Active clinical trials for "Consciousness Disorders"

Recently introduced hybrid PET/MR scanners provide the opportunity to measure simultaneously, and in direct spatial correspondence, both metabolic demand and functional activity of the brain, hence capturing complementary information on the brain's physiological state. Here we exploited PET/MR simultaneous imaging to explore the relationship between the metabolic information provided by resting-state fluorodeoxyglucose-PET (FDG-PET) and fMRI (rs-fMRI) in patients with disorders of consciousness.

Precise description of behavioral signs denoting transition from unresponsive wakefulness syndrome/vegetative state (UWS/VS) to minimally conscious state (MCS) or emergence from MCS after severe brain injury is crucial for prognostic purposes. A few studies have attempted this goal but involved either non-standardized instruments, limited temporal accuracy or samples, or focused on (sub)acute patients. The objective of this study is to describe the behavioral signs that led to a change of diagnosis, as well as the factors influencing this transition, in a large sample of patients with chronic disorders of consciousness after severe brain injury.

Objective to explore the high risk factors and inducements of consciousness disorder in patients with hypoglycemia, and to conduct a retrospective study.

Background: The Coma Recovery Scale-Revised (CRS-R) is the most recommended instrument to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures. Objectives: This study investigates which information derived from the CRS-R provides the most reliable prediction of both the neurobehavioral state and recovery of consciousness at the discharge of a long-term neurorehabilitation program. Methods: The clinical records of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were used to implement machine learning classifiers that were trained to predict the neurobehavioral state and recovery of consciousness at discharge.

In this study, the core body temperature of 7 disorders of consciousness ( DOC ) patients were measured every 2 hour for 168 h .

Studies have shown that prolonged deep sedation is associated with adverse clinical outcomes in adult intensive care unit (ICU) patients. The revised guidelines for management of pain, agitation and delirium by the Society of Critical Care Medicine in 2013 also recommended that adult ICU patients should be maintained at a light level of sedation. The key point in light sedation strategy is the assessment of depth of sedation. At present, sedation is monitored mainly by the subjective clinical score systems, such as the Ramsay Scale, the Riker's Sedation-Agitation Scale (SAS) and the Richmond Agitation-Sedation Scale (RASS). However, the subjective and intermittent nature of these scales instruments has limited their application in light sedation algorithm. Consequently, objective and continuous measurement of the level of sedation would be more desirable in clinical practice. In recent years, objective measures of brain function have been of great interests in the evaluation of sedation level, and bispectral index (BIS) has been the most investigated instrument. Several studies compared BIS with subjective sedation scales in adult ICU patients, and yielded conflicting results. The different approaches to select BIS value may be the most important reason for these inconsistent agreements between BIS and subjective sedation scales. A formal scheme of subjective assessment of the depth of sedation and level of consciousness should incorporate exerting verbal and physical stimuli and observation of the patient's subsequent responses. Our primary aim is to clarify the diagnostic accuracy of BIS in detecting early deep sedation against the reference standard of subjective scale instrument. We hypothesize that BIS monitoring will provide accurate, subjective and continuous evaluation of deepen sedation.

Improved treatment of severe brain injuries has resulted in increased survival rates. While some of these patients regain consciousness after a transient state of coma, others may develop a disorder of consciousness (DoC). Diagnosis of DoC currently relies on standardized behavioral assessment. The importance of accuracy in such diagnosis cannot be overstated, as it guides critical decisions on treatment (including pain management), and could underlie end-of-life decisions. Despite this importance, current behavioral diagnosis often fails, if because of the major sensory and motor deficits associated with DoC, or because of the heterogeneous etiology and pathophysiology associated with the condition. Finally, the need for accurate diagnosis and prognosis transcends the needs of the patients alone: caregiving of these patients is very stressful, principally for the large uncertainty associated with them. Thus, more accurate diagnosis and prognosis provide major relief for caregivers, and paradoxically, even if the news is not "good". For all these reasons it is critical to developing personalized diagnosis and prognosis prediction tools that permit a stratified analysis at the single-patient level. The PerBrain Project will benefit from the multidisciplinary partners' expertise, and the unique opportunity to perform longitudinal assessments in four clinical sites through both established and novel electrophysiological, neuroimaging, and physiological techniques. Based on the collected data, the investigators will develop a multimodal personalized diagnostic tool for DoC patients using state-of-the-art computational tools, such as machine learning, in order to better determine the current state (diagnosis) and future outcome (prognosis). The overall aim of this project will provide for a better understanding of the pathophysiological mechanisms in DoC, which will, in turn, allow personalized rehabilitation strategies, and improved single-patient predictions of state and prognosis.

Following severe traumatic brain injury, patients may remain unconscious for many years. It is the intestinal microbiome and metabolomics analysis comparing differentially intestinal microflora and metabolites between patients with chronic disorder of consciousness and controls so far. The investigators have mature data analysis technology. The obtained results provide new insight into understanding the molecular mechanisms underlying the chronic disorder of consciousness

Detecting preserved consciousness in brain-injured patients by traditional clinical means requires presence of motor function. Otherwise, patients may be erroneously classified as being in a vegetative state. In order to circumvent the need for motor function, paradigms using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have been developed. According to a recent meta-analysis, 15% of patients with a clinical diagnosis of vegetative state can follow commands by performing mental imaginary tasks, strongly suggesting they are indeed conscious. This is of utmost importance for prognosis, treatment, and resource allocation. However, consciousness paradigms are usually employed in rehabilitation medicine. Therefore, opportunities to optimize patient outcome at an early stage may be lost. As a novel approach, the CONsciousness in NEurocritical Care cohorT study using fMRI and EEG (CONNECT-ME) will import the full range of consciousness paradigms into neurocritical care. The investigators aim to assess patients with acute brain injury for preserved consciousness by serial multimodal evaluations using active, passive and resting state fMRI- and EEG-based paradigms. A prospective longitudinal database and a biobank for genomic and metabolomic research will be established. This approach will add essential clinical information, including detection of preserved consciousness in patients previously thought of as unconscious. Due to its complexity, this project is divided into nine work packages. Eventually, the investigators will have established a clinical service for the systematic assessment of covert consciousness, as well as an interdisciplinary research group dedicated to the neuronal mechanisms by which consciousness recovers after acute brain injury.

Following severe traumatic brain injury, patients may remain unconscious for many years. It is the first proteomic analysis comparing differentially expressed proteins between patients with chronic disorder of consciousness and controls so far. The investigators generated accurate lists of proteomes and identified differences at different disease time courses. The obtained results provide new insight into understanding the molecular mechanisms underlying the chronic disorder of consciousness.