Reliability of real-time continuous glucose monitoring in infants

Glucose variability in 6-12-month-old healthy infants

Background

Metabolic programming of glucose homeostasis in the first 1,000 days of life may impact lifelong metabolic and cardiovascular health. Continuous glucose monitoring (CGM) devices may help measure the impact of dietary intake on glucose rhythms and metabolism in infants during the complementary feeding period.

Objectives

Demonstrate the feasibility of CGM to measure and quantify glucose variability in response to infant feeding and to evaluate associations between macronutrient meal composition and glucose variability.

Methods

The "FreeStyle Libre Pro^®" device interstitial glucose meter was applied to the anterior thigh of 10 healthy 6-12-month-old infants. Parents recorded food intake, time of feeding, and used daily dairies to record sleep time and duration. Descriptive statistics were employed for food intake, sleep and key glycemic parameters over three full days. Mixed linear models were used to assess glycemic changes.

Results

Mid-day, afternoon, and evening feeds contained >30 g carbohydrate and induced higher 2-h iAUC (3.42, 3.41, and 3.50 mmol/L*h respectively) compared to early and mid-morning feedings with ≤25 g carbohydrates (iAUC 2.72 and 2.81 mmol/L*h, p < 0.05). Early morning and evening milk feedings contained approximately 9 g of fat and induced a longer time to reach maximal glucose value (Tmax; 75 and 68 min, respectively) compared to lower fat feedings (2.9-5.9 g; Tmax range: 34-60 min; p < 0.05). Incremental glucose value at time of food intake (C0) increased significantly from 0.24 ± 0.39 mM in early morning to 1.07 ± 0.57 mM in the evening (p < 0.05). Over the day, 70% of glucose values remained within the normal range (3.5-5.5 mmol/L), 10% were between 5.5-10 mmol/L, and 20% were < 3.5 mmol/L.

Conclusion

Our data support the feasibility of using CGM to measure glucose in 6-12-month-old infants. The observation of possible diurnal glucose variability and typical glucose values may have implications for future studies investigating metabolic adaptation to nutritional intake in early life.

Accuracy to detect neonatal hyperglycaemia using real-time continuous glucose monitoring during postoperative period

Identifying hyperglycaemia during postoperative period is essential for neonates. The objective of the study was to analyse the accuracy and reliability of continuous glucose monitoring (CGM) device for detecting hyperglycaemia during postoperative period in neonates. In this prospective study, hourly glucose recordings by CGM device and six hourly by glucometer glucose (GG-reference test for patient management) were collected in ten surgical neonates during first three postoperative days. Mean absolute relative difference (MARD) and proportion of CGM values within ± 15%/15 mg/dL, ± 20%/20 mg/dL, and ± 30%/30 mg/dL of GG, were analysed from matched pair CGM and GG recordings. The diagnostic performance of CGM for neonatal hyperglycaemia (> 150 mg/dL) was expressed as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV); and the clinical reliability was assessed in Clarke Error Grid Analysis. A total of 720 CGM glucose readings, 120 matched -paired glucose readings by CGM --GG, and 37 episodes were hyperglycaemia by GG. The MARD was 10.76 %; the percentages of glucose readings within 15%/15 mg/dL, 20%/20 mg/dL, and 30%/30 mg/dL were 94.6%, 97.3% and 100% during the hyperglycaemia period. The sensitivity, specificity, PPV and NPV to detect hyperglycaaemia by CGM device were 100%, 93.9%, 88% and 100 % respectively. In Clarke Error Grid Analysis, 97.3 % points were in zone A and B during the hyperglycaemia period.

CONCLUSION

CGM device can be a clinically reliable tool for hyperglycaemia management during postoperative period in neonates.

WHAT IS KNOWN

• Neonates are vulnerable for hyperglycaemia during post-operative period and bed side glucometers are used for frequent glucose monitoring in them. • Continuous glucose monitor(CGM) devices are used for the glucose monitoring in adult and paediatric diabetes care.

WHAT IS NEW

• For the first time, this study analysed the accuracy and clinical reliability of FreeStyle Libre (CGM device) for identifying hyperglycaemia during post-operative period in neonates. • CGM device has very good accuracy for detecting hyperglycaemia in neonates, it may help the clinician for better glucose stability during post-operative period.

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.

Accuracy of continuous glucose monitoring in preterm infants: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Continuous glucose monitoring (CGM) could be a valuable instrument for measurement of glucose concentration in preterm neonate. We undertook a systematic review and meta-analysis to compare the diagnostic accuracy of CGM devices to intermittent blood glucose evaluation methods for the detection of hypoglycaemic or hypoglycaemic events in preterm infants.

