Michael Schoemaker

The SCGM1 System: subcutaneous continuous glucose monitoring based on microdialysis technique.

The SCGM1 System is designed to allow continuous glucose monitoring in the subcutaneous interstitial fluid for up to 120 h. The system is based on the microdialysis technique and is composed of three components: (1) a disposable Cassette, which contains the microdialysis catheter (with the necessary tubes), an electrochemical flow-through sensor for glucose measurement, and the fluid reservoirs for both the microdialysis perfusate and a reagent solution containing glucose oxidase; (2) the Sensor Unit, which houses the Cassette and is worn by the patient using a belt pack; and (3) the Data Manager, with an integrated blood glucose meter for the calibration of the glucose signal. The Data Manager also has the option of displaying the continuous glucose signal. The Sensor Unit and Data Manager exchange glucose data and calibration data by radio transmission. In vitro precision was assessed by measurements of two standard glucose solutions (90 mg/dL, 3.4%; 360 mg/dL, 2.4%) over a time course of 4 days. The mean difference (+/- SD) between SCGM1 System devices (n = 11) and 15 glucose standard solutions with different concentrations was 1.4 +/- 3.5 mg/dL. The mean relative difference and the mean absolute relative difference ranged from - 0.6% to 3.7% and from 0.2% to 3.8%, respectively. The inherent physical lag time was 31 +/- 2 min (n = 10). The interference on the glucose signal of ascorbic acid, acetaminophen, and uric acid at the highest physiological concentrations was below 4%. The SCGM1 System showed a reliable and precise performance under in vitro conditions.

Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment.

Background: Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult. Materials and Methods: Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision. Results: The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel. Conclusions: Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day.

Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring

Background: The performance of a continuous glucose monitoring (CGM) system in the early stage of development was assessed in an inpatient setting that simulates daily life conditions of people with diabetes. Performance was evaluated at low glycemic, euglycemic, and high glycemic ranges as well as during phases with rapid glucose excursions. Methods: Each of the 30 participants with type 1 diabetes (15 female, age 47 ± 12 years, hemoglobin A1c 7.7% ± 1.3%) wore two sensors of the prototype system in parallel for 7 days. Capillary blood samples were measured at least 16 times per day (at least 15 times per daytime and at least once per night). On two subsequent study days, glucose excursions were induced. For performance evaluation, the mean absolute relative difference (MARD) between CGM readings and paired capillary blood glucose readings and precision absolute relative difference (PARD), i.e., differences between paired CGM readings were calculated. Results: Overall aggregated MARD was 9.2% and overall aggregated PARD was 7.5%. During induced glucose excursions, MARD was 10.9% and PARD was 7.8%. Lowest MARD (8.5%) and lowest PARD (6.4%) were observed in the high glycemic range (euglycemic range, MARD 9.1% and PARD 7.4%; low glycemic range, MARD 12.3% and PARD 12.4%). Conclusions: The performance of this prototype CGM system was, particularly in the hypoglycemic range and during phases with rapid glucose fluctuations, better than performance data reported for other commercially available systems. In addition, performance of this prototype sensor was noticeably constant over the whole study period. This prototype system is not yet approved, and performance of this CGM system needs to be further assessed in clinical studies.

Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures

Background: Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these. Methods: CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay. Results: Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average. Conclusions: Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

Overview of a Novel Sensor for Continuous Glucose Monitoring

The core element of a continuous glucose monitoring (CGM) system is the glucose sensor, which should enable reliable CGM readings in the interstitial fluid in subcutaneous tissue for a period of several days. The aim of this article is to describe the layout and constituents of a novel glucose sensor and the rationale behind the measures that were used to optimize its performance. In order to achieve a stable glucose sensor signal, special attention was paid to the sensor materials and architecture, i.e., biocompatible coating of the sensor, limitation of glucose flux into the working electrode, low oxidation potential by use of manganese dioxide, and a tissue-averaging sensor design. A series of in vitro and in vivo evaluations showed that the sensor enables stable and accurate glucose sensing in the subcutaneous tissue for up to 7 days. Parallel measurements with four sensors in a single patient showed a close agreement between these sensors. In summary, this high-performance needle-type glucose sensor is well suited for CGM in patients with diabetes.

Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings

Introduction: The accuracy of continuous glucose monitoring (CGM) systems is often assessed with respect to blood glucose (BG) readings. CGM readings are affected by a physiological and a technical time delay when compared to BG readings. In this analysis, the dependence of CGM performance parameters on the BG rate of change was investigated for 2 CGM systems. Methods: Data from a previously published study were retrospectively analyzed. An established CGM system (Dexcom G4, Dexcom, San Diego, CA; system A) and a prototype system (Roche Diagnostics GmbH, Mannheim, Germany; system B) with 2 sensors each were worn by 10 subjects in parallel. Glucose swings were induced to achieve rapidly changing BG concentrations. Mean absolute relative differences (MARD) were calculated in different BG rate-of-change categories. In addition, sensor-to-sensor precision was assessed. Results: At BG rates of change of –1 mg/dl/min to 0 mg/dl/min and 0 mg/dl/min to +1 mg/dl/min, MARD results were 12.6% and 11.3% for system A and 8.2% and 10.0% for system B. At rapidly changing BG concentrations (<–3 mg/dl/min and ≥+3 mg/dl/min), higher MARD results were found for both systems, but system B was less affected (system A: 24.9% and 29.6%, system B: 10.6% and 16.3%). The impact of rate of change on sensor-to-sensor precision was less pronounced. Conclusions: Both systems were affected by rapidly changing BG concentrations to some degree, although system B was mostly unaffected by decreasing BG concentrations. It would seem that technological advancements in CGM systems might allow for a more precise tracking of BG concentrations even at rapidly changing BG concentrations.

