Ignite Creation Date:
2025-12-24 @ 7:55 PM
Ignite Modification Date:
2025-12-26 @ 6:39 PM
Study NCT ID:
NCT06223204
Status:
COMPLETED
Last Update Posted:
2024-05-02
First Post:
2023-12-19
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}], 'ancestors': [{'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'OTHER', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 16}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-01-31', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2024-04-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-04-30', 'studyFirstSubmitDate': '2023-12-19', 'studyFirstSubmitQcDate': '2024-01-15', 'lastUpdatePostDateStruct': {'date': '2024-05-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-01-25', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-04-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.', 'timeFrame': '5 hours', 'description': 'EIT signals will be collected at multiple frequencies between 50 kHz and 1 MHz from the thoracic region in euglycemia, hypoglycemia and hyperglycemia using a multi-channel EIT measurement device.'}], 'secondaryOutcomes': [{'measure': 'Change of hypoglycemia symptoms across the glycemic trajectory.', 'timeFrame': '5 hours', 'description': 'Hypoglycemia symptoms will be collected in euglycemia, hypoglycemia and hyperglycemia using a standardized questionnaire (Edinburgh Hypoglycemia Scale, a higher score means more symptoms, minimum score 7 points, maximum score 77 points).'}, {'measure': 'Voice parameters indicative of dysglycemia', 'timeFrame': '5 hours', 'description': 'Voice data will be collected using a microphone in euglycemia, hypoglycemia and hyperglycemia. After sampling, an interpretable machine learning (ML) method will be used to identify voice parameters indicative of dysglycemia.'}, {'measure': 'Change in cognitive performance across the glycemic trajectory.', 'timeFrame': '5 hours', 'description': 'Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance).'}, {'measure': 'Change in cognitive performance across the glycemic trajectory.', 'timeFrame': '5 hours', 'description': 'Cognitive performance will be assessed using the Digit Symbol Substitution Test (higher score means better cognitive performance).'}, {'measure': 'Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as area under the receiver operating characteristics curve (AUROC).', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as root mean squared error (RMSE).', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as mean absolute relative difference (MARD).', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}, {'measure': 'Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using the Clarke Error Grid.', 'timeFrame': '5 hours', 'description': 'Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Diabetes Mellitus']}, 'descriptionModule': {'briefSummary': 'The GLEAM study aims at assessing the potential of electrical impedance tomography (EIT) for noninvasive glucose measurement.', 'detailedDescription': 'Within the GLEAM study, paired samples of EIT and blood glucose measurements will be collected in individuals with type 1 diabetes during standardized euglycemia, hypoglycemia and hyperglycemia. These samples will be used to assess the potential of EIT for noninvasive glucose measurement and/or dysglycemia detection.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '60 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Written, informed consent\n* Type 1 Diabetes mellitus as defined by WHO for at least 6 months\n* Aged 18 - 60 years\n* HbA1c ≤ 9.0 %\n* Insulin treatment with good knowledge of insulin self-management\n* Use of a continuous (CGM) or flash glucose monitoring system (FGM)\n* Native language German or Swiss German\n\nExclusion Criteria:\n\n* Incapacity to give informed consent\n* Contraindications to insulin aspart (NovoRapid®)\n* Known allergies to adhesives of the EIT device (e.g., gel electrodes)\n* Pregnancy, breast-feeding or lack of safe contraception\n* Active heart, lung, liver, gastrointestinal, renal or psychiatric disease\n* Patients with implantable electronic devices (e.g., pacemaker or implantable cardioverter defibrillator (ICD)) or thoracic metal implants\n* Epilepsy or history of seizure\n* Active drug or alcohol abuse\n* Chronic neurological or ear-nose-and-throat (ENT) disease influencing voice or history of voice disorder\n* Thoracic or back deformities\n* Body mass index (BMI) \\>35.0 kg/m2\n* Open wounds, burns, or rashes on the upper thorax\n* Active smoking\n* Medication known to interfere with voice or to induce listlessness (e.g., opioids, benzodiazepines, etc.)'}, 'identificationModule': {'nctId': 'NCT06223204', 'acronym': 'GLEAM', 'briefTitle': 'GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography', 'organization': {'class': 'OTHER', 'fullName': 'Insel Gruppe AG, University Hospital Bern'}, 'officialTitle': 'GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography - a Pilot Project', 'orgStudyIdInfo': {'id': 'GLEAM'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'Controlled euglycemia, hypoglycemia and hyperglycemia', 'interventionNames': ['Other: Controlled euglycemia, hypoglycemia and hyperglycemia']}], 'interventions': [{'name': 'Controlled euglycemia, hypoglycemia and hyperglycemia', 'type': 'OTHER', 'description': 'EIT measurements are collected in different glycemic states (euglycemia, hypoglycemia and hyperglycemia). Venous blood glucose is measured using a gold-standard glucose analyzer.', 'armGroupLabels': ['Controlled euglycemia, hypoglycemia and hyperglycemia']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Bern', 'country': 'Switzerland', 'facility': 'Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism', 'geoPoint': {'lat': 46.94809, 'lon': 7.44744}}], 'overallOfficials': [{'name': 'Christoph Stettler, Prof. MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism; Bern, Switzerland'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Insel Gruppe AG, University Hospital Bern', 'class': 'OTHER'}, 'collaborators': [{'name': "CSEM Centre Suisse d'Electronique et de Microtechnique SA", 'class': 'UNKNOWN'}, {'name': 'Idiap Research Institute', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}