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

Официальное название

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

Обзор

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.

Подробное описание

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.

Первичные конечные точки

• Interstitial Glucose [Срок оценки: For the duration of the study, up to 5 days]

Вторичные конечные точки (4)

• ECG -Interval across different fiducial points [Срок оценки: For the duration of the study, up to 5 days]
• ECG - Slope across different fiducial points [Срок оценки: For the duration of the study, up to 5 days]
• ECG - Indices of Heart Rate Variability [Срок оценки: For the duration of the study, up to 5 days]
• Blood Pressure (Systolic and Diastolic) [Срок оценки: For the duration of the study, up to 5 days]

Критерии участия

Критерии включения

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.

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Публикации

• 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
• 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.
• 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

Идентификаторы

NCT: NCT05461144 · JH206817a

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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