A Novel Method for Continuous Online Glucose Monitoring in Humans: The Comparative Microdialysis Technique

The aim of this study was to prove the feasibility of continuous subcutaneous glucose monitoring in humans using the comparative microdialysis technique (CMT). The performance of the CMT was determined by comparing tissue glucose values with venous or capillary blood glucose values in healthy volunteers and type 1 diabetic subjects. The CMT is a microdialysis-based system for continuous online glucose monitoring in humans. This technique does not require calibration by the patient. Physiological saline with glucose (5.5 mM) is pumped in a stop-flow mode through a microdialysis probe inserted into the abdominal s.c. tissue. Tissue glucose concentration is calculated by comparing the dialysate and perfusate glucose concentrations. The time delay due to the measurement process is 9 min. We tested the CMT on six healthy volunteers and six type 1 diabetic patients for 24 h in our clinical setting. Comparisons were made to HemoCue analyzer (Angelholm, Sweden) capillary blood glucose measurements (healthy volunteers) and to venous blood glucose concentration determined with a Hitachi analyzer (diabetic patients). The mean absolute relative error of the CMT glucose values from the blood glucose values was 17.8+/-15.5% (n = 167) for the healthy volunteers and 11.0+/-10.8% (n = 425) for the diabetic patients. The mean difference was 0.42+/-1.06 mM (healthy volunteers) and -0.17+/-1.22 mM (diabetic patients). Error grid analysis for the values obtained in diabetic patients demonstrated that 99% of CMT glucose values were within clinically acceptable regions (regions A and B of the Clarke Error Grid). The study results show that the CMT is an accurate technique for continuous online glucose monitoring.

Significance and Reliability of MARD for the Accuracy of CGM Systems

Background: There is a need to assess the accuracy of continuous glucose monitoring (CGM) systems for several uses. Mean absolute relative difference (MARD) is the measure of choice for this. Unfortunately, it is frequently overlooked that MARD values computed with data acquired during clinical studies do not reflect the accuracy of the CGM system only, but are strongly influenced by the design of the study. Thus, published MARD values must be understood not as precise values but as indications with some uncertainty. Data and Methods: Data from a recent clinical trial, Monte Carlo simulations, and assumptions about the error distribution of the reference measurements have been used to determine the confidence region of MARD as a function of the number and the accuracy of the reference measurements. Results: The uncertainty of the computed MARD values can be quantified by a newly introduced MARD reliability index (MRI), which independently mirrors the reliability of the evaluation. Thus MARD conveys information on the accuracy of the CGM system, while MRI conveys information on the uncertainty of the computed MARD values. Conclusions: MARD values from clinical studies should not be used blindly but the reliability of the evaluation should be considered as well. Furthermore, it should not be ignored that MARD does not take into account the key feature of CGM sensors, the frequency of the measurements. Additional metrics, such as precision absolute relative difference (PARD) should be used as well to obtain a better evaluation of the CGM performance for specific uses, for example, for artificial pancreas.

Performance Comparison of CGM Systems MARD Values Are Not Always a Reliable Indicator of CGM System Accuracy

Background: The ongoing progress of continuous glucose monitoring (CGM) systems results in an increasing interest in comparing their performance, in particular in terms of accuracy, that is, matching CGM readings with reference values measured at the same time. Most often accuracy is evaluated by the mean absolute relative difference (MARD). It is frequently overseen that MARD does not only reflect accuracy, but also the study protocol and evaluation procedure, making a cross-study comparison problematic. Methods: We evaluate the effect of several factors on the MARD statistical properties: number of paired reference and CGM values, distribution of the paired values, accuracy of the reference measurement device itself and the time delay between data pairs. All analysis is done using clinical data from 12 patients wearing 6 sensors each. Results: We have found that a few paired points can have a potentially high impact on MARD. Leaving out those points for evaluation thus reduces the MARD. Similarly, accuracy of the reference measurements greatly affects the MARD as numerical and graphical data show. Results also show that a log-normal distribution of the paired references provides a significantly different MARD than, for example, a uniform distribution. Conclusions: MARD is a reasonable parameter to characterize the performance of CGM systems when keeping its limitations in mind. To support clinicians and patients in selecting which CGM system to use in a clinical setting, care should be taken to make MARD more comparable by employing a standardized evaluation procedure.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator:

Background: Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia. Method: We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events. Results: The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min). Conclusion: The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.