Retrofitting Real-Life Dexcom G5 Data

Continuous Glucose Monitoring among Infants Born Very Preterm: Evidence for Accuracy in Neonatal Intensive Care

OBJECTIVE

To evaluate the accuracy of a device for continuous glucose monitoring (CGM) among infants born preterm admitted to the neonatal intensive care unit.

STUDY DESIGN

We analyzed paired CGM sensor glucose (SG) and point-of-care blood glucose (BG) measurements collected in infants born at ≤32 weeks of gestation or with a birth weight ≤1500 g. CGM was initiated within 48 hours from birth and maintained for 5 days. BG was performed every 12 hours and used to calibrate the sensor. Measures of CGM accuracy were computed from SG and BG pairs.

RESULTS

We included 501 SG-BG paired measurements from 51 infants (age 30.5 weeks [IQR 29.0-31.0 weeks], birth weight 1400 g [IQR 1100-1500 g] with at least 24 hours of CGM data. The mean absolute relative difference (MARD) between SG and point-of-care BG measures was 7.1% [IQR 5.6-9.3], corresponding to a difference of -5.6 mg/dL [95% CI -25 to +14 mg/dl]. The median sensor use was 96 hours [IQR 72-120] with 2.0 [IQR 1.7-2.4] calibrations per day.

CONCLUSIONS

Accuracy of SG measurements compared with BG measurements appears to be acceptable in a clinical study setting, with a negligible difference between SG and BG. Our data suggest that SG use may be clinically acceptable when the sensor is regularly calibrated.

Implantable and transcutaneous continuous glucose monitoring system: a randomized cross over trial comparing accuracy, efficacy and acceptance

AIM

To compare accuracy, efficacy and acceptance of implantable and transcutaneous continuous glucose monitoring (CGM) systems.

METHODS

In a randomized crossover trial we compared 12 weeks with Eversense implantable sensor (EVS) and 12 weeks with Dexcom G5 transcutaneous sensor (DG5) in terms of accuracy, evaluated as Mean Absolute Relative Difference (MARD) vs capillary glucose (SMBG), time of CGM use, adverse events, efficacy (as HbA1c, time in range, time above and below range) and psychological outcomes evaluated with Diabetes Treatment Satisfaction Questionnaire (DTSQ), Glucose Monitoring Satisfaction Survey (GMSS), Hypoglycemia Fear Survey (HFS2), Diabetes Distress Scale (DDS).

RESULTS

16 subjects (13 males, 48.8 ± 10.1 years, HbA1c 55.8 ± 7.9 mmol/mol, mean ± SD) completed the study. DG5 was used more than EVS [percentage of use 95.7 ± 3.6% vs 93.5 ± 4.3% (p = 0.02)]. MARD was better with EVS (12.2 ± 11.5% vs. 13.1 ± 14.7%, p< 0.001). No differences were found in HbA1c. While using EVS time spent in range increased and time spent in hyperglycemia decreased, but these data were not confirmed by analysis of retrofitted data based on SMBG values. EVS reduced perceived distress, without significant changes in other psychological outcomes.

CONCLUSIONS

CGM features may affect glycemic control and device acceptance.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

A Personalized Week-to-Week Updating Algorithm to Improve Continuous Glucose Monitoring Performance

BACKGROUND

Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends.

METHODS

In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week.

RESULTS

The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance.

CONCLUSIONS

Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.