Influence of time point of calibration on accuracy of continuous glucose monitoring in individuals with type 1 diabetes

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Modeling continuous glucose monitoring with fractional differential equations subject to shocks

Continuous Glucose Monitoring (CGM) produces long time-series of noisy observations of a single variable (tissue glucose concentration), whose evolution may be explained by a dynamical model. In order to represent the unknown mixture of possible control mechanisms of different orders affecting the measured variable, a fractional differential approach seems justified. In any case, variations in food intake and/or physical activity ought to be taken into account if a plausible interpretation of the dynamics is to be obtained. In the present work, the mathematical construction and the numerical implementation of a Fractional Differential Equations (FDE) initial value problem are systematically reviewed, with the intent of offering the reader a concise and mathematically rigorous description of this approach. An FDE model for CGM is formulated: the model includes compartments for stomach and intestinal glucose contents and for blood and tissue (subcutaneous) glucose concentrations, as well as the shock effects of food ingestion and of increased glucose consumption due to physical activity. The model parameters, including the (non-integer) order of differentiation, are estimated from CGM observations on six Type 1 diabetic patients. The best-fit fractional orders for the six subjects range from 1.59 to 2.13. For comparison, best fits have also been computed for all subjects using an average fractional order of 1.9 and integer orders of 1 and 2.The results indicate that in the case of CGM the fractional differential model, which should be physiologically more appropriate, in fact fits the data much better than the first-order model and also better than the 2nd-order model.

First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHODS

To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient.

RESULTS

Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase.

CONCLUSIONS

The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia

BACKGROUND

The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia.

SUBJECTS AND METHODS

CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics.

RESULTS

The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm.

CONCLUSIONS

The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm

The purpose of this study was to investigate the effect of using a 1-point calibration approach instead of a 2-point calibration approach on the accuracy of a continuous glucose monitoring (CGM) algorithm. A previously published real-time CGM algorithm was compared with its updated version, which used a 1-point calibration instead of a 2-point calibration. In addition, the contribution of the corrective intercept (CI) to the calibration performance was assessed. Finally, the sensor background current was estimated real-time and retrospectively. The study was performed on 132 type 1 diabetes patients. Replacing the 2-point calibration with the 1-point calibration improved the CGM accuracy, with the greatest improvement achieved in hypoglycemia (18.4% median absolute relative differences [MARD] in hypoglycemia for the 2-point calibration, and 12.1% MARD in hypoglycemia for the 1-point calibration). Using 1-point calibration increased the percentage of sensor readings in zone A+B of the Clarke error grid analysis (EGA) in the full glycemic range, and also enhanced hypoglycemia sensitivity. Exclusion of CI from calibration reduced hypoglycemia accuracy, while slightly increased euglycemia accuracy. Both real-time and retrospective estimation of the sensor background current suggest that the background current can be considered zero in the calibration of the SCGM1 sensor. The sensor readings calibrated with the 1-point calibration approach indicated to have higher accuracy than those calibrated with the 2-point calibration approach.

Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems

The relationship between both interstitial and blood glucose remains a debated topic, on which there is still no consensus. The experimental evidence suggests that blood and interstitial fluid glucose levels are correlated by a kinetic equilibrium, which as a consequence has a time and magnitude gradient in glucose concentration between blood and interstitium. Furthermore, this equilibrium can be perturbed by several physiological effects (such as foreign body response, wound-healing effect, etc.), with a consequent reduction of interstitial fluid glucose versus blood glucose correlation. In the present study, the impact of operating in the interstitium on continuous glucose monitoring systems (CGMs) will be discussed in depth, both for the application of CGMs in the management of diabetes and in other critical areas, such as tight glycaemic control in critically ill patients.

Accuracy of a Novel Noninvasive Multisensor Technology to Estimate Glucose in Diabetic Subjects During Dynamic Conditions

The purpose of this study was to determine whether an approach of multisensor technology with integrated data analysis in an armband system (SenseWear® Pro Armband, SWA) can provide estimates of plasma glucose concentration in diabetes. In all, 41 subjects with diabetes participated. On day 1 subjects underwent an oral glucose tolerance test (OGTT) and on day 2 a 60-minute treadmill test (TT). SWA plasma glucose estimates were compared against reference peripheral venous glucose concentrations. A continuous glucose monitoring device (CGM) was also placed on each subject to serve as a reference for clinical comparison. Pearson coefficient, Clarke error grid (CEG), and mean absolute relative difference (MARD) analyses were used to compare the performance of plasma glucose estimation. There were significant correlations between plasma glucose concentrations estimated by the SWA and the reference plasma glucose concentration during the OGTT (r = .65, P < .05) and the TT (r = .91, P < .05). CEG analysis revealed that during the OGTT, 93% of plasma glucose concentration readings were in the clinically acceptable zone A+B for the SWA and 95% for the CGM. During the TT, the SWA had 96% of readings in zone A+B, compared to 97% for the CGM. During OGTTs, MARDs for the SWA and CGM were 26% and 18%, respectively. During TTs, MARDs were 16% and 12%, respectively. Plasma glucose concentration estimation by the SWA's noninvasive multisensor approach appears to be feasible and its performance in estimating glucose approaches that of a CGM. The success of this pilot study suggests that multisensor technology holds promising potential for the development of a wearable, noninvasive, painless glucose monitor.

Non-invasive continuous glucose monitoring with multi-sensor systems: a Monte Carlo-based methodology for assessing calibration robustness

In diabetes research, non-invasive continuous glucose monitoring (NI-CGM) devices represent a new and appealing frontier. In the last years, some multi-sensor devices for NI-CGM have been proposed, which exploit several sensors measuring phenomena of different nature, not only for measuring glucose related signals, but also signals reflecting some possible perturbing processes (temperature, blood perfusion). Estimation of glucose levels is then obtained combining these signals through a mathematical model which requires an initial calibration step exploiting one reference blood glucose (RBG) sample. Even if promising results have been obtained, especially in hospitalized volunteers, at present the temporal accuracy of NI-CGM sensors may suffer because of environmental and physiological interferences. The aim of this work is to develop a general methodology, based on Monte Carlo (MC) simulation, to assess the robustness of the calibration step used by NI-CGM devices against these disturbances. The proposed methodology is illustrated considering two examples: the first concerns the possible detrimental influence of sweat events, while the second deals with calibration scheduling. For implementing both examples, 45 datasets collected by the Solianis Multisensor system are considered. In the first example, the MC methodology suggests that no further calibration adjustments are needed after the occurrence of sweat events, because the “Multisensor+model” system is able to deal with the disturbance. The second case study shows how to identify the best time interval to update the model's calibration for improving the accuracy of the estimated glucose. The methodology proposed in this work is of general applicability and can be helpful in making those incremental steps in NI-CGM devices development needed to further improve their performance.