2022
DOI: 10.1177/20552076221129712
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HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia

Abstract: 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 approac… Show more

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Cited by 5 publications
(15 citation statements)
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“…The potential to prevent hospital admissions and reduce length of stay, along with longer-term impact of reducing disability and the direct and indirect costs associated should be considered. Future studies incorporating artificial intelligence and machine learning algorithms may further reduce the burden of hypoglycaemia ( 30 ).…”
Section: Discussionmentioning
confidence: 99%
“…The potential to prevent hospital admissions and reduce length of stay, along with longer-term impact of reducing disability and the direct and indirect costs associated should be considered. Future studies incorporating artificial intelligence and machine learning algorithms may further reduce the burden of hypoglycaemia ( 30 ).…”
Section: Discussionmentioning
confidence: 99%
“…Previously collected CGM data was used to identify periods of high hypoglycaemia risk in the early morning in patients with CHI; opening the door for targeted interventions on a group and individual level. Further work by this group ( 81 ) investigated patterns of hypoglycaemia at an individual level and found that each patient with CHI had clear and individual weekly patterns for repeated hypoglycaemia. Peeks et al.…”
Section: Novel Directions and A Possible Future For Cgm In Hypoglycaemiamentioning
confidence: 99%
“…Worth et al. used CGM to identify individual patterns in weekly hypoglycaemia risk of patients with CHI ( 81 ). The same group developed interpretative algorithms to facilitate patient understanding of patterns and provided suggestions for reflection designed to modify parental behaviours ( 35 ).…”
Section: Novel Directions and A Possible Future For Cgm In Hypoglycaemiamentioning
confidence: 99%
See 1 more Smart Citation
“…Worth et al reported that short-term use of an algorithm to detect weekly patterns in hypoglycemia frequency resulted in reduced time below range (hypoglycemia) from 7.1-5.4% [26]. However, in a related study, this same group found that CGM alone had insufficient accuracy for use as a standalone tool in clinical practice.…”
Section: Managementmentioning
confidence: 99%