Ignite Creation Date:
2026-03-26 @ 3:20 PM
Ignite Modification Date:
2026-03-30 @ 12:13 AM
Study NCT ID:
NCT07410702
Status:
RECRUITING
Last Update Posted:
2026-03-24
First Post:
2026-02-08
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Correlating EEG Dynamics With Consciousness Alteration Under Anesthesia
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'interventionBrowseModule': {'meshes': [{'id': 'D000777', 'term': 'Anesthetics'}], 'ancestors': [{'id': 'D002492', 'term': 'Central Nervous System Depressants'}, {'id': 'D045505', 'term': 'Physiological Effects of Drugs'}, {'id': 'D020228', 'term': 'Pharmacologic Actions'}, {'id': 'D020164', 'term': 'Chemical Actions and Uses'}, {'id': 'D002491', 'term': 'Central Nervous System Agents'}, {'id': 'D045506', 'term': 'Therapeutic Uses'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 250}, 'targetDuration': '1 Day', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2026-02-10', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2027-02-20', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-22', 'studyFirstSubmitDate': '2026-02-08', 'studyFirstSubmitQcDate': '2026-02-08', 'lastUpdatePostDateStruct': {'date': '2026-03-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-10-20', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Identification of EEG Biomarkers for Sedation, Adequate Anesthesia, and Deep Anesthesia During Induction', 'timeFrame': 'From 2026.02.20 to 2026.12.31', 'description': 'Identification of two or more EEG biomarkers associated with three distinct levels of consciousness (sedation, adequate anesthesia depth, and deep anesthesia) during the induction of general anesthesia.'}, {'measure': 'Identification of Common EEG Biomarkers of Consciousness Across Anesthetic Agents', 'timeFrame': 'From 2026.02.20 to 2026.12.31', 'description': 'dentification of common EEG biomarkers of consciousness that are consistent across two or more anesthetic agents with differing pharmacological mechanisms.'}], 'secondaryOutcomes': [{'measure': 'Classification Performance of Novel EEG Biomarkers Compared to BIS for Assessing Anesthesia Depth', 'timeFrame': 'From 2027.01.01 to 2027.10.31', 'description': '1\\. Comparison of the precision (hit rate) in classifying the three consciousness states sedation, adequate anesthesia depth, and deep anesthesia) between the newly identified EEG biomarkers and the Bispectral Index (BIS) for one or more anesthetic agents.'}, {'measure': 'Development and Validation of a Mechine learning Model', 'timeFrame': 'From 2027.01.01 to 2027.10.31', 'description': 'Development and validation of a tree-based ensemble machine learning model using 70% of the dataset for training and 30% for testing, including evaluation of its accuracy and the area under the receiver operating characteristic curve (AUC-ROC)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['General Anesthetics', 'Electroencephalography', 'Neural correlates of consciousness', 'Consciousness monitoring'], 'conditions': ['Altered State of Consciousness', 'General Anesthetics']}, 'referencesModule': {'references': [{'pmid': '38157438', 'type': 'RESULT', 'citation': 'Liang Z, Tang B, Chang Y, Wang J, Li D, Li X, Wei C. State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness. Anesthesiology. 2024 May 1;140(5):935-949. doi: 10.1097/ALN.0000000000004896.'}]}, 'descriptionModule': {'briefSummary': 'This prospective observational study is designed to investigate and compare the dynamic features of whole-brain electroencephalogram (EEG) during the induction of unconsciousness using various anesthetic agents with distinct pharmacological mechanisms. The primary objective is to identify common, drug-agnostic EEG biomarkers of anesthetic depth and to develop a novel, universal assessment system that addresses the limitations of the currently prevalent Bispectral Index (BIS), which demonstrates variable sensitivity across different anesthetics.\n\nApproximately 250 adult patients (ASA I-II) scheduled for elective surgery under general anesthesia will be enrolled. Patients will undergo preoperative cognitive assessment prior to induction. During anesthesia induction, 32-channel EEG signals will be continuously recorded alongside BIS values and behavioral state assessments using the MOAA/S scale as the reference standard.\n\nPatients will receive one of the following intravenous anesthetics for induction: Propofol, Ciprofol, Remimazolam, Esketamine, or Fospropofol. Features will be extracted from the preprocessed EEG data. Statistical analyses will compare these features across drug groups and in relation to behavioral state transitions. Machine learning models (e.g., Random Forest) will then be trained to classify states of consciousness based on the extracted EEG features, with model performance validated against the behavioral gold standard.\n\nThe study aims to establish a more robust and generalizable neurophysiological framework for monitoring anesthetic depth, potentially improving the precision and safety of clinical anesthesia management.', 'detailedDescription': "The aim of the study is to identify and validate common whole-brain EEG biomarkers that accurately track the transition between conscious states (wakefulness, sedation, unconsciousness) across five intravenous anesthetics with distinct mechanisms of action: Propofol, Ciprofol, Remimazolam, Esketamine, and Fospropofol.