Clinical trial NCT05461144: AI Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics
Official title: Development and Validation of Artificial Intelligence Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Metabolic Disease
Keywords: Endochrine
Lead sponsor: University Hospitals Coventry and Warwickshire NHS Trust
Sponsor class: OTHER
Collaborators: name: University of Warwick
First posted: 2022-07-15
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The population will be recruited from the Warwickshire Institute for the study of diabetes, Endocrinology and Metabolism, at the University Hospitals Coventry and Warwickshire. WISDEM is a flagship partnership between the hospitals and the University of Warwick Medical School created to tackle diabetes and related metabolic conditions.

Brief summary: This observational study aims to recruit up to thirty T1DM patients from a diabetic outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to thirty-six hours in a calorimetry room at the Human Metabolism Research Unit under controlled conditions, followed by a phase of free-living, for up to three days, in which participants will go about their normal daily activities without restriction. Throughout the study, the participants will wear commercially available wearable sensors to measure and record physiological signals (e.g., electrocardiogram and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep-learning methods for the purpose of non-invasive glycaemic event detection.

Detailed description: The study volunteers will be asked to an attend an 'inpatient' facility for up to 36 hrs dedicated to advanced metabolic measurement (HMRU). They will be asked to consume prepared meals of varying macronutrient content as part of a balanced diet, and performed prescribed physical activity. During this time the volunteers will be measured by instrumentation which will investigate the chemical concentration in respired gases (e.g. whole-body calorimeters, metabolic carts); bloods, saliva and urine samples will be taken. If the participant then wishes, we will ask them to continue to wear the wearable devices in a home setting for a maximum one week.

The data derived from this study will allow new tools and mathematical models to be developed that can be used to analyse and simulate patient metabolic response. It is envisaged this study will give further evidence to support future research into glucose utilisation in diseased metabolic populations.

Eligibility criteria: Inclusion Criteria:

The study will be open to all individuals living independently, over 18 years without acute illness or ongoing clinical investigation, or volunteers with a stable medical condition may be included. Volunteers with an ongoing medical condition will only be included after detailed consultation with our clinical and dietetics members of the team; however, it is imperative that volunteers are able to provide written informed consent.

Exclusion Criteria:

Whilst the study employs a deliberately open inclusion criterion, the following exclusion measures will be employed:

* Children (under 18 yrs)
* Any adult who lacks decisional capacity
* Claustrophobia, isolophobia, recent abnormal exercise, radiation exposure within the preceding 24 hours of entering the whole-body calorimeter and feeling unwell in any way.
* Needle phobia
* Any medical/endocrine problem that could affect energy expenditure (e.g. thyroid problems, Cushing's syndrome)
* Chronic inflammatory disorders like rheumatoid arthritis, or long term use of steroids or other immunomodulators like cyclosporine, azathioprine.
* Beta blockers
* Currently actively losing weight
* Depression or any psychiatric illness

Primary outcomes:
measure: Interstitial Glucose
timeFrame: For the duration of the study, up to 5 days
Secondary outcomes:
measure: ECG -Interval across different fiducial points
timeFrame: For the duration of the study, up to 5 days
measure: ECG - Slope across different fiducial points
timeFrame: For the duration of the study, up to 5 days
measure: ECG - Indices of Heart Rate Variability
timeFrame: For the duration of the study, up to 5 days
measure: Blood Pressure (Systolic and Diastolic)
timeFrame: For the duration of the study, up to 5 days
Other outcomes:
Locations:
Study officials:
References:
citation: Porumb M, Stranges S, Pescape A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep. 2020 Jan 13;10(1):170. doi: 10.1038/s41598-019-56927-5.
citation: Porumb M, Griffen C, Hattersley J, Pecchia L. Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders. Biomedical Signal Processing and Control. 2020;62:102054.