Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These t...

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Autores principales: Worth, Chris, Hoskyns, Lucy, Salomon-Estebanez, Maria, Nutter, Paul W., Harper, Simon, Derks, Terry G.J, Beardsall, Kathy, Banerjee, Indraneel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900115/
https://www.ncbi.nlm.nih.gov/pubmed/36755920
http://dx.doi.org/10.3389/fendo.2023.1116864
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author Worth, Chris
Hoskyns, Lucy
Salomon-Estebanez, Maria
Nutter, Paul W.
Harper, Simon
Derks, Terry G.J
Beardsall, Kathy
Banerjee, Indraneel
author_facet Worth, Chris
Hoskyns, Lucy
Salomon-Estebanez, Maria
Nutter, Paul W.
Harper, Simon
Derks, Terry G.J
Beardsall, Kathy
Banerjee, Indraneel
author_sort Worth, Chris
collection PubMed
description In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.
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spelling pubmed-99001152023-02-07 Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023 Worth, Chris Hoskyns, Lucy Salomon-Estebanez, Maria Nutter, Paul W. Harper, Simon Derks, Terry G.J Beardsall, Kathy Banerjee, Indraneel Front Endocrinol (Lausanne) Endocrinology In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use. Frontiers Media S.A. 2023-01-23 /pmc/articles/PMC9900115/ /pubmed/36755920 http://dx.doi.org/10.3389/fendo.2023.1116864 Text en Copyright © 2023 Worth, Hoskyns, Salomon-Estebanez, Nutter, Harper, Derks, Beardsall and Banerjee https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Worth, Chris
Hoskyns, Lucy
Salomon-Estebanez, Maria
Nutter, Paul W.
Harper, Simon
Derks, Terry G.J
Beardsall, Kathy
Banerjee, Indraneel
Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title_full Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title_fullStr Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title_full_unstemmed Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title_short Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023
title_sort continuous glucose monitoring for children with hypoglycaemia: evidence in 2023
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900115/
https://www.ncbi.nlm.nih.gov/pubmed/36755920
http://dx.doi.org/10.3389/fendo.2023.1116864
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