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

NCT05461144 | Not Yet Recruiting

• 1 other identifier: JH206817a
• Study Type: observational
• Target: 30
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Metabolic Disease

Keywords

Endochrine

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• ECG -Interval across different fiducial points

  For the duration of the study, up to 5 days

• ECG - Slope across different fiducial points

  For the duration of the study, up to 5 days

• ECG - Indices of Heart Rate Variability

  For the duration of the study, up to 5 days

• Blood Pressure (Systolic and Diastolic)

  For the duration of the study, up to 5 days

Study Arms (1)

Type1diabetes patients

Males and females diagnosed with T1D, aged over 18 years old who are currently under the care of the Warwickshire Institute for the Study of Diabetes, Endocrinolgy and Metabolism (WISDEM) at the University Hospitals Coventry and Warwickshire.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• University Hospitals Coventry and Warwickshire NHS Trust: lead
• University of Warwick: collaborator

Related Publications (3)

• 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.

  PMID: 31932608 BACKGROUND

• 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.

  BACKGROUND

• Cisuelo O, Stokes K, Oronti IB, Haleem MS, Barber TM, Weickert MO, Pecchia L, Hattersley J. Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions. BMJ Open. 2023 Apr 18;13(4):e067899. doi: 10.1136/bmjopen-2022-067899.

  PMID: 37072364 DERIVED

Biospecimen

Retention: SAMPLES WITHOUT DNA

Saliva samples for circadian biomarkers (cortisol, melatonin) Plasma for endocrine markers (insulin and glucose)

MeSH Terms

Conditions

Metabolic Diseases

Condition Hierarchy (Ancestors)

Nutritional and Metabolic Diseases

Central Study Contacts

John G Hattersley, PhD

CONTACT

+44 (0) 24 7696 6068; john.hattersley@uhcw.nhs.uk

Leandro Pechhia, PhD

CONTACT

+44 (0) 24 7657 3383; L.Pecchia@warwick.ac.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 24, 2022
• First Posted: July 15, 2022
• Study Start: September 30, 2022
• Primary Completion: May 1, 2026
• Study Completion (Estimated): May 1, 2027
• Last Updated: July 15, 2022

Record last verified: 2022-07

Data Sharing

• IPD Sharing: Will not share