A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance.

Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Institute's POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.

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.

Comment on Hochfellner et al. Accuracy Assessment of the GlucoMen® Day CGM System in Individuals with Type 1 Diabetes: A Pilot Study. Biosensors 2022, 12, 106.

In their recent article entitled "Accuracy Assessment of the GlucoMen® Day CGM System in Individuals with Type 1 Diabetes: A Pilot Study" [...].

Patch Pumps: Periodic Insulin Delivery Patterns.

Recent in vitro experiments with patch pumps (PP) Omnipod (OP), Omnipod DASH (OP-D), A6 TouchCare (A6), and Accu-Chek Solo (ACS) have observed periodic fluctuations in the delivered amount of insulin during basal rate and consecutive bolus delivery in some PP, calling for a more systematic characterization of these periodic delivery patterns. Here, it was found that during basal rate delivery of 1 U/h, some devices of OP, OP-D, and A6 showed deviations of up to ±30% from target delivery that consistently repeated every 5 hours, whereas ACS showed no clear periodicity with considerably lower deviations. Similar results were found during consecutive bolus delivery of 1 U, where deviations repeated consistently every five boluses in some devices of OP, OP-D, and A6. However, there was a large variability in the periodic delivery patterns between individual devices of the same PP model. Examining their pumping techniques indicated a connection between the insulin delivery mechanism and observed delivery patterns of the PP. However, the clinical impact of such patterns is unclear.

Accuracy Evaluation of a Novel Reusable Patch Pump Prototype.

A prototype of an insulin patch pump that operates with a double pump mechanism was tested. Accuracy of bolus delivery of 0.2 U and 1.0 U, and a basal rate of 1.0 U/h were evaluated with a microgravimetric method. In addition, occlusion detection time at basal rates of 0.1 U/h and 1.0 U/h was assessed. Mean deviation from target was lower than 2% for both bolus sizes. Regarding basal rate accuracy, mean deviation over 72 hours was lower than 1%. Occlusion detection occurred in less than 30 minutes with both basal rates. Our study results suggest that the tested pump prototype provides delivery accuracy and occlusion detection that is similar or even better compared to all commercially available pumps tested with a similar experimental approach.

A Statistical Approach for Assessing the Compliance of Integrated Continuous Glucose Monitoring Systems with Food and Drug Administration Accuracy Requirements.

To assess the compliance of "integrated" continuous glucose monitoring (CGM) systems with U.S. Food and Drug Administration requirements, the calculation of confidence intervals (CIs) on agreement rates (ARs), that is, the percentage of CGM measurements lying within a certain deviation of a comparator method, is stipulated. However, despite the existence of numerous approaches that could yield different results, a specific procedure for calculating CIs is not described anywhere. This report, therefore, proposes a suitable statistical procedure to allow transparency and comparability between CGM systems. Three existing methods were applied to six data sets from different CGM performance studies. The results indicate that a bootstrap-based method that accounts for the clustered structure of CGM data is reliable and robust. We thus recommend its use for the estimation of CIs of ARs. A software implementation of the proposed method is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems.

BACKGROUND: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems. METHODS: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. RESULTS: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment). CONCLUSIONS: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.

Continuous Glucose Profiles in Healthy People With Fixed Meal Times and Under Everyday Life Conditions.

BACKGROUND: The increased use of continuous glucose monitoring (CGM) and automated insulin delivery systems raises the question about therapeutic targets for glucose profiles in people with diabetes. This study aimed to assess averaged pre- and postprandial glucose profiles in people without diabetes to provide guidance for normal glucose patterns in clinical practice. For that, number and timing of meal intake were predefined. MATERIAL AND METHODS: To assess glucose traces in 36 participants without diabetes (mean age = 23.7 ± 5.7 years), CGM was performed for up to 14 days, starting with a run-in phase (first 3 days, excluded from analysis) followed by 4 days with fixed meal times at 8:00 am, 1:00 pm, and 6:00 pm and the remaining 7 days spent under everyday life conditions. Data from two simultaneously worn CGM sensors were averaged and adjusted to capillary plasma-equivalent glucose values. Glucose data were evaluated through descriptive statistics. RESULTS: Median glucose concentration on days with fixed meal times and under everyday life conditions was 95.0 mg/dL (91.6-99.1 mg/dL, interquartile range) and 98.1 mg/dL (93.7-100.8 mg/dL), respectively. On days with fixed meal times, mean premeal glucose was 92.8 ± 9.4 mg/dL, and mean peak postmeal glucose was 143.3 ± 23.5 mg/dL. CONCLUSIONS: By defining the time of meal intake, a clear pattern of distinct postprandial glucose excursions in participants without diabetes could be demonstrated and analyzed. The presented glucose profiles might be helpful as an estimate for adequate clinical targets in people with diabetes.

Real-Time Continuous Glucose Monitoring Can Predict Severe Hypoglycemia in People with Type 1 Diabetes: Combined Analysis of the HypoDE and DIAMOND Trials.

