Comparing Clinical Accuracy and Analytical Accuracy Between Continuous Glucose Monitors.

This study compares performance between two continuous glucose monitors (CGMs). The study design contains a mix of laboratory results (CGM vs YSI) and home results (CGM vs glucose meter). Analysis is provided for both clinical accuracy and analytical accuracy of CGM glucose measurements. Both types of accuracy are important. Error grid analysis informs about clinical accuracy. Analytical error is important as most users would prefer a CGM with a smaller spread of CGM versus reference differences. The authors provide the percentage of time that no result was obtained. Study design, data analysis, and editorial support were provided by a manufacturer of one of the products studied. This study provides a template for comparisons.

Comparison of Point Accuracy Between Two Widely Used Continuous Glucose Monitoring Systems.

BACKGROUND: Safe and effective self-management of glucose levels requires immediate access to accurate data. We assessed the point accuracy of the Dexcom G7 Continuous Glucose Monitoring System (Dexcom, Inc., San Diego, CA, USA) and FreeStyle Libre 3 (Abbott Diabetes Care, Alameda, CA, USA) sensors in a head-to-head comparison. METHOD: Multicenter, single-arm, prospective, nonsignificant risk evaluation enrolled adults (≥ 18 years) with diagnosed type 1 diabetes (T1D) or type 2 diabetes (T2D). Accuracy was assessed by comparing sensor data to laboratory reference values Yellow Springs Instrument [YSI] and capillary blood glucose values. Outcome measures were differences in mean absolute relative difference (MARD), number and percentage of matched glucose pairs within ±20 mg/dL/±20 of reference values within glucose ranges: < 54, 54 to 69, 70 to 180, 181 to 250, > 250 mg/dL, and combined. RESULTS: Data from 55 adults were included in the analysis. Analysis showed significantly lower MARD with the FreeStyle Libre 3 sensor vs the Dexcom G7 sensor (8.9% vs 13.6%, respectively, P < .0001) with a higher percentage of glucose values within ±20 mg/dL/±20 of reference (91.4% vs 78.6%). The MARD values for both continuous glucose monitoring (CGM) sensors were similar during the first 12 hours; however, the FreeStyle Libre 3 MARD was notably lower than the Dexcom G7 MARD during the next 12 hours (10.0% vs 15.1%, respectively, P < .0001) and throughout the study period. CONCLUSIONS: The FreeStyle Libre 3 sensor was more accurate than the Dexcom G7 sensor in all metrics evaluated throughout the study period. This is the first head-to-head study to our knowledge that compares the flagship products currently in widespread use of the two largest CGM manufacturers.

Type 1 diabetes in adults: diagnosis and management NICE guideline

This guideline covers care and treatment for adults (aged 18 and over) with type 1 diabetes. It includes advice on diagnosis, education and support, blood glucose management, cardiovascular risk, and identifying and managing long-term complications. In June 2022, we reviewed the evidence and made new recommendations on periodontitis.

Examination of the Igls Criteria for Defining Functional Outcomes of ß-cell Replacement Therapy: IPITA Symposium Report.

CONTEXT: The Igls criteria were developed to provide a consensus definition for outcomes of ß-cell replacement therapy in the treatment of diabetes during a January 2017 workshop sponsored by the International Pancreas & Islet Transplant Association (IPITA) and the European Pancreas & Islet Transplant Association. In July 2019, a symposium at the 17th IPITA World Congress was held to examine the Igls criteria after 2 years in clinical practice, including validation against continuous glucose monitoring (CGM)-derived glucose targets, and to propose future refinements that would allow for comparison of outcomes with artificial pancreas system approaches. EVIDENCE ACQUISITION: Utilization of the criteria in various clinical and research settings was illustrated by population as well as individual outcome data of 4 islet and/or pancreas transplant centers. Validation against CGM metrics was conducted in 55 islet transplant recipients followed-up to 10 years from a fifth center. EVIDENCE SYNTHESIS: The Igls criteria provided meaningful clinical assessment on an individual patient and treatment group level, allowing for comparison both within and between different ß-cell replacement modalities. Important limitations include the need to account for changes in insulin requirements and C-peptide levels relative to baseline. In islet transplant recipients, CGM glucose time in range improved with each category of increasing ß-cell graft function. CONCLUSIONS: Future Igls 2.0 criteria should consider absolute rather than relative levels of insulin use and C-peptide as qualifiers with treatment success based on glucose assessment using CGM metrics on par with assessment of glycated hemoglobin and severe hypoglycemia events.

