treatment.

RESULTS

The study found a significant increase in the δ-band power ratio, as well as a decrease in RCS scores and β-band power ratio after modeling. AGNHW treatment significantly reduced the δ-band power ratio and increased the β-band power ratio compared to naloxone, suggesting its superior arousal effects. The results also revealed a decrease in the time proportion of WAKE and REM EEG patterns after modeling, accompanied by a significant increase in the time proportion of NREM EEG patterns. Both naloxone and AGNHW effectively counteracted the disordered sleep EEG patterns. Additionally, AGNHW was more effective than naloxone in improving hypothermia caused by acute alcohol poisoning in rats.

CONCLUSION

Our study provides evidence for the arousal effects of AGNHW in alcohol-induced coma rats. It also suggests a potential role for AGNHW in regulating post-comatose sleep rhythm disorders.

Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness

Objective

This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors.

Methods

This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model.

Result

The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets.

Conclusion

This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.

Twenty-four-hour rhythmicities in disorders of consciousness are associated with a favourable outcome

Fluctuations of consciousness and their rhythmicities have been rarely studied in patients with a disorder of consciousness after acute brain injuries. 24-h assessment of brain (EEG), behaviour (eye-opening), and circadian (clock-controlled hormones secretion from urine) functions was performed in acute brain-injured patients. The distribution, long-term predictability, and rhythmicity (circadian/ultradian) of various EEG features were compared with the initial clinical status, the functional outcome, and the circadian rhythmicities of behaviour and clock-controlled hormones. Here we show that more physiological and favourable patterns of fluctuations are associated with a higher 24 h predictability and sharp up-and-down shape of EEG switches, reminiscent of the Flip-Flop model of sleep. Multimodal rhythmic analysis shows that patients with simultaneous circadian rhythmicity for brain, behaviour, and hormones had a favourable outcome. Finally, both re-emerging EEG fluctuations and homogeneous 24-h cycles for EEG, eye-opening, and hormones appeared as surrogates for preserved functionality in brainstem and basal forebrain, which are key prognostic factors for later improvement. While the recovery of consciousness has previously been related to a high short-term complexity, we suggest in this exploratory study the importance of the high predictability of the 24 h long-term generation of brain rhythms and highlight the importance of circadian body-brain rhythms in awakening.

Prognosis of consciousness disorders in the intensive care unit

Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Consciousness is a multifaceted concept, involving both wakefulness, i.e., a condition of being alert that is regulated by the brainstem, and awareness, a subjective experience of any thoughts or perception or emotion. Recently, the European Academy of Neurology has published international guidelines for a better diagnosis of coma and other disorders of consciousness (DOC) through the investigation of sleep patterns, such as slow-wave and REM, and the study of the EEG using machine learning methods and artificial intelligence. The management of sleep disorders in DOC patients is an increasingly hot topic and deserves careful diagnosis, to allow for the most accurate prognosis and the best medical treatment possible. The aim of this review was to investigate the anatomo-physiological basis of the sleep/wake cycle, as well as the main sleep patterns and sleep disorders in patients with DOC. We found that the sleep characteristics in DOC patients are still controversial. DOC patients often present a theta/delta pattern, while epileptiform activity, as well as other sleep elements, have been reported as correlating with outcomes in patients with coma and DOC. The absence of spindles, as well as REM and K-complexes of NREM sleep, have been used as poor predictors for early awakening in DOC patients, especially in UWS patients. Therefore, sleep could be considered a marker of DOC recovery, and effective treatments for sleep disorders may either indirectly or directly favor recovery of consciousness.

Development and Validation of a Website to Guide Decision-Making for Disorders of Consciousness

Background

This study aimed to develop and validate a nomogram and present it on a website to be used to predict the overall survival at 16, 32, and 48 months in patients with prolonged disorder of consciousness (pDOC).

Methods

We retrospectively analyzed the data of 381 patients with pDOC at two centers. The data were randomly divided into training and validation sets using a ratio of 6:4. On the training set, Cox proportional hazard analyses were used to identify the predictive variables. In the training set, two models were screened by COX regression analysis, and based on clinical evidence, model 2 was eventually selected in the nomogram after comparing the receiver operating characteristic (ROC) of the two models. In the training and validation sets, ROC curves, calibration curves, and decision curve analysis (DCA) curves were utilized to measure discrimination, calibration, and clinical efficacy, respectively.

