Ignite Creation Date:
2025-12-26 @ 11:11 AM
Ignite Modification Date:
2025-12-31 @ 9:10 PM
Study NCT ID:
NCT05949528
Status:
UNKNOWN
Last Update Posted:
2023-07-18
First Post:
2022-03-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003244', 'term': 'Consciousness Disorders'}], 'ancestors': [{'id': 'D019954', 'term': 'Neurobehavioral Manifestations'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D019965', 'term': 'Neurocognitive Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITHOUT_DNA', 'description': 'Electroencephalogram (EEG) refers to the resting state and event-related potential (ERP) of the brain.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 200}, 'targetDuration': '6 Months', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-12-12', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-10', 'completionDateStruct': {'date': '2024-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-07-14', 'studyFirstSubmitDate': '2022-03-14', 'studyFirstSubmitQcDate': '2023-07-14', 'lastUpdatePostDateStruct': {'date': '2023-07-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-07-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Spectrum analysis of chronic disorders of consciousness', 'timeFrame': '6 months', 'description': 'The EEG of 59 patients with disturbance of consciousness will be collected in resting state and listening to music, and the absolute power spectral density values (alpha,beta,theta,delta bands dB/Hz) will be calculated using spectral analysis.'}, {'measure': 'Duration of each microstate', 'timeFrame': '6 months', 'description': 'The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate method to accurately estimate topographical differences. The calculations were performed for measures of duration (ms). The duration of each microstate were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.'}, {'measure': 'Occurrence of each microstate', 'timeFrame': '6 months', 'description': 'The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of occurrence (times per minute). The occurrence of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.'}, {'measure': 'Global explained variance (GEV) of each microstate', 'timeFrame': '6 months', 'description': 'The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of GEV (%). The GEV of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness.'}], 'secondaryOutcomes': [{'measure': 'Coma Recovery Scale-Revised(CRS-R)', 'timeFrame': '30 minutes before samples collection', 'description': 'The Coma Recovery Scale-Revised (CRS-R) score was utilized to measure the severity of the condition. It comprises 23 items arranged hierarchically into six subscales, including auditory, visual, motor, oromotor/verbal, communication, and arousal processes. Reflexive activity is represented by the lowest item on each subscale, while cognitively mediated behaviors are portrayed by the highest items. The scale ranges from 0 (indicating the lowest level of consciousness) to 23 (indicating the highest level of consciousness). Generally, a higher score suggests a better level of consciousness, while a lower score suggests a lower level of consciousness.'}, {'measure': 'Glasgow Outcome Scale (GOS)', 'timeFrame': '6 months', 'description': 'A GOS score ≥ 4 points is considered to indicate a good prognosis, while a GOS score \\< 4 points is considered to indicate a poor prognosis'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['resting-state electroencephalograms', 'ERP', 'microstate'], 'conditions': ['Disorders of Consciousness']}, 'descriptionModule': {'briefSummary': 'Currently, there are significant challenges in the clinical assessment of patients with consciousness disorders, such as distinguishing between vegetative state (VS) and minimally conscious state (MCS), and predicting patient prognosis. This study aims to utilize different research techniques, such as auditory stimulation, as well as modified microstate methods, to enhance the disease classification and prognosis prediction of patients with chronic consciousness disorders.', 'detailedDescription': 'The investigators collected resting-state electroencephalograms (EEGs) and EEGs under various event-related potential (ERP) stimuli from patients with chronic consciousness disorders, and performed analyses on these data. The resting-state EEGs were subjected to spectral analysis and microstate analysis. The ERP EEGs were analyzed in the time domain, as well as for phase coupling and other measures.Using these computed indicators, the investigators use machine learning, deep learning, and other methods to predict disease classification and prognosis assessment in patients with chronic consciousness disorders.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '40 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with chronic disorder of consciousness were enrolled in the Hangzhou Mingzhou Brain Rehabilitation Hospital, and healthy controls were all recorded by the First Affiliated Hospital of Zhejiang University.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients diagnosed with COMA /VS/MCS\n2. The course of disease was more than 4 weeks\n3. The vital signs were stable and able to tolerate the test process\n4. Complete skull\n5. Right-handed, no history of ear disease or hearing loss before onset\n\nExclusion Criteria:\n\n1. History of epilepsy\n2. Sedatives\n3. Muscle relaxants and epileptic prophylaxis within 24 hours'}, 'identificationModule': {'nctId': 'NCT05949528', 'briefTitle': 'Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Zhejiang University'}, 'officialTitle': 'Assessing Chronic Consciousness Disorders in Patients Using Clinical Evaluation With Resting-state EEG and ERPs: an Extensive Study Exploring Efficacy and Diagnostic Potential of EEG and ERP Measurements', 'orgStudyIdInfo': {'id': 'EEG assessment'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Healthy controls (HCs)', 'description': 'Healthy controls (HCs)', 'interventionNames': ['Other: no intervention']}, {'label': 'Emerged from Minimally Conscious State (EMCS)', 'description': 'Emerged from Minimally Conscious State (EMCS): recovery of functional object uses or communication from chronic', 'interventionNames': ['Other: no intervention']}, {'label': 'Minimally conscious state (MCS)', 'description': 'Minimally conscious state (MCS): have reproducible signs of awareness and exhibit fluctuations in consciousness', 'interventionNames': ['Other: no intervention']}, {'label': 'Vegetative state (VS)', 'description': 'Vegetative state (VS): can open their eyes and preserve sleep-wake cycles, but unaware of themselves and their surroundings', 'interventionNames': ['Other: no intervention']}], 'interventions': [{'name': 'no intervention', 'type': 'OTHER', 'description': 'no intervention', 'armGroupLabels': ['Emerged from Minimally Conscious State (EMCS)', 'Healthy controls (HCs)', 'Minimally conscious state (MCS)', 'Vegetative state (VS)']}]}, 'contactsLocationsModule': {'locations': [{'zip': '310000', 'city': 'Hangzhou', 'state': 'Zhejiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Yi Ling', 'role': 'CONTACT', 'email': 'lywenzhoumc@163.com', 'phone': '15168236137'}], 'facility': 'Yi Ling', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}], 'centralContacts': [{'name': 'Yi Ling', 'role': 'CONTACT', 'email': 'lywenzhoumc@163.com', 'phone': '15168236137'}, {'name': 'Fangping He', 'role': 'CONTACT', 'email': 'lywenzhoumc@163.com', 'phone': '13819114225'}], 'overallOfficials': [{'name': 'Benyuan Luo', 'role': 'STUDY_CHAIR', 'affiliation': 'Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital of Zhejiang University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}