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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.

التفاصيل البيبلوغرافية
العنوان: 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.
المؤلفون: Cisuelo O; School of Engineering, University of Warwick, Coventry, UK owain.cisuelo@warwick.ac.uk., Stokes K; School of Engineering, University of Warwick, Coventry, UK., Oronti IB; School of Engineering, University of Warwick, Coventry, UK., Haleem MS; School of Engineering, University of Warwick, Coventry, UK., Barber TM; Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK.; Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.; Human Metabolism Research Unit, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK., Weickert MO; Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK.; Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK., Pecchia L; School of Engineering, University of Warwick, Coventry, UK.; Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy., Hattersley J; School of Engineering, University of Warwick, Coventry, UK.; Human Metabolism Research Unit, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
المصدر: BMJ open [BMJ Open] 2023 Apr 18; Vol. 13 (4), pp. e067899. Date of Electronic Publication: 2023 Apr 18.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: BMJ Publishing Group Ltd Country of Publication: England NLM ID: 101552874 Publication Model: Electronic Cited Medium: Internet ISSN: 2044-6055 (Electronic) Linking ISSN: 20446055 NLM ISO Abbreviation: BMJ Open Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : BMJ Publishing Group Ltd, 2011-
مواضيع طبية MeSH: Diabetes Mellitus, Type 1*/complications , Hypoglycemia*/diagnosis , Hypoglycemia*/etiology, Humans ; Adult ; Blood Glucose ; Blood Glucose Self-Monitoring ; Artificial Intelligence ; Pilot Projects ; Social Conditions ; Data Collection ; Electrocardiography ; Observational Studies as Topic
مستخلص: Introduction: Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.
Methods and Analysis: This observational study aims to recruit 30 patients with T1DM from a diabetes 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 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.
Ethics and Dissemination: This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.
Trial Registration Number: NCT05461144.
Competing Interests: Competing interests: None declared.
(© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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معلومات مُعتمدة: United Kingdom WT_ Wellcome Trust
فهرسة مساهمة: Keywords: General diabetes; General endocrinology; Health informatics
سلسلة جزيئية: ClinicalTrials.gov NCT05461144
المشرفين على المادة: 0 (Blood Glucose)
تواريخ الأحداث: Date Created: 20230418 Date Completed: 20230420 Latest Revision: 20240213
رمز التحديث: 20240213
مُعرف محوري في PubMed: PMC10124264
DOI: 10.1136/bmjopen-2022-067899
PMID: 37072364
قاعدة البيانات: MEDLINE
الوصف
تدمد:2044-6055
DOI:10.1136/bmjopen-2022-067899