Continuous Glucose Monitoring in Resource-Constrained Settings for Hypoglycaemia Detection: Looking at the Problem from the Other Side of the Coin

Hypoglycemia Unawareness and Recurrent Severe Hypoglycemia in an Individual With Type 1 Diabetes Mellitus on Insulin

Background/Objective

Hypoglycemia unawareness is a complication of recurrent hypoglycemia that can complicate diabetes management and impact quality of life. We present the case of an individual with type 1 diabetes with hypoglycemia unawareness and recurrent severe hypoglycemia requiring emergency intervention.

Case Report

A 55-year-old man with type 1 diabetes was referred for hypoglycemia unawareness and recurrent hypoglycemia with seizures. Over the prior 4 years he had >400 paramedic responses with 56 hospitalizations. Blood glucose levels ranged between 0.7 and 2.4 mmol/L during these episodes and presenting Hemoglobin A1c (HbA1c) was 4.6% (28 mmol/mol). He was taking insulin glargine daily and aspart with meals via insulin pens with no alternative etiology for his hypoglycemia was identified. The patient expressed difficulty with self-management, social instability, and limited appointment attendance. He was provided a continuous glucose monitor, educational support, and glycemic targets were broadened. After 6 months, HbA1c was 4.6% (28 mmol/mol) and he had 65 paramedic responses. A multidisciplinary team was organized for biweekly follow-up, community outreach, remote technological support, and psychological counseling. After 2 years, the patient had 2 emergency responses and HbA1c was 7.2% (55.2 mmol/mol).

Discussion

Permissive hyperglycemia, educational interventions, and continuous glucose monitoring are validated strategies for prevention of hypoglycemia. Limiting hypoglycemia is crucial to restore hypoglycemia awareness, and in severe cases may require high intensity follow-up, community outreach, and psychosocial support.

Conclusion

Hypoglycemia unawareness can complicate hypoglycemia prevention. Severe refractory cases are often multifaceted and may warrant a multidisciplinary approach to identify and target patient-specific needs.

Pediatric Diabetes and Diabetic Ketoacidosis After COVID-19: Challenges Faced and Lessons Learnt

The coronavirus disease (COVID-19) pandemic affected the management and follow-up of several chronic ailments, including pediatric type 1 diabetes mellitus (T1DM). Restricted access to healthcare and fear of contracting the virus during medical facility visits resulted in poor compliance, irregular follow-up visits, treatment, and delayed diagnosis of complications in pediatric diabetes such as diabetic ketoacidosis (DKA). As such, the incidence of complicated DKA in resource-limited settings is high due to delayed presentation, poor compliance with therapy, and associated comorbidities such as malnutrition and sepsis. The pandemic had only added to the woes. The increased surge in DKA, in the face of limited resources, prompted clinicians to find alternative solutions to manage these children effectively. In this narrative review, we discuss the key challenges faced globally while caring for children with T1DM and DKA during the COVID-19 pandemic, and the lessons learned thereof.

Temporal Trends of Blood Glucose in Children with Cerebral Malaria

Hypoglycemia, defined as a blood glucose < 2.2 mmol/L, is associated with death in pediatric cerebral malaria (CM). The optimal duration of glucose monitoring in CM is unknown. We collected data from 1,674 hospitalized Malawian children with CM to evaluate the association between hypoglycemia and death or neurologic disability in survivors. We assessed the optimal duration of routine periodic measurements of blood glucose. Children with hypoglycemia at admission had a 2.87-fold higher odds (95% CI: 1.35-6.09) of death and, if they survived, a 3.21-fold greater odds (95% CI: 1.51-6.86) of sequelae at hospital discharge. If hypoglycemia was detected at 6 hours but not at admission, there was a 7.27-fold higher odds of death (95% CI: 1.85-8.56). The presence of newly developed hypoglycemia after admission was not independently associated with neurological sequelae in CM survivors. Among all new episodes of blood sugar below a treatment threshold of 3.0 mmol/L, 94.7% occurred within 24 hours of admission. In those with blood sugar below 3.0 mmol/L in the first 24 hours, low blood sugar persisted or recurred for up to 42 hours. Hypoglycemia at admission or 6 hours afterward is strongly associated with mortality in CM. Children with CM should have 24 hours of post-admission blood glucose measurements. If a blood glucose less than the treatment threshold of 3.0 mmol/L is not detected, routine assessments may cease. Children who have blood sugar values below the treatment threshold detected within the first 24 hours should continue to have periodic glucose measurements for 48 hours post-admission.

