AI Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics

Metabolic Disease Clinical Trial

Official title:

Development and Validation of Artificial Intelligence Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05461144
• Other study ID #: JH206817a
• Status: Not yet recruiting
• Start date: September 30, 2022
• Est. completion date: May 1, 2027

Study information

• Verified date: July 2022
• Source: University Hospitals Coventry and Warwickshire NHS Trust
• Contact: John G Hattersley, PhD
• Phone: +44 (0) 24 7696 6068
• Email: john.hattersley[@]uhcw.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 30
• Est. completion date: May 1, 2027
• Est. primary completion date: May 1, 2026
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Metabolic Disease
• Metabolic Diseases

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
University Hospitals Coventry and Warwickshire NHS Trust	University of Warwick

References & Publications (2)

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.

Porumb M, Stranges S, Pescapè 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Interstitial Glucose	As measured by a continuous glucose monitor [NOTE] Observational study thus a key measurement not a true outcome measure.	For the duration of the study, up to 5 days
Secondary	ECG -Interval across different fiducial points	As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure.; The interval across different fiducial points (P.Q.R,S,T) is one of the features that are useful to quantify the difference in ECG signals for different glycaemic events.	For the duration of the study, up to 5 days
Secondary	ECG - Slope across different fiducial points	As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure.; The Slope across different fiducial points (P.Q.R,S,T) is one of the features that are useful to quantify the difference in ECG signals for different glycaemic events.	For the duration of the study, up to 5 days
Secondary	ECG - Indices of Heart Rate Variability	As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure.; Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. There are several indices that are useful to quantify the difference in ECG signals for different glycaemic events such as Ultra Low Frequency (ULF) (=0.003 Hz), Very Low Frequency (VLF) (0.0033-0.04 Hz), Low Frequency (LF) (0.04-0.15 Hz) and High Frequency (HF) (0.15-0.4 Hz)	For the duration of the study, up to 5 days
Secondary	Blood Pressure (Systolic and Diastolic)	As measured by an ambulatory blood pressure device [NOTE] Observational study thus a key measurement not a true outcome measure.	For the duration of the study, up to 5 days