4.7 Review

Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

Related references

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Editorial Material Pediatrics

Case for funding of continuous glucose monitoring systems for patients with recurrent hypoglycaemia

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Maltodextrin May Be a Promising Treatment Modality After Near-total Pancreatectomy in Infants Younger Than Six Months with Persistent Hyperinsulinism: A Case Report

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Summary: This article presents a case of a male infant with congenital hyperinsulinism (CHI) who continued to have persistent hypoglycemia despite undergoing a near-total pancreatectomy and receiving multiple medical treatments. However, the addition of maltodextrin to his diet successfully controlled his hypoglycemic episodes.

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Continuous Glucose Monitoring in the Management of Neonates With Persistent Hypoglycemia and Congenital Hyperinsulinism

Myat Win et al.

Summary: This retrospective study introduces the use of real-time CGM in monitoring persistent neonatal hypoglycemia. The study found significant fluctuations in glucose levels in babies with congenital hyperinsulinism, highlighting the challenges of preventing hypoglycemia. The results also suggest the potential application of CGM as an adjunct to clinical care in managing neonatal hypoglycemia.

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Association of Neonatal Hypoglycemia With Academic Performance in Mid-Childhood

Rajesh Shah et al.

Summary: This prospective cohort study examined the association between neonatal hypoglycemia and low educational achievement at age 9 to 10 years among infants at risk of hypoglycemia. The results showed that exposure to neonatal hypoglycemia was not significantly associated with lower educational achievement in mid-childhood among participants who were screened and treated if needed.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2022)

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Congenital Hyperinsulinism International: A Community Focused on Improving the Lives of People Living With Congenital Hyperinsulinism

Julie Raskin et al.

Summary: Congenital hyperinsulinism (HI) is a rare disease that causes severe hypoglycemia in newborns. It has complex disease management and limited treatment options, leading to numerous challenges for patients and their families. This paper describes the challenges of living with HI, shares family stories, and discusses the efforts of the patient organization CHI to improve the lives of HI patients.

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Global Registries in Congenital Hyperinsulinism

Tai L. S. Pasquini et al.

Summary: Congenital hyperinsulinism is the most frequent cause of severe, persistent hypoglycemia in newborn babies and children. Research priorities include understanding the natural history of the disease, developing new treatments, and managing hypoglycemia to prevent brain damage or death.

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A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets

Alessandro Rossi et al.

Summary: This study compared continuous glucose monitoring outcomes between adult patients with hepatic glycogen storage disease (GSDIa) and matched healthy volunteers. The study identified differences in glycemic variation and glycemic control between the two groups and emphasized the importance of generating a set of continuous glucose monitoring metrics for GSDIa. Future studies should explore the prognostic value of continuous glucose monitoring outcomes in GSDIa patients.

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Families' Experiences of Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A Thematic Analysis

Sameera Hannah Auckburally et al.

Summary: In this qualitative study, families' experiences of using continuous glucose monitoring (CGM) in patients with congenital hyperinsulinism (CHI) were explored. The study found that CGM served as an educational tool and motivated behavioral changes, leading to positive experiences and improved management of hypoglycemia. However, there were also negative experiences reported, such as alarms and reliability issues at low glucose levels. Families expected CHI-specific modifications and personalized application of CGM. Incorporating end users' opinions and experiences is crucial for optimizing the use of CGM in glycaemic monitoring of CHI.

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Dynamic Methods for Childhood Hypoglycemia Phenotyping: A Narrative Review

Alessandro Rossi et al.

Summary: Hypoglycemia is a result of an imbalance between glucose supply and demand, caused by defects in postprandial glucose regulation. It is a common metabolic emergency in children and can have severe neurological consequences. Therefore, investigating the specific cause of hypoglycemia is crucial for diagnosis, treatment, and prevention.

FRONTIERS IN ENDOCRINOLOGY (2022)

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Impact of tight glycemic control and hypoglycemia after pediatric cardiac surgery on neurodevelopmental outcomes at three years of age: Findings from a randomized clinical trial

Anjali Sadhwani et al.

Summary: This study examined the association between neurodevelopmental outcomes and tight glycemic control as well as hypoglycemia in children undergoing cardiac surgery. The results showed that there was no significant impact of tight glycemic control on neurodevelopmental outcomes at the age of three. However, children who experienced moderate to severe hypoglycemia had worse neurodevelopmental outcomes in longitudinal analyses.