Glycemic improvement with a novel interim intervention technique using retrospective professional continuous glucose monitoring (GLITTER study): A study from Mumbai, India.

BACKGROUND/AIMS: To assess the efficacy of a novel interim intervention technique using retrospective, blinded, professional continuous glucose monitoring system (pro CGM) with the Freestyle Libre Pro system over a 14-day single sensor wear-period. METHODS: A retrospective analysis comprised of 105 consecutive adults at single centre in India with Type 2 diabetes, HbA1c > 53 mmol/mol (>7%), on non-insulin divabetes agents and/or insulin. The interim intervention technique included three visits over 14 days: Visit 1, sensor placed and patients asked to keep a food log while on pro-CGM; Visit 2 (within 1 week), interim assessment of pro CGM and diet or pharmacotherapy modifications made accordingly; Visit 3 at day 14, pro CGM re-evaluated to assess glycemic control. Glucose target range was set at 70-180 mg/dL. Analyses included pre & post daily average glucose, time in range, time above range, and time below range. RESULTS: Average time for interim analysis was 5 days after pro CGM initiation. At Visit 3, daily average glucose decreased from 191.3 mg/dL at baseline to 137.4 mg/dL (p < 0.001). Time in range improved from 42.2% to 80.2% (p < 0.001). Time above range decreased from 52.1% to 18.3% (p < 0.001), with a concurrent decrease in time below range from 5.7% to 1.5% (p < 0.001). Recurrent hypoglycemia was detected in 27 (25.7%) individuals, whose average baseline time below range reduced from 21.1% to 1.9% (p < 0.001). CONCLUSION: The interim intervention technique is a cost effective and efficient method for improving glucose outcomes through lifestyle and pharmacotherapy modifications and while utilizing a single pro-CGM sensor. LOCATION OF STUDY: Wockhardt Hospital, Mumbai, India. CLINICAL REGISTRATION: not applicable due to retrospective chart review study design.

Glycemic deviation index: a novel method of integrating glycemic numerical value and variability.

BACKGROUND: There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established. METHODS: In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30). RESULTS: Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels. CONCLUSIONS: In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture.

BACKGROUND: Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano. METHODS: Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring. RESULTS: In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down. HYPOTHESIS AND DISCUSSION: The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis.

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Do patients with gestational diabetes mellitus and their own blood glucose meter have better pregnancy outcomes than those not using a glucose meter?

ABSTRACT: To compare pregnancy outcomes between patients with gestational diabetes mellitus (GDM) with and without their own blood glucose meter.We conducted a retrospective-cohort study of 835 women with GDM at the Second Hospital of Tianjin Medical University, Tianjin, China from 1 January 2016 to 31 December 2018. Perinatal outcomes of these patients were monitored and collected in the Tianjin Maternal and Child Health System. Each patient was advised by a certified clinical nutritionist regarding dietary analysis and lifestyle recommendations. All pregnant women with GDM were divided into the following 2 groups according to whether they had their own blood glucose meter: women with self-measured blood glucose levels with a routine obstetric examination in the study group (n = 424); and those with non-self-measured blood glucose levels with a double obstetric examination in the control group (n = 411). Maternal and fetal pregnancy outcomes were compared between these 2 groups. According to different self-management modes, the women were also divided into eight subgroups to compare blood sugar control and compliance with recommended insulin therapy.The cesarean section rate was significantly lower in the study group than in the control group (P < .05). The prevalence of large-for-gestational age (P < .05) and macrosomia was significantly lower in the study group than in the control group (both P < .05). The prevalence of appropriate-for-gestational age was significantly higher in the study group than in the control group (P < .05). Birth weight was significantly lower in the study group than in the control group (P < .05). The mean times for blood sugar control and from the doctor recommendation for insulin treatment to the patient compliance in the study group were significantly shorter than those in the control group (both P < .05). The proportion of insulin required in the study group was significantly lower than that in the control group (P < .05). There were no significant differences in the time of controlling blood sugar and compliance among the 4 subgroups of the study group. However, subgroups with a dietary diary in the control group were better.Self-monitoring blood sugar plus a routine obstetric examination can help patients with GDM control blood sugar, even without dietary diaries and treadmills. In addition to increasing the number of obstetric examinations, recording dietary diaries is helpful for controlling blood sugar in patients with GDM who are unwilling to measure blood sugar by themselves.

