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HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia
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 mo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580093/ https://www.ncbi.nlm.nih.gov/pubmed/36276186 http://dx.doi.org/10.1177/20552076221129712 |
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author | Worth, Chris Nutter, Paul W Dunne, Mark J Salomon-Estebanez, Maria Banerjee, Indraneel Harper, Simon |
author_facet | Worth, Chris Nutter, Paul W Dunne, Mark J Salomon-Estebanez, Maria Banerjee, Indraneel Harper, Simon |
author_sort | Worth, Chris |
collection | PubMed |
description | 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 (HYpoglycaemia-Prevention-thrOugh-CGM-HEatmap-Technology), 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. |
format | Online Article Text |
id | pubmed-9580093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-95800932022-10-20 HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia Worth, Chris Nutter, Paul W Dunne, Mark J Salomon-Estebanez, Maria Banerjee, Indraneel Harper, Simon Digit Health Original Research 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 (HYpoglycaemia-Prevention-thrOugh-CGM-HEatmap-Technology), 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. SAGE Publications 2022-10-17 /pmc/articles/PMC9580093/ /pubmed/36276186 http://dx.doi.org/10.1177/20552076221129712 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Worth, Chris Nutter, Paul W Dunne, Mark J Salomon-Estebanez, Maria Banerjee, Indraneel Harper, Simon HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title | HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title_full | HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title_fullStr | HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title_full_unstemmed | HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title_short | HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
title_sort | hypo-cheat’s aggregated weekly visualisations of risk reduce real world hypoglycaemia |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580093/ https://www.ncbi.nlm.nih.gov/pubmed/36276186 http://dx.doi.org/10.1177/20552076221129712 |
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