Objective: We combined data from two landmark trials (DIAMOND and HypoDE) to examine the diagnostic performance of low glucose measurements derived from open and masked continuous glucose monitoring (CGM) to predict the occurrence of future severe hypoglycemia (SH). Methods: We analyzed hypoglycemia parameters (low blood glucose index [LBGI], % <70 mg/dL, 54-69 mg/dL [level 1 hypoglycemia] and <54 mg/dL [level 2 hypoglycemia]) from masked CGM over 14 days during baseline and from open CGM over 14 days after randomization. We used receiver operating characteristics (ROC) curves to evaluate the screening performance of these measures to predict future SH. Positive likelihood ratios were calculated to indicate the overall diagnostic performance of these parameters. Results: Data from 288 individuals with type 1 diabetes (mean age 45.6 ± 12.8 years, diabetes duration 20.7 ± 13.7 years, HbA1c 8.2% ± 1.0%, Hypoglycemia Unawareness Score 3.4 ± 2.1) were analyzed. Area under ROC-curve (AUC) for LBGI and % <70 mg/dL ranged between 0.68 and 0.75, indicating that LBGI and % <70 mg/dL could significantly predict future SH. Significance of AUC regarding % <54 mg/dL were mixed (0.63-0.72). Positive and negative likelihood ratios ranged between 1.82 to 3.40 and 0.56 to 0.32, respectively. Suggested optimal cutoff values were remarkedly lower in open CGM than in masked CGM. Conclusion: These results indicate that CGM-derived hypoglycemic parameters have a good screening performance to significantly predict future clinical hypoglycemia. In addition, this analysis suggests that cutoff values to indicate elevated hypoglycemia risk in the future are substantially lower in open CGM than in masked CGM. ClinicalTrials.gov registration numbers: HypoDE: NCT02671968. DIAMOND: NCT02282397.

Mean Absolute Relative Difference of Blood Glucose Monitoring Systems and Relationship to ISO 15197.

BACKGROUND: The analytical quality of a blood glucose monitoring system (BGMS) is often assessed according to the requirements described in the international standard ISO 15197. However, the mean absolute relative difference (MARD) is sometimes used as well. This analysis aims at providing empirical data from BGMS evaluation studies conducted according to ISO 15197 and at providing an estimation of how MARD and percentage of measurement results within ISO accuracy limits are related. METHODS: Results of 77 system accuracy evaluations conducted according to ISO 15197 were used to calculate MARD between BGMS and a laboratory comparison method's results (glucose oxidase or hexokinase method). Additionally, bias and 95%-limits of agreement (LoA) using the Bland and Altman method were calculated. RESULTS: MARD results ranged from 2.3% to 20.5%. The lowest MARD of a test strip lot that showed <95% of results within ISO limits was 6.1%. The distribution of MARD results shows that only 3.6% of test strip lots with a MARD equal to or below 7% showed <95% of results within ISO limits (2.2% of all test strip lots). Bias of test strip lots that showed ≥95% of results within the limits ranged from -10.3% to +7.4%. The half-width of the 95%-LoA of test strip lots that showed ≥95% of results within the limits ranged from 4.8% to 24.0%. CONCLUSION: There is a threshold MARD that may allow an estimate whether ISO 15197 requirements are fulfilled, but this statement cannot be made with certainty.

Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges.

BACKGROUND: Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated. METHODS: A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics. RESULTS: On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects. DISCUSSION: The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.

Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes.

AIM: Correct estimation of meal carbohydrate content is a prerequisite for successful intensified insulin therapy in patients with diabetes. In this survey, the counting error in adult patients with type 1 diabetes was investigated. METHODS: Seventy-four patients with type 1 diabetes estimated the carbohydrate content of 24 standardized test meals. The test meals were categorized into 1 of 3 groups with different carbohydrate content: low, medium, and high. Estimation results were compared with the meals' actual carbohydrate content as determined by calculation based on weighing. A subgroup of the participants estimated the test meals for a second (n=35) and a third time (n=22) with a mean period of 11 months between the estimations. RESULTS: During the first estimation, the carbohydrate content was underestimated by -28% (-50, 0) of the actual carbohydrate content. Particularly meals with high mean carbohydrate content were underestimated by -34% (-56, -13). Median counting error improved significantly when estimations were performed for a second time (p<0.001). CONCLUSIONS: Participants generally underestimated the carbohydrate content of the test meals, especially in meals with higher carbohydrate content. Repetition of estimation resulted in significant improvements in estimation accuracy and is important for the maintenance of correct carbohydrate estimations. The ability to estimate the carbohydrate content of a meal should be checked and trained regularly in patients with diabetes.

Therapy adjustments in people with type 1 diabetes with impaired hypoglycemia awareness on multiple daily injections using real-time continuous glucose monitoring: a mechanistic analysis of the HypoDE study.

