View clinical trials related to Consciousness Disorder.
Filter by:Identify potential markers of recovery through event-related potentials (ERPs) in the evolution of altered consciousness with potential ability to discriminate between different conditions of altered state of consciousness. Identify potential markers with prognostic value, in order to identify personalized and specific intensive rehabilitation paths based on the outcome indexes obtained.
BACKGROUND: Transient loss of consciousness (TLOC) - defined as spontaneous disruption of consciousness not due to head trauma and with complete recovery - has a lifetime prevalence of 50%. It is one of the commonest neurological complaints in primary and emergency care. Over 90% of TLOC is due to either syncope, epilepsy or dissociative seizures (DS, also known as 'Psychogenic Nonepileptic Seizures'). The rapid and accurate distinction of these diagnoses is vital to allow appropriate further management but at least 20-30% of patients are not managed optimally or misdiagnosed. We have previously demonstrated that, in patients with established diagnoses of epilepsy, syncope, or DS, an automated classifier using only information from 36 questions based on patient experience and lay witness reports (the initial Paroxysmal Event Profile, iPEP) could accurately diagnose 86.0% of patients (with 100% sensitivity and 91.7% specificity for syncope) AIMS: To calibrate the iPEP for discrimination between syncope, epilepsy, and DS in patients newly presenting with TLOC, validate its performance in an independent sample, and to explore acceptability of the use of such a tool to people with TLOC and witnesses. METHODS: Nested qualitative-quantitative prospective single-centre development and validation of the iPEP in patients presenting to Emergency Departments, syncope or epilepsy clinics with first presentations of TLOC, with semi-structured interviews conducted with a purposive sample of participants from the quantitative study. The iPEP will be calibrated using a previously-described procedure for variable selection and training of Random Forest (RF) classifiers, and validated with assessment of overall classification accuracy, alongside sensitivity, specificity, positive and negative predictive values, and area under receiver-operating curve for each of the three target diagnoses. Performance will be evaluated against a benchmark set by results from previous research in patients with established diagnoses of epilepsy, syncope, and DS. OUTPUTS: Results will be submitted for publication in academic and professional literature. If performance from feasibility can be replicated in validation, the iPEP will be suitable to begin process of registration as a medical device for implementation in clinical pathways to minimise inappropriate referrals and treatment, streamline patient pathways, and enable earlier ordering of appropriate investigations to ensure prompt and appropriate diagnosis and management. If pilot performance could be replicated in this population and proportional savings from current estimated costs of misdiagnosis achieved, this could potentially save £63.9 million of annual UK healthcare expenditure.
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.
The primary aim of this research proposal is to use multimodal metrics (e.g., clinical data and advanced neuroimaging) in the early (i.e., acute hospitalization) phase of recovery from COVID-19-related disorders of consciousness to predict outcome at 3, 6, and 12 months post-hospitalization. We aim to construct an algorithm that synthesizes the results of these metrics to help predict recovery.
Altered gamma activity has been observed in several neurological and psychiatric disorders, including a reduction in gamma synchronization in patients with disorders of consciousness. Modulation of gamma oscillations with rhythmic stimulation has been used as a possible therapeutic tool. Hence, we try to use acoustic and electric stimulation at gamma frequency to restore brain oscillation and thereby to improve conscious awareness in patients with disorders of consciousness.
The aim of this study is to assess the utility of advanced magnetic resonance imaging (MRI) and electroencephalographic (EEG) technologies for predicting functional outcomes in patients with severe traumatic brain injury (TBI).
There is practical evidence but no scientific investigations that persons with severe disorders of consciousness can profit from animal-assisted therapy regarding their level of awareness. The aim of this study is to investigate the effect of animal-assisted therapy on brain activity of inpatients at REHAB Basel with severe disorders of consciousness. To do so, the frontal brain activity of 20 the inpatients at REHAB Basel in a minimally conscious state is investigated via near-infrared spectroscopy (NIRS). Moreover, 20 healthy participants are included as control subjects.