BMC PEDIATRICS (2022)

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Towards values-based healthcare for inherited metabolic disorders: An overview of current practices for persons with liver glycogen storage disease and fatty acid oxidation disorders

Annieke Venema et al.

Summary: Value-based healthcare aims to achieve better outcomes for patients, improve quality of care, and reduce costs. Current practices primarily focus on common diseases, but there are challenges when applying these strategies to rare diseases.

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Continuous glucose monitoring in an infant with panhypopituitarism having hypoglycemia on growth hormone therapy

Kaho Kiuchi et al.

Summary: The usefulness of continuous glucose monitoring (CGM) in controlling glucose levels in infants with hypoglycemia and panhypopituitarism (PH) was explored in this case report. The study found that CGM effectively reduced the proportion of time for hypoglycemia without increasing hyperglycemia. No adverse events were associated with the use of CGM in this patient.

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The hypoglycaemia error grid: A UK-wide consensus on CGM accuracy assessment in hyperinsulinism

Chris Worth et al.

Summary: This study developed a Hypoglycaemia Error Grid (HEG) for CGM assessment in CHI patients and found that CGM accuracy is inadequate to recommend it as a standalone tool for routine clinical use. However, CGM still has value in other aspects for CHI patients.

FRONTIERS IN ENDOCRINOLOGY (2022)

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

Chris Worth et al.

Summary: This study developed HYPO-CHEAT, a heatmap-based technology, to identify weekly hypoglycemia patterns in children with congenital hyperinsulinism. Compared to other methods, HYPO-CHEAT showed promising results in preventing hypoglycemia and empowering patients to self-care.

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Continuous Glucose Monitoring Profiles in Healthy, Nondiabetic Young Children

Stephanie N. DuBose et al.

Summary: This study aimed to establish reference glucose ranges in healthy young children without diabetes and found that these children had an average glucose level of 103 mg/dL (5.7 mmol/L), spent most of their time within the range of 70-140 mg/dL (3.9-7.8 mmol/L), and had minimal time below 70 mg/dL (3.9 mmol/L).

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Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

Hima Zafar et al.

Summary: This study summarizes recent developments in non-invasive continuous glucose monitoring using sweat sensors, with a focus on potential integration into wearable platforms.

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Monitoring the Frequency and Duration of Hypoglycemia in Preterm Infants and Identifying Associated Factors

Maria del Mar Fernandez Martinez et al.

Summary: The study found a high incidence of hypoglycemia with significant duration in very low birth weight neonates, especially within the first 48 hours after birth. Continuous glucose monitoring (CGMS) is considered a potential method for improving glycemic control and preventing related complications in these neonates.

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Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Omer Mujahid et al.

Summary: The use of machine learning techniques in predicting hypoglycemia has been increasing in recent years for diabetic patients to anticipate and intervene in potential hypoglycemic events, improving their quality of life. The review identified a split between hypoglycemia prediction and detection works categorized based on machine learning approaches, training data, and prediction horizon.

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Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Chris Worth et al.

Summary: This study quantified the timing of hypoglycemia in patients with HI and revealed a high risk of hypoglycemia in the early hours. Despite real-time CGM, prolonged hypoglycemic events still occurred in patients with HI.

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Real-time continuous glucose monitoring in preterm infants (REACT): an international, open-label, randomised controlled trial

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Summary: The study found that using real-time continuous glucose monitoring can reduce the risk of prolonged or severe hyperglycaemia and hypoglycaemia in preterm infants. Further research is needed to determine optimal glucose targets, strategies to achieve them, and the potential impact on long-term health outcomes.

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Summary: This scoping review looked at Health-related Quality of Life in children and adolescents with congenital hyperinsulinism, revealing heterogeneous study designs and inconsistent results. It is recommended to use generic and condition-specific instruments in larger samples for longitudinal observation to gain a more comprehensive understanding of the impact of the disease on patients' quality of life.

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Summary: This study evaluated the effectiveness of continuous glucose monitoring in children with hypoglycemia, particularly in those with hyperinsulinism. Results showed that CGM had high accuracy in detecting severe hypoglycemia, but had a high number of false-positive readings at lower glucose levels. Parents reported less anxiety, better sleep, and a preference for using CGM for monitoring their at-risk children. Newer CGM systems with improved accuracy at lower glucose levels could further enhance monitoring in the future.

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Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

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Summary: Innovative machine learning model developed for predicting hypoglycemia risk in youth with T1D based on CGM datasets, with high sensitivity and specificity. Model performance improved with inclusion of insulin and carbohydrate data for certain prediction horizons. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control.

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