DATA SOURCES

A structured electronic database search was performed for studies that assessed the accuracy of CGM against any intermittent blood glucose testing methods in detecting episodes of altered glycaemia in preterm infants. No restrictions were used. Three review authors screened records and included studies.

DATA EXTRACTION

Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. From individual patient data (IPD), sensitivity and specificity were determined using predefined thresholds. The mean absolute relative difference (MARD) of the studied CGM devices was assessed and if those satisfied the accuracy requirements (EN ISO 15197). IPD datasets were meta-analysed using a logistic mixed-effects model. A bivariate model was used to estimate the summary receiver operating characteristic curve (ROC) curve and extract the area under the curve (AUC). The overall level of certainty of the evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation.

RESULTS

Among 4481 records, 11 were included. IPD datasets were obtained for five studies. Only two of the studies showed an MARD lower than 10%, with none of the five CGM devices studied satisfying the European Union (EU) ISO 15197 requirements. Pooled sensitivity and specificity of CGM devices for hypoglycaemia were 0.39 and 0.99, whereas for hyperglycaemia were 0.87 and 0.99, respectively. The AUC was 0.70 and 0.86, respectively. The certainty of the evidence was considered as low to moderate. Limitations primarily related to the lack of representative population, reference standard and CGM device.

CONCLUSIONS

CGM devices demonstrated low sensitivity for detecting hypoglycaemia in preterm infants, however, provided high accuracy for detection of hyperglycaemia.

PROSPERO REGISTRATION NUMBER

CRD42020152248.

Open-Source Technology for Real-Time Continuous Glucose Monitoring in the Neonatal Intensive Care Unit: Case Study in a Neonate With Transient Congenital Hyperinsulinism

BACKGROUND

Use of real-time continuous glucose monitoring (rtCGM) systems has been shown to be a low-pain, safe, and effective method of preventing hypoglycemia and hyperglycemia in people with diabetes of various age groups. Evidence on rtCGM use in infants and in patients with conditions other than diabetes remains limited.

OBJECTIVE

This case study describes the off-label use of rtCGM and the use of an open-source app for glucose monitoring in a newborn with prolonged hypoglycemia secondary to transient congenital hyperinsulinism during the perinatal period.

METHODS

The Dexcom G6 rtCGM system (Dexcom, Inc) was introduced at 39 hours of age. Capillary blood glucose checks were performed regularly. In order to benefit from customizable alert settings and detect hypoglycemic episodes, the open-source rtCGM app xDrip+ was introduced at 9 days of age.

RESULTS

Time in range (45-180 mg/dL) for interstitial glucose remained consistently above 90%, whereas time in hypoglycemia (<45 mg/dL) decreased. Mean glucose was maintained above 70 mg/dL at 72 hours of life and thereafter. Daily sensor glucose profiles showed cyclic fluctuations that were less pronounced over time.

CONCLUSIONS

While off-label use of medication is both common practice and a necessity in newborn infants, there are few examples of off-label uses of medical devices, rtCGM being a notable exception. Real-time information allowed us to better understand glycemic patterns and to improve the quality of glycemic control accordingly. Severe hypoglycemia was prevented, and measurement of serum levels of insulin and further lab diagnostics were performed much faster, while the patient's individual burden caused by invasive procedures was reduced. Greater customizability of threshold and alert settings would be beneficial for user groups with glycemic instability other than people with diabetes, and for hospitalized newborn infants in particular. Further research in the field of personal and off-label rtCGM use, efficacy studies evaluating the accuracy of low glucose readings, and studies on the differences between algorithms in translating raw sensor data, as well as customization of commercially available rtCGM systems, is needed.

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.

Complexities in the medical management of hypoglycaemia due to congenital hyperinsulinism

Congenital Hyperinsulinism (CHI) is a rare disease of hypoglycaemia but is the most common form of recurrent and severe hypoglycaemia causing brain injury and neurodisability in children. The management of CHI is complex due to the limited choice of medications, all with a limited therapeutic window, often lacking efficacy and associated with serious side effects. The therapeutic strategy in CHI is to recognize and treat hypoglycaemia promptly, thereby optimizing long-term neurological outcomes; this should be achieved through individualized treatment plans that deliver glycaemic stability while minimizing side effects. Further, such a strategy should consider the likelihood of reduction in disease severity over time, with dose adjustments and medication withdrawal as indicated to optimize both safety and tolerability. The option for pancreatic surgery should also be considered in specific circumstances as appropriate for the patient's best long-term interests.