GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography (GLEAM)
GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography - a Pilot Project
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Vera Lehmann, MD PhD
- Phone Number: +41 31 632 40 70
- Email: vera.lehmann@insel.ch
Study Locations
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-
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Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Written, informed consent
- Type 1 Diabetes mellitus as defined by WHO for at least 6 months
- Aged 18 - 60 years
- HbA1c ≤ 9.0 %
- Insulin treatment with good knowledge of insulin self-management
- Use of a continuous (CGM) or flash glucose monitoring system (FGM)
- Native language German or Swiss German
Exclusion Criteria:
- Incapacity to give informed consent
- Contraindications to insulin aspart (NovoRapid®)
- Known allergies to adhesives of the EIT device (e.g., gel electrodes)
- Pregnancy, breast-feeding or lack of safe contraception
- Active heart, lung, liver, gastrointestinal, renal or psychiatric disease
- Patients with implantable electronic devices (e.g., pacemaker or implantable cardioverter defibrillator (ICD)) or thoracic metal implants
- Epilepsy or history of seizure
- Active drug or alcohol abuse
- Chronic neurological or ear-nose-and-throat (ENT) disease influencing voice or history of voice disorder
- Thoracic or back deformities
- Body mass index (BMI) >35.0 kg/m2
- Open wounds, burns, or rashes on the upper thorax
- Active smoking
- Medication known to interfere with voice or to induce listlessness (e.g., opioids, benzodiazepines, etc.)
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Other
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Other: Controlled euglycemia, hypoglycemia and hyperglycemia
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EIT measurements are collected in different glycemic states (euglycemia, hypoglycemia and hyperglycemia).
Venous blood glucose is measured using a gold-standard glucose analyzer.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.
Time Frame: 5 hours
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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.
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5 hours
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Change of hypoglycemia symptoms across the glycemic trajectory.
Time Frame: 5 hours
|
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).
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5 hours
|
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Voice parameters indicative of dysglycemia
Time Frame: 5 hours
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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.
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5 hours
|
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Change in cognitive performance across the glycemic trajectory.
Time Frame: 5 hours
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Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance).
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5 hours
|
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Change in cognitive performance across the glycemic trajectory.
Time Frame: 5 hours
|
Cognitive performance will be assessed using the Digit Symbol Substitution Test (higher score means better cognitive performance).
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5 hours
|
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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).
Time Frame: 5 hours
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Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
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Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.
Time Frame: 5 hours
|
Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
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Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.
Time Frame: 5 hours
|
Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
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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).
Time Frame: 5 hours
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Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
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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).
Time Frame: 5 hours
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Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
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Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.
Time Frame: 5 hours
|
Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
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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.
Time Frame: 5 hours
|
Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia.
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5 hours
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Christoph Stettler, Prof. MD, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism; Bern, Switzerland
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- GLEAM
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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