Results

The final model included age, Glasgow coma scale (GCS) score, serum albumin level, and computed tomography (CT) midline shift, all of which had a significant effect on survival after DOCs. For the 16-, 32-, and 48-month survival on the training set, the model had good discriminative power, with areas under the curve (AUCs) of 0.791, 0.760, and 0.886, respectively. For the validation set, the AUCs for the 16-, 32-, and 48-month survival predictions were 0.806, 0.789, and 0.867, respectively. Model performance was good for both the training and validation sets according to calibration plots and DCA.

Conclusion

We developed an accurate, efficient nomogram, and a corresponding website based on four correlated factors to help clinicians improve their assessment of patient outcomes and help personalize the treatment process and clinical decisions.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.

Electroacupuncture Enhances Neuroplasticity by Regulating the Orexin A-Mediated cAMP/PKA/CREB Signaling Pathway in Senescence-Accelerated Mouse Prone 8 (SAMP8) Mice

Learning and memory disorders and decreased neuroplasticity are the main clinical manifestations of age-induced cognitive dysfunction. Orexin A (OxA) has been reported to show abnormally elevated expression in the cerebrospinal fluid (CSF) of patients with Alzheimer's disease (AD) and to be associated with cognitive impairment. Here, we further assessed whether the excitatory neurotransmitter OxA is involved in neuroplasticity and cognitive function in senescence-accelerated mouse prone 8 (SAMP8) mice. In this study, we investigated the mechanism of OxA by using behavioral tests, CSF microdialysis, immunofluorescence, toluidine blue staining, gene silencing, transmission electron microscopy, and Western blotting. The results showed that 10 Hz electroacupuncture (EA) effectively alleviated learning and memory impairment in 7-month-old SAMP8 mice, reduced OxA levels in the CSF, increased the level of the neurotransmitter glutamate, alleviated pathological damage to hippocampal tissue, improved the synaptic structure, enhanced synaptic transmission, and regulated the expression of cAMP/PKA/CREB signaling pathway-related proteins. These results suggest that EA enhances neuroplasticity in SAMP8 mice by regulating the OxA-mediated cAMP/PKA/CREB signaling pathway, thus improving cognitive function. These findings suggest that EA may be beneficial for the prevention and treatment of age-induced cognitive impairment.

A dynamic model to predict long-term outcomes in patients with prolonged disorders of consciousness

PURPOSE

It is important to predict the prognosis of patients with prolonged disorders of consciousness (DOC). This study established and validated a nomogram and corresponding web-based calculator to predict outcomes for patients with prolonged DOC.

METHODS

All data were obtained from the First Affiliated Hospital of Nanchang University and the Shangrao Hospital of Traditional Chinese Medicine. Predictive variables were identified by univariate and multiple logistic regression analyses. Receiver operating characteristic curves, calibration curves, and a decision curve analysis (DCA) were utilized to assess the predictive accuracy, discriminative ability, and clinical utility of the model, respectively.

RESULTS

Independent prognostic factors, such as age, Glasgow coma scale score, state of consciousness, and brainstem auditory-evoked potential grade were integrated into a nomogram. The model demonstrated good discrimination in the training and validation cohorts, with area-under-the-curve values of 0.815 (95% confidence interval [CI]: 0.748-0.882) and 0.805 (95% CI: 0.727-0.883), respectively. The calibration plots and DCA demonstrated good model performance and clear clinical benefits in both cohorts.

CONCLUSIONS

Based on our nomogram, we developed an effective, simple, and accurate model of a web-based calculator that may help individualize healthcare decision-making. Further research is warranted to optimize the system and update the predictors.

Sleep Treatments in Disorders of Consciousness: A Systematic Review

Sleep disorders are among the main comorbidities in patients with a Disorder of Consciousness (DOC). Given the key role of sleep in neural and cognitive functioning, detecting and treating sleep disorders in DOCs might be an effective therapeutic strategy to boost consciousness recovery and levels of awareness. To date, no systematic reviews have been conducted that explore the effect of sleep treatments in DOCs; thus, we systematically reviewed the existing studies on both pharmacological and non-pharmacological treatments for sleep disorders in DOCs. Among 2267 assessed articles, only 7 were included in the systematic review. The studies focused on two sleep disorder categories (sleep-related breathing disorders and circadian rhythm dysregulation) treated with both pharmacological (Modafinil and Intrathecal Baclofen) and non-pharmacological (positive airway pressure, bright light stimulation, and central thalamic deep brain stimulation) interventions. Although the limited number of studies and their heterogeneity do not allow generalized conclusions, all the studies highlighted the effectiveness of treatments on both sleep disorders and levels of awareness. For this reason, clinical and diagnostic evaluations able to detect sleep disorders in DOC patients should be adopted in the clinical routine for the purpose of intervening promptly with the most appropriate treatment.

