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| Name | Class |
|---|---|
| CSEM Centre Suisse d'Electronique et de Microtechnique SA | UNKNOWN |
| Idiap Research Institute | UNKNOWN |
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The GLEAM study aims at assessing the potential of electrical impedance tomography (EIT) for noninvasive glucose measurement.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Controlled euglycemia, hypoglycemia and hyperglycemia | Other |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Controlled euglycemia, hypoglycemia and hyperglycemia | Other | EIT measurements are collected in different glycemic states (euglycemia, hypoglycemia and hyperglycemia). Venous blood glucose is measured using a gold-standard glucose analyzer. |
| Measure | Description | Time Frame |
|---|---|---|
| Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory. | 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. | 5 hours |
| Measure | Description | Time Frame |
|---|---|---|
| Change of hypoglycemia symptoms across the glycemic trajectory. | 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). | 5 hours |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Christoph Stettler, Prof. MD | Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism; Bern, Switzerland | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism | Bern | Switzerland |
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| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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| Voice parameters indicative of dysglycemia |
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. |
| 5 hours |
| Change in cognitive performance across the glycemic trajectory. | Cognitive performance will be assessed using the Trail Making B Test (more time needed to complete the tests means worse cognitive performance). | 5 hours |
| Change in cognitive performance across the glycemic trajectory. | Cognitive performance will be assessed using the Digit Symbol Substitution Test (higher score means better cognitive performance). | 5 hours |
| 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). | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity. | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity. | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| 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). | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| 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). | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots. | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |
| 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. | Signals for machine learning modeling will be collected in euglycemia, hypoglycemia and hyperglycemia. | 5 hours |