Nctid:
NCT06223204
Payload:
{"hasResults"=>false, "derivedSection"=>{"miscInfoModule"=>{"versionHolder"=>"2024-10-04"}, "conditionBrowseModule"=>{"meshes"=>[{"id"=>"D000003920", "term"=>"Diabetes Mellitus"}], "ancestors"=>[{"id"=>"D000044882", "term"=>"Glucose Metabolism Disorders"}, {"id"=>"D000008659", "term"=>"Metabolic Diseases"}, {"id"=>"D000004700", "term"=>"Endocrine System Diseases"}], "browseLeaves"=>[{"id"=>"M7115", "name"=>"Diabetes Mellitus", "asFound"=>"Diabetes Mellitus", "relevance"=>"HIGH"}, {"id"=>"M11639", "name"=>"Metabolic Diseases", "relevance"=>"LOW"}, {"id"=>"M25403", "name"=>"Glucose Metabolism Disorders", "relevance"=>"LOW"}, {"id"=>"M7862", "name"=>"Endocrine System Diseases", "relevance"=>"LOW"}], "browseBranches"=>[{"name"=>"Nutritional and Metabolic Diseases", "abbrev"=>"BC18"}, {"name"=>"Gland and Hormone Related Diseases", "abbrev"=>"BC19"}, {"name"=>"All Conditions", "abbrev"=>"All"}]}}, "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"}}}}