Narrative Review: Quantitative EEG in Disorders of Consciousness

In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.

A Novel INCNS Score for Prediction of Mortality and Functional Outcome of Comatose Patients

Objectives: The purpose of this study was to verify the veracity and reliability of the INCNS score for prediction of neurological ICU (NICU) mortality and 3-month functional outcome and mortality in comatose patients.

Methods: In this prospective study, data of the patients admitted to NICU from January 2013 to January 2019 were collected for validation. The 3-month functional outcomes were evaluated using modified Rankin Scale (mRS). By using the receiver operating characteristics curve (ROC) analysis, we compared the INCNS score with Glasgow Coma Scale (GCS), Full Outline of Un-Responsiveness Score (FOUR) and Acute Physiology and Chronic Health Evaluation II (APACHE II) for assessment of the predictive performance of these scales for 3-month functional outcome and mortality and NICU mortality performed at 24- and 72-h after admission to the NICU.

Results: Totally 271 patients were used for evaluation; the INCNS score achieved an AUC (area under the receiver operating characteristic curve) of 0.766 (95% CI: 0.711–0.815) and 0.824 (95% CI: 0.774–0.868) for unfavorable functional outcomes, an AUC of 0.848 (95% CI: 0.800–0.889) and 0.892 (95% CI: 0.848–0.926) for NICU mortality, and an AUC of 0.811 (95% CI: 0.760–0.856) and 0.832 (95% CI: 0.782–0.874) for the 3-month mortality after discharge from the NICU at 24- and 72-h. The INCNS score exhibited a significantly better predictive performance of mortality and 3-month functional outcomes than FOUR and GCS. There was no significant difference in predicting NICU mortality and 3-month functional outcomes between INCNS and APACHE II, but INCNS had better predictive performance of 3-month mortality than APACHE II.

Conclusions: The INCNS score could be used for predicting the functional outcomes and mortality rate of comatose patients.

Sleep in disorders of consciousness: behavioral and polysomnographic recording

BACKGROUND

Sleep-wakefulness cycles are an essential diagnostic criterion for disorders of consciousness (DOC), differentiating prolonged DOC from coma. Specific sleep features, like the presence of sleep spindles, are an important marker for the prognosis of recovery from DOC. Based on increasing evidence for a link between sleep and neuronal plasticity, understanding sleep in DOC might facilitate the development of novel methods for rehabilitation. Yet, well-controlled studies of sleep in DOC are lacking. Here, we aimed to quantify, on a reliable evaluation basis, the distribution of behavioral and neurophysiological sleep patterns in DOC over a 24-h period while controlling for environmental factors (by recruiting a group of conscious tetraplegic patients who resided in the same hospital).

METHODS

We evaluated the distribution of sleep and wakefulness by means of polysomnography (EEG, EOG, EMG) and video recordings in 32 DOC patients (16 unresponsive wakefulness syndrome [UWS], 16 minimally conscious state [MCS]), and 10 clinical control patients with severe tetraplegia. Three independent raters scored the patients' polysomnographic recordings.

RESULTS

All but one patient (UWS) showed behavioral and electrophysiological signs of sleep. Control and MCS patients spent significantly more time in sleep during the night than during daytime, a pattern that was not evident in UWS. DOC patients (particularly UWS) exhibited less REM sleep than control patients. Forty-four percent of UWS patients and 12% of MCS patients did not have any REM sleep, while all control patients (100%) showed signs of all sleep stages and sleep spindles. Furthermore, no sleep spindles were found in 62% of UWS patients and 21% of MCS patients. In the remaining DOC patients who had spindles, their number and amplitude were significantly lower than in controls.

CONCLUSIONS

The distribution of sleep signs in DOC over 24 h differs significantly from the normal sleep-wakefulness pattern. These abnormalities of sleep in DOC are independent of external factors such as severe immobility and hospital environment.