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 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.

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 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.

Dysglycemia in Children with Severe Acute Malnutrition: A Systematic Review and Meta-Analysis

Dysglycemia is a common complication of severe acute malnutrition (SAM) in children. Its prevalence and impact on short- and long-term outcomes are not well described. This systematic review was undertaken to review the available evidence on dysglycemia (either hypo- or hyperglycemia) in hospitalized children with SAM. The 2 primary objectives of this systematic review were to understand the prevalence of hypoglycemia and hyperglycemia in children with SAM. A secondary objective was to understand the relation between dysglycemia and clinical outcomes like mortality in children with SAM. MEDLINE was searched with terms related to children, SAM, and dysglycemia. A meta-analysis of proportions was completed to determine the hypoglycemia prevalence and a standard meta-analysis was done to determine the relation between hypoglycemia and mortality. The certainty of the evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. A total of 2148 articles were identified in the database search of which 16 met the inclusion criteria for the systematic review based on screening done by multiple reviewers. The overall prevalence of hypoglycemia in SAM across studies based on the meta-analysis of proportions was 9% (95% CI: 7%, 12%; I2 = 92%). Meta-analysis results showed that hypoglycemia was associated with a higher chance of mortality during hospitalization in children with SAM (OR: 4.29; 95% CI: 3.04, 6.05; I2 = 0%). According to the GRADE evaluation, the certainty of the evidence for the prevalence of hypoglycemia was low and for hyperglycemia was very low. For the relation between hypoglycemia and mortality, the certainty of the evidence was moderate. A meta-analysis was not carried out for the prevalence of hyperglycemia due to the wide range of definitions used for across studies, but the prevalence ranged from 2% to 38% in the literature. This systematic review highlights the need for further work in this area to include serial glucose measurements to understand the clinical importance of dysglycemia during hospitalization in children with SAM.

Continuous glucose monitoring for hypoglycaemia in children: Perspectives in 2020

Hypoglycaemia in children is a major risk factor for adverse neurodevelopment with rates as high as 50% in hyperinsulinaemic hypoglycaemia (HH). A key part of management relies upon timely identification and treatment of hypoglycaemia. The current standard of care for glucose monitoring is by infrequent fingerprick plasma glucose testing but this carries a high risk of missed hypoglycaemia identification. High-frequency Continuous Glucose Monitoring (CGM) offers an attractive alternative for glucose trend monitoring and glycaemic phenotyping but its utility remains largely unestablished in disorders of hypoglycaemia. Attempts to determine accuracy through correlation with plasma glucose measurements using conventional methods such as Mean Absolute Relative Difference (MARD) overestimate accuracy at hypoglycaemia. The inaccuracy of CGM in true hypoglycaemia is amplified by calibration algorithms that prioritize hyperglycaemia over hypoglycaemia with minimal objective evidence of efficacy in HH. Conversely, alternative algorithm design has significant potential for predicting hypoglycaemia to prevent neuroglycopaenia and consequent brain dysfunction in childhood disorders. Delays in the detection of hypoglycaemia, alarm fatigue, device calibration and current high cost are all barriers to the wider adoption of CGM in disorders of hypoglycaemia. However, machine learning, artificial intelligence and other computer-generated algorithms now offer significant potential for further improvement in CGM device technology and widespread application in childhood hypoglycaemia.