Comparison of oral glucose tolerance test and ambulatory glycaemic profiles in pregnant women in Uganda with gestational diabetes using the FreeStyle Libre flash glucose monitoring system.

BACKGROUND: The diagnosis of hyperglycaemia in sub-Saharan Africa (SSA) is challenging. Blood glucose levels obtained during oral glucose tolerance test (OGTT) may not reflect home glycaemic profiles. We compare OGTT results with home glycaemic profiles obtained using the FreeStyle Libre continuous glucose monitoring device (FSL-CGM). METHODS: Twenty-eight women (20 with gestational diabetes [GDM], 8 controls) were recruited following OGTT between 24 and 28 weeks of gestation. All women wore the FSL-CGM device for 48-96 h at home in early third trimester, and recorded a meal diary. OGTT was repeated on the final day of FSL-CGM recording. OGTT results were compared with ambulatory glycaemic variables, and repeat OGTT was undertaken whilst wearing FSL-CGM to determine accuracy of the device. RESULTS: FSL-CGM results were available for 27/28 women with mean data capture 92.8%. There were significant differences in the ambulatory fasting, post-prandial peaks, and mean glucose between controls in whom both primary and secondary OGTT was normal (n = 6) and those with two abnormal OGTTs or "true" GDM (n = 7). There was no difference in ambulatory mean glucose between these controls and the 13 women who had an abnormal primary OGTT and normal repeat OGTT. These participants had significantly lower body mass index (BMI) than the true GDM group (29.0 Vs 36.3 kg/m2, p-value 0.014). Paired OGTT/FSL-CGM readings revealed a Mean Absolute difference (MAD) -0.58 mmol/L and Mean Absolute Relative Difference (MARD) -11.9%. Bland-Altman plot suggests FSL-CGM underestimated blood glucose by approximately 0.78 mmol/L. CONCLUSION: Diagnosis of GDM on a single OGTT identifies a proportion of women who do not have a significantly higher home glucose levels than controls. This raises questions about factors which may affect the reproducibility of OGTT in this population, including food insecurity and atypical phenotypes of diabetes. More investigation is needed to understand the suitability of the OGTT as a diagnostic test in sub-Saharan Africa.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

The challenge of diabetes home control in COVID-19 times: Proof is in the pudding.

SARS-CoV-2 pandemic had changed self-care in diabetes. The aim of this work is to analyze the effect of lockdown on glycemic control in people with type 1 diabetes on multiple daily insulin injections using FGM. During lockdown no deleterious effect on glycemic control measured by FGM.

Time in Range for Multiple Technologies in Type 1 Diabetes: A Systematic Review and Network Meta-analysis.

BACKGROUND: Time in range is a key glycemic metric, and comparisons of management technologies for this outcome are critical to guide device selection. PURPOSE: We conducted a systematic review and network meta-analysis to compare and rank technologies for time in glycemic ranges. DATA SOURCES: We searched Evidenced-Based Medicine Reviews, CINAHL, Embase, MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PROSPERO, PsycInfo, PubMed, and Web of Science until 24 April 2019. STUDY SELECTION: We included randomized controlled trials ≥2 weeks' duration comparing technologies for management of type 1 diabetes in adults (≥18 years of age), excluding pregnant women. DATA EXTRACTION: Data were extracted using a predefined template. Outcomes were percent time with sensor glucose levels 3.9-10.0 mmol/L (70-180 mg/dL), >10.0 mmol/L (180 mg/dL), and <3.9 mmol/L (70 mg/dL). DATA SYNTHESIS: We identified 16,772 publications, of which 14 eligible studies compared eight technologies comprising 1,043 participants. Closed-loop systems led to greater percent time in range than any other management strategy, and mean percent time in range was 17.85 (95% predictive interval 7.56-28.14) longer than with usual care of multiple daily injections with capillary glucose testing. Closed-loop systems ranked best for percent time in range or above range with use of Surface Under the Cumulative RAnking curve (SUCRA) (98.5% and 93.5%, respectively). Closed-loop systems also ranked highly for time below range (SUCRA 62.2%). LIMITATIONS: Overall risk of bias ratings were moderate for all outcomes. Certainty of evidence was very low. CONCLUSIONS: In the first integrated comparison of multiple management strategies considering time in range, we found that the efficacy of closed-loop systems appeared better than all other approaches.

Use of Factory-Calibrated Real-time Continuous Glucose Monitoring Improves Time in Target and HbA1c in a Multiethnic Cohort of Adolescents and Young Adults With Type 1 Diabetes: The MILLENNIALS Study.

OBJECTIVE: International type 1 diabetes registries have shown that HbA1c levels are highest in young people with type 1 diabetes; however, improving their glycemic control remains a challenge. We propose that use of the factory-calibrated Dexcom G6 CGM system would improve glycemic control in this cohort. RESEARCH DESIGN AND METHODS: We conducted a randomized crossover trial in young people with type 1 diabetes (16-24 years old) comparing the Dexcom G6 CGM system and self-monitoring of blood glucose (SMBG). Participants were assigned to the interventions in random order during two 8-week study periods. During SMBG, blinded continuous glucose monitoring (CGM) was worn by each participant for 10 days at the start, week 4, and week 7 of the control period. HbA1c measurements were drawn after enrollment and before and after each treatment period. The primary outcome was time in range 70-180 mg/dL. RESULTS: Time in range was significantly higher during CGM compared with control (35.7 ± 13.5% vs. 24.6 ± 9.3%; mean difference 11.1% [95% CI 7.0-15.2]; P < 0.001). CGM use reduced mean sensor glucose (219.7 ± 37.6 mg/dL vs. 251.9 ± 36.3 mg/dL; mean difference -32.2 mg/dL [95% CI -44.5 to -20.0]; P < 0.001) and time above range (61.7 ± 15.1% vs. 73.6 ± 10.4%; mean difference 11.9% [95% CI -16.4 to -7.4]; P < 0.001). HbA1c level was reduced by 0.76% (95% CI -1.1 to -0.4) (-8.5 mmol/mol [95% CI -12.4 to -4.6]; P < 0.001). Times spent below range (<70 mg/dL and <54 mg/dL) were low and comparable during both study periods. Sensor wear was 84% during the CGM period. CONCLUSIONS: CGM use in young people with type 1 diabetes improves time in target and HbA1c levels compared with SMBG.

TIR generated by continuous glucose monitoring is associated with peripheral nerve function in type 2 diabetes.

AIMS: Continuous glucose monitoring (CGM)-derived time-in-range (TIR) of 3.9-10 mmol/L is associated with diabetic retinopathy in type 2 diabetes (T2DM), but its relationship to peripheral nerve function has not been previously investigated. To explore the association between the TIR and nerve conduction study parameters in patients with T2DM, we performed a cross-sectional analysis. METHODS: A total of 740 patients with T2DM were enrolled in this study. All of the participants were divided into tertiles according to the TIR (TIR low: ≤53%; TIR medium: 54-76%; TIR high: ≥77%). Composite Z-scores of nerve conduction velocity (CV), latency, and amplitude were calculated. The linear correlation between the TIR and composite nerve function Z-score was evaluated and risk assessment was analysed using binary logistic regression. RESULTS: The composite Z-score of the CV and amplitude increased with higher TIR and the composite Z-score of latency significantly decreased as the TIR tertiles increased (all P trend < 0.05). After adjusting for age, diabetes duration, height, weight and other confounding factors, higher TIR was associated with a higher composite Z-score of CV (ß = 0.230, P < 0.001), amplitude (ß = 0.099, P = 0.010), and lower composite Z-score of latency (ß = -0.172, P < 0.001). The risk of TIR tertiles and low composite Z-score of CV remained significant even after adjustment of HbA1c (TIR medium: OR = 0.48, P = 0.001; TIR high: OR = 0.41, P < 0.001). CONCLUSIONS: Higher TIR tertiles were independently associated with better peripheral nerve function. CGM-derived TIR may be a promising approach to screen patients for further assessment of possible diabetic peripheral neuropathy.

Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average.

INTRODUCTION: The capability of glucometer internal quality control (QC) in detecting varying magnitude of systematic error (bias), and the potential use of moving sum of positive results (MovSum) and moving average (MA) techniques as potential alternatives were evaluated. MATERIALS AND METHODS: The probability of error detection using routine QC and manufacturer's control limits were investigated using historical data. Moving sum of positive results and MA algorithms were developed and optimized before being evaluated through numerical simulation for false positive rate and probability of error detection. RESULTS: When the manufacturer's default control limits (that are multiple times higher than the running standard deviation (SD) of the glucometer) was used, they had 0-75% probability of detecting small errors up to 0.8 mmol/L. However, the error detection capability improved to 20-100% when the running SD of the glucometer was used. At a binarization threshold of 6.2 mmol/L and block sizes of 200 to 400, MovSum has a 100% probability of detecting a bias that is greater than 0.5 mmol/L. Compared to MovSum, the MA technique had lower probability of bias detection, especially for smaller bias magnitudes; MA also had higher false positive rates. CONCLUSIONS: The MovSum technique is suited for detecting small, but clinically significant biases. Point of care QC should follow conventional practice by setting the control limits according to the running mean and SD to allow proper error detection. The glucometer manufacturers have an active role to play in liberalizing QC settings and also enhancing the middleware to facility patient-based QC practices.

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.

Performance of the Dexcom G6 Continuous Glucose Monitoring System in Pregnant Women with Diabetes.

Background: The aim of this study was to determine the performance of the Dexcom G6 continuous glucose monitoring (CGM) system across three sensor wear sites in pregnant women with diabetes in the second or third trimesters. Methods: Participants with type 1 (T1D), type 2 (T2D), or gestational (GDM) diabetes mellitus were enrolled at three sites. Each wore two G6 sensors on the abdomen, upper buttock, and/or posterior upper arm for 10 days and underwent a 6-h clinic session between days 3 and 7 of sensor wear, during which YSI reference blood glucose values were obtained every 30 min. No intentional glucose manipulations were performed. Accuracy metrics included the proportion of CGM values that were within ±20% of paired reference values >100 mg/dL or ±20 mg/dL of YSI values ≤100 mg/dL (hereafter referred to as %20/20), as well as the analogous %15/15, %30/30, and %40/40. The mean absolute relative difference (MARD) between CGM-YSI pairs was also calculated. Results: Thirty-two participants with T1D (n = 20), T2D (n = 3), or GDM (n = 9) were enrolled: 19 were in the second trimester and 13 were in the third trimester of pregnancy. Compared with the reference, 92.5% of CGM values were within ±20%/20 mg/dL. The overall MARD and that of sensors worn on the abdomen, upper buttock, and posterior upper arm was 10.3%, 11.5%, 11.2%, and 8.7%, respectively. There were no device-related adverse events. Skin reactions at the insertion sites were absent or minor. Conclusions: The Dexcom G6 CGM system is accurate and safe in pregnant women with diabetes.