INTRODUCTION: Studies have shown beneficial effects of real-time continuous glucose monitoring (rtCGM) usage on clinical outcomes. The objective of this analysis was to identify which therapy adjustments were made by people with type 1 diabetes with impaired hypoglycemia awareness during rtCGM usage enabling reductions in the number of low glucose events observed in the HypoDE (Hypoglycemia in Deutschland) study. RESEARCH DESIGN AND METHODS: In the multicenter randomized controlled trial in people with type 1 diabetes on multiple daily injections with impaired hypoglycemia awareness, participants recorded their diabetes therapy in 7-day logbooks at baseline and at 6-month follow-up. They used rtCGM or self-monitoring of blood glucose for therapy adjustments. This mechanistic analysis looked at changes in various aspects of therapy. RESULTS: Logbooks were completed by 70 participants in the rtCGM group and 65 participants in the control group. Participants in the rtCGM group kept their total carbohydrate consumption, daily insulin doses and distribution constant during the study. However, they reported an increased intake of rescue carbohydrates (0.8±0.6 (mean±SD) vs 1.0±0.8 intake/day; baseline-adjusted between-group difference 0.3 intake (0.1-0.5), p=0.031). The glucose threshold at which rescue carbohydrate intake was initiated was elevated from 71±13 mg/dL (3.9±0.7 mmol/L) to 79±14 mg/dL (4.4±0.8 mmol/L) (adjusted between-group difference +7.6 mg/dL (2.4-12.8) (+0.4 mmol/L (0.1-0.7)); p=0.005) in the rtCGM group. Regression analysis showed that follow-up low glucose events were associated with group allocation (p<0.001), low glucose events at baseline (p=0.016) and rescue threshold (p=0.001). CONCLUSIONS: No major adjustments in insulin therapy were made by study participants with impaired hypoglycemia awareness; however, they were more active in preventing hypoglycemia by taking rescue carbohydrates earlier and more often. TRIAL REGISTRATION NUMBER: NCT02671968.

Diabetes management intervention studies: lessons learned from two studies.

INTRODUCTION: Several clinical studies investigated improvements of patient outcomes due to diabetes management interventions. However, chronic disease management is intricate with complex multifactorial behavior patterns. Such studies thus have to be well designed in order to allocate all observed effects to the defined intervention and to exclude effects of other confounders as well as possible. This article aims to provide challenges in interpreting diabetes management intervention studies and suggests approaches for optimizing study implementation and for avoiding pitfalls based on current experiences. MAIN BODY: Lessons from the STeP and ProValue studies demonstrated the difficulty in medical device studies that rely on behavioral changes in intervention group patients. To successfully engage patients, priority should be given to health care professionals being engaged, operational support in technical issues being available, and adherence being assessed in detail. Another difficulty is to avoid contamination of the control group with the intervention; therefore, strict allocation concealment should be maintained. However, randomization and blinding are not always possible. A limited effect size due to improvements regarding clinical endpoints in the control group is often caused by the Hawthorne effect. Improvements in the control group can also be caused with increased attention paid to the subjects. In order to reduce improvements in the control group, it is essential to identify the specific reasons and adjust study procedures accordingly. A pilot phase is indispensable for this. Another option is to include a third study arm to control for enhanced standard of care and study effects. Furthermore, retrospective data collection could be a feasible option. Adaptive study designs might reduce the necessity of a separate pilot study and combine the exploratory and confirmatory stages of an investigation in one single study. CONCLUSION: There are several aspects to consider in medical device studies when using interventions that rely on changes in behavior to achieve an effective implementation and significant study results. Improvements in the control group may reduce effect sizes and limit statistical significance; therefore, alternatives to the traditional randomized controlled trials may be considered.

Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics.

BACKGROUND: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. METHOD: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. RESULTS: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). CONCLUSION: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Insulin Pump Therapy for Patients With Type 2 Diabetes Mellitus: Evidence, Current Barriers, and New Technologies.

An increasing number of patients with type 2 diabetes mellitus (T2DM) use insulin pumps. The first insulin pumps especially designed for patients with T2DM have recently become available. However, national guidelines do not primarily recommend the use of continuous subcutaneous insulin infusion (CSII) for this patient group. The effectiveness of CSII in T2DM has not yet been convincingly demonstrated, despite some positive evidence. An overview and an assessment of various studies to date will be given. T2DM is a heterogeneous disease with a substantial phenotypic variability; therefore, it is difficult to provide general conclusions about the effectiveness of CSII in T2DM therapy. The pump types, characteristics, and associated barriers may play a relevant role for therapy outcome. Most advanced functions like various bolus dosages offered by conventional insulin pumps are not needed for T2DM treatment and complicate the device handling for this subject group. Additionally, new technologies like increased connectivity, advanced software features, and interoperability are currently becoming available representing further barriers. The implementation of this technological progress might be a benefit for pumps for T2DM as well. However, these have not been sufficiently examined either and increased security challenges due to integrated peripheral components should not be neglected in terms of a sound cybersecurity. Pump features and handling for patients with T2DM should be as easy as possible, indicating a need for insulin pumps specially designed for patients with T2DM. However, it has to be investigated if pumps designed for T2DM are more effective than other intensified insulin regimens.