Dataset Information

A head-to-head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot.

ABSTRACT: To compare the performance of a professional continuous glucose monitoring (proCGM) and a personal continuous glucose monitoring (persCGM) system worn in parallel under standardized conditions in individuals with type 1 diabetes (T1D), two CGM systems (iPro2 - proCGM; Minimed 640G - persCGM) worn in parallel using the same sensor (Enlite 2) were compared. Ten people with T1D were included in this single-centre, open-label study in which CGM performance was evaluated. The study consisted of a 24-hours inpatient phase (meals, exercise, glycaemic challenges) and a 4-day home phase. Analyses included fulfilment of ISO 15197:2013 criteria, mean absolute relative difference (MARD), Parkes Error Grid and Bland-Altman plots. During the inpatient stay, ISO 15197:2013 criteria fulfilment was 58.4% (proCGM) and 57.8% (persCGM). At home, the systems met ISO 15197:2013 criteria by 66.5% (proCGM) and 65.3% (persCGM). No difference of MARD in inpatient phase (19.1 ±?16.7% vs. 19.0 ±?19.6; P = 0.83) and home phase (18.6 ±?26.8% vs. 17.4 ±?21.3%, P = 0.87) was observed. All sensors performed less accurately during hypoglycaemia. ProCGM and persCGM showed similar performance during daytime and night-time for the inpatient and the home phase. However, sensor performance was reduced during hypoglycaemia for both systems.

SUBMITTER: Moser O

PROVIDER: S-EPMC6590188 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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A head-to-head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot.

Moser Othmar O Pandis Marlene M Aberer Felix F Kojzar Harald H Hochfellner Daniel D Elsayed Hesham H Motschnig Melanie M Augustin Thomas T Kreuzer Philipp P Pieber Thomas R TR Sourij Harald H Mader Julia K JK

Diabetes, obesity & metabolism 20181225 4

To compare the performance of a professional continuous glucose monitoring (proCGM) and a personal continuous glucose monitoring (persCGM) system worn in parallel under standardized conditions in individuals with type 1 diabetes (T1D), two CGM systems (iPro2 - proCGM; Minimed 640G - persCGM) worn in parallel using the same sensor (Enlite 2) were compared. Ten people with T1D were included in this single-centre, open-label study in which CGM performance was evaluated. The study consisted of a 24- ...[more]

PMID: 30484947

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Project description:ObjectiveContinuous glucose monitoring (CGM) is the standard of care for glucose monitoring in children with diabetes, however there are limited data reporting their use in hyperinsulinaemic hypoglycaemia (HH). Here, we evaluate CGM accuracy and its impact on quality of life in children with HH.MethodsReal-time CGM (Dexcom G5 and G6) was used in children with HH aged 0-16years. Data from self-monitoring capillary blood glucose (CBG) and CGM were collected over a period of up to 28days and analysed. Quality of life was assessed by the PedsQL4.0 general module and PedsQL2.0 family impact module, completed by children and their parents/carers before and after CGM insertion. Analysis of accuracy metrics included mean absolute relative difference (MARD) and proportion of CGM values within 15, 20, and 30% or 15, 20, and 30 mg/dL of reference glucose values >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20, % 30/30). Clinical reliability was assessed with Clarke error grid (CEG) analyses.ResultsProspective longitudinal study with data analysed from 40 children. The overall MARD between reference glucose and paired CGM values (n=4,928) was 13.0% (Dexcom G5 12.8%, Dexcom G6 13.1%). The proportion of readings meeting %15/15 and %20/20 were 77.3% and 86.4%, respectively, with CEG analysis demonstrating 97.4% of all values in zones A and B. Within the hypoglycaemia range (<70 mg/dL), the median ARD was 11.4% with a sensitivity and specificity of 64.2% and 91.3%, respectively. Overall PedsQL child report at baseline and endpoint were 57.6 (50.5 - 75.8) and 87.0 (82.9 - 91.2), and for parents were 60.3 (44.8 - 66.0) and 85.3 (83.7 - 91.3), respectively (both p<0.001).ConclusionUse of CGM for children with HH is feasible, with clinically acceptable accuracy, particularly in the hypoglycaemic range. Quality of life measures demonstrate significant improvement after CGM use. These data are important to explore use of CGM in disease indications, including neonatal and paediatric diabetes, cystic fibrosis and glycogen storage disorders.

Project description:INTRODUCTION:The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM). METHODS:Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed. RESULTS:Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption. CONCLUSIONS:Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Project description:In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies.

Dataset Information

A head-to-head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot.

Publications

A head-to-head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot.

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