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| ID | Type | Description | Link |
|---|---|---|---|
| 2021-AO2599-32 | Other Identifier | Centre intercommunal Toulon La seine sur Mer |
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| Name | Class |
|---|---|
| Centre Hospitalier Intercommunal de Toulon La Seyne sur Mer | OTHER |
| Assistance Publique Hopitaux De Marseille | OTHER |
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The aim of this study is to demonstrate the efficacy of an algorithm to anticipate the post prandial glycemic profile in type I diabetic patient.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Measure of glycemia levels and comparison to algorithm predictions | Other | The study aims to evaluate the efficacy of a new algorithm in predicting the evolution of glycemia levels after a full meal. Glycemia levels measured during and after a full meal will be compared to the values predicted by the algorithm. Composition of the meals will also be collected. |
| Measure | Description | Time Frame |
|---|---|---|
| Algorithm efficacy | The Algorithm efficacy in predicting the risk or absence of risk of hyperglycemia two hours after taking a full meal is evaluated by comparing glycemic values calculated by the algorithm and those obtained using continuous glucose monitoring system measurements. | 15 days |
| Measure | Description | Time Frame |
|---|---|---|
| Algorithm efficacy (lower margin of error) | The Algorithm efficacy in predicting the risk or absence of risk of hyperglycemia two hours after taking a full meal is evaluated by comparing glycemic values calculated by the algorithm and those obtained using continuous glucose monitoring system measurements (the accepted margin of error is reduced by 25 to 50% when compared with primary outcome). | 15 days |
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Inclusion Criteria:
Exclusion Criteria:
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Type I diabetes
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| APHM | Marseille | France | ||||
| Hopital Sainte Musse |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| D006943 | Hyperglycemia |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| Algorithm efficacy versus meal composition | Meals will be analyzed within different groups according to their respective nutritional index. For each group, algorithm prediction reliability will be determined. | 15 days |
| Influence of the age of the patients on algorithm results | The Algorithm efficacy will be analyzed according to the age of patients | 15 days |
| Influence of the BMI on algorithms results | Weight and height of patients will be combined to report BMI in kg/m2. Algorithm efficacy will be analyzed according to the BMI of patients | 15 days |
| Influence of the insulin administration on algorithm results | Data obtained from patients using insulin pumps will be compared to data obtained from patients using multiple daily insulin injections. | 15 days |
| Toulon |
| France |
| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |