دورية أكاديمية
أعرض في EDS

HYPO-CHEAT's aggregated weekly visualisations of risk reduce real world hypoglycaemia.

التفاصيل البيبلوغرافية
العنوان: HYPO-CHEAT's aggregated weekly visualisations of risk reduce real world hypoglycaemia.
المؤلفون: Worth C; Department of Computer Science, University of Manchester, Manchester, UK.; Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, UK., Nutter PW; Department of Computer Science, University of Manchester, Manchester, UK., Dunne MJ; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK., Salomon-Estebanez M; Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, UK., Banerjee I; Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, UK.; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK., Harper S; Department of Computer Science, University of Manchester, Manchester, UK.
المصدر: Digital health [Digit Health] 2022 Oct 17; Vol. 8, pp. 20552076221129712. Date of Electronic Publication: 2022 Oct 17 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: SAGE Publications Ltd Country of Publication: United States NLM ID: 101690863 Publication Model: eCollection Cited Medium: Print ISSN: 2055-2076 (Print) Linking ISSN: 20552076 NLM ISO Abbreviation: Digit Health Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Thousand Oaks, CA] : SAGE Publications Ltd, [2015]-
مستخلص: Background: Children with congenital hyperinsulinism (CHI) are at constant risk of hypoglycaemia with the attendant risk of brain injury. Current hypoglycaemia prevention methods centre on the prediction of a continuous glucose variable using machine learning (ML) processing of continuous glucose monitoring (CGM). This approach ignores repetitive and predictable behavioural factors and is dependent upon ongoing CGM. Thus, there has been very limited success in reducing real-world hypoglycaemia with a ML approach in any condition.
Objectives: We describe the development of HYPO-CHEAT ( HY poglycaemia- P revention-thr O ugh-CGM- HE atmap- T echnology), which is designed to overcome these limitations by describing weekly hypoglycaemia risk. We tested HYPO-CHEAT in a real-world setting to evaluate change in hypoglycaemia.
Methods: HYPO-CHEAT aggregates individual CGM data to identify weekly hypoglycaemia patterns. These are visualised via a hypoglycaemia heatmap along with actionable interpretations and targets. The algorithm is iterative and reacts to anticipated changing patterns of hypoglycaemia. HYPO-CHEAT was compared with Dexcom Clarity's pattern identification and Facebook Prophet's forecasting algorithm using data from 10 children with CHI using CGM for 12 weeks. HYPO-CHEAT's efficacy was assessed via change in time below range (TBR).
Results: HYPO-CHEAT identified hypoglycaemia patterns in all patients. Dexcom Clarity identified no patterns. Predictions from Facebook Prophet were inconsistent and difficult to interpret. Importantly, the patterns identified by HYPO-CHEAT matched the lived experience of all patients, generating new and actionable understanding of the cause of hypos. This facilitated patients to significantly reduce their time in hypoglycaemia from 7.1% to 5.4% even when real-time CGM data was removed.
Conclusions: HYPO-CHEAT's personalised hypoglycaemia heatmaps reduced total and targeted TBR even when CGM was reblinded. HYPO-CHEAT offers a highly effective and immediately available personalised approach to prevent hypoglycaemia and empower patients to self-care.
Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
(© The Author(s) 2022.)
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فهرسة مساهمة: Keywords: Hypoglycaemia; behaviour change; continuous glucose monitoring; data aggregation; human–computer interaction; prevention
تواريخ الأحداث: Date Created: 20221024 Latest Revision: 20221025
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC9580093
DOI: 10.1177/20552076221129712
PMID: 36276186
قاعدة البيانات: MEDLINE
الوصف
تدمد:2055-2076
DOI:10.1177/20552076221129712