Diabete Mellitus, Medical Device
Conditions
Keywords
Non-invasive glucose monitoring, Raman spectroscopy
Brief summary
The study was established to collect data and reference measurements in order to establish calibration models for the Prototype 0.3
Detailed description
Subjects are recruited at two different sites. Site 1: In group 1 subjects will on their regular stay in the clinic perform four measurement sessions a day. A measurement session consist of a reference capillary blood sample and two measures on the IMD. Site 2: The study in group 2 consists of 26 home-based measurements and two in-clinic days. During the home measurements, 6 measurement sessions will be performed by the subjects a day. A measurement session consists of two reference BGMs, two reference CGMs and two measurements on the device. On in-clinic visits, subjects will be administered high glucose breakfast and the following 6-7 hours, measurement sessions are performed every 15 minutes.
Interventions
DEVICEPrototype 0.3
Prototype 0.3 is a non-invasive glucose monitoring device using Raman spectroscopy
Sponsors
RSP Systems A/S
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* At least 18 years of age * Have a diagnosis of type 1 or type 2 diabetes mellitus (for RSP-09-02, only patients with type 1 diabetes) * Skin phototype 1-4 * Be willing to perform a minimum of 6 / 12 (31 during excursion days) finger sticks per day during the study * Be able to follow the instructions provided to him/her by the study site and perform all study tasks as specified by the protocol * Be willing to provide written signed and dated informed consent
Exclusion criteria
* Inability to comply with the study procedures as described by the study protocol, according to the opinion of the investigator, due to e.g. known psychiatric diagnoses, lack of cognitive ability, alcohol dependency, drug use, psychosocial overload * Have known severe allergy to medical grade adhesive or isopropyl alcohol used to clean the skin * Be breastfeeding, pregnant, attempting to conceive or not willing and able to practice birth control during the study execution (applicable to female subjects only) * Unable to hold hand/arm steadily (including tremors and Parkinson's Disease) * Severe diabetes related complications such as advanced autonomic neuropathy, kidney disease, foot ulcers, legal blindness, or symptomatic cardiovascular disease as evidenced by a history of cardiovascular episode(s) * Systemic or topical administration of glucocorticoids for the past 7 days * Undergoing dialysis treatment * Have extensive skin changes/diseases at the proposed measurement site (thenar) that could interfere with the accuracy of interstitial glucose measurements * Have a concomitant medical condition which could interfere with the study devices (study arm 1: intake of acetaminophen study arm 2: intake of salicylic acid or higher doses of ascorbic acid) * Unsuitable for participation due to any other cause as determined by the Investigator. Mental incapacity, unwillingness or language barriers precluding adequate understanding or co-operation e.g. with required documentation * Dependency from the sponsor or the clinical investigator (e.g. co-workers of the sponsor, the study site, and/ or their families) Additional
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Generation and validation of predictive models by Inter Subject Unified Performance (ISUP). | 2 years | Spectral Raman data will be collected together with paired reference measurements. Collected data will be used to generate calibration models capable of predicting tissue glucose levels. Models will be validated on independent data sets using the ISUP measure. |
| Generation and validation of predictive models by Mean Absolute Relative Difference (MARD) measures | 2 years | Spectral Raman data will be collected together with paired reference measurements. Collected data will be used to generate calibration models capable of predicting tissue glucose levels. Models will be validated on independent data sets using the MARD measure. |
Countries
Germany
Outcome results
None listed