\n\nDesign:\n\nThis is a single-center, prospective, observational cohort study. Consecutive eligible patients will be enrolled and grouped based on the clinical choice of anesthetic drug used for induction of general anesthesia. Data analysis will be performed by researchers blinded to the group allocation during the feature extraction and model development phases.\n\nApproximately 250 adult patients (aged ≥18 years) scheduled for elective surgery under general anesthesia at Tongji Hospital, Wuhan, China, will be recruited between April 2026 and December 2027. Participants must have an ASA physical status of I or II, normal cognitive function (MMSE score ≥24), and provide written informed consent.\n\nInterventions and Procedures:\n\nAll procedures represent standard clinical care; no experimental interventions are administered.\n\n1. Preoperative Assessment: Demographics, medical history, and MMSE score will be recorded.\n2. EEG and Behavioral Data Acquisition: During anesthesia induction, 32-channel EEG will be continuously recorded using a Greentek system with electrodes placed according to the international 10-20 system. Simultaneously, the BIS value (sensor placed infraorbitally)\\[referrence\\] and the patient's behavioral state will be recorded every 30 seconds using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) scale as the reference standard and the absence of the pupillary light reflex. Based on these behavioral responses, the depth of anesthesia will be categorized into three stages: sedation, adequate anesthesia depth, and deep anesthesia.\n3. Anesthetic Protocol: Induction of anesthesia will be performed by the attending anesthesiologist in accordance with standard institutional practice. One of the five study drugs will be administered as an intravenous bolus, with the induction dose maintained via continuous infusion for 5 minutes.\n\nData Processing and Analysis\n\n1. Data Curation: Data will be checked for quality, and epochs with artifacts or missing clinical data will be excluded.\n2. EEG Feature Extraction: Pre-processed EEG data will be analyzed to extract features including but not limited to power spectral density, permutation entropy, phase-lag entropy, and functional connectivity metrics.\n3. Data Analysis: During the induction of unconsciousness with five distinct general anesthetic agents, EEG biomarkers corresponding to transitions between three behavioral states-sedation, adequate anesthesia depth, and deep anesthesia-will be identified. The performance of these biomarkers in tracking depth of anesthesia will be quantitatively compared against that of the Bispectral Index (BIS).\n4. Machine Learning Modeling: The dataset will be split into training (70%) and validation (30%) sets. A tree-based ensemble model (e.g., Random Forest) will be trained to classify consciousness states based on EEG features. Model performance will be evaluated using AUC, accuracy, precision, and cross-validation."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients scheduled for elective surgery requiring general anesthesia', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ≥ 18 years.\n2. Scheduled to undergo elective surgery requiring general anesthesia with endotracheal intubation or laryngeal mask airway (LMA).\n3. American Society of Anesthesiologists (ASA) Physical Status Class I to III.\n4. Preoperative Mini-Mental State Examination (MMSE) score ≥ 24, indicating normal cognitive function.\n5. Body mass index (BMI) ≤ 30 kg/m².\n6. Ability to understand the study and provide written informed consent.\n\nExclusion Criteria:\n\n1. History of drug abuse or dependence.\n2. Known major neurological disorders (e.g., epilepsy, stroke, neurodegenerative diseases).\n3. History of major psychiatric disorders.\n4. Known or suspected pregnancy.\n5. Inability to provide informed consent due to cognitive impairment, language barrier, or any other reason.'}, 'identificationModule': {'nctId': 'NCT07410702', 'briefTitle': 'Correlating EEG Dynamics With Consciousness Alteration Under Anesthesia', 'organization': {'class': 'OTHER', 'fullName': 'Huazhong University of Science and Technology'}, 'officialTitle': 'EEG Signatures of Unconsciousness Induced by Anesthetic Agents and Their Dynamics Across the Consciousness Continuum', 'orgStudyIdInfo': {'id': 'TJ-IRB202601004'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'General Anesthesia', 'description': 'Adult patients requiring general anesthesia for surgical procedures', 'interventionNames': ['Drug: Anesthetic Agents']}], 'interventions': [{'name': 'Anesthetic Agents', 'type': 'DRUG', 'description': 'Loss of consciousness was induced in humans using five distinct general anesthetic agents: propofol, ciprofol, remimazolam, esketamine, and fospropofol', 'armGroupLabels': ['General Anesthesia']}]}, 'contactsLocationsModule': {'locations': [{'zip': '430030', 'city': 'Wuhan', 'state': 'Hubei', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Hua Zheng, Dr.', 'role': 'CONTACT', 'email': 'hzheng@hust.edu.cn', 'phone': '0086-27-83663173'}], 'facility': 'Department of Anaesthesiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}], 'centralContacts': [{'name': 'Hua Zheng, MD. & PhD.', 'role': 'CONTACT', 'email': 'hzheng@hust.edu.cn', 'phone': '0086-27-83663173'}], 'overallOfficials': [{'name': 'Pu Zhou, PhD.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Huazhong University of Science and Technology'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Huazhong University of Science and Technology', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate professor', 'investigatorFullName': 'Hua Zheng', 'investigatorAffiliation': 'Huazhong University of Science and Technology'}}}}