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.

A Proposal for the Clinical Characterization of Continuous Glucose Monitoring Trend Arrow Accuracy.

The assessment and characterization of trend accuracy, that is, the ability of a continuous glucose monitoring (CGM) system to correctly indicate the direction and rate of change (RoC) of glucose levels, has received comparatively little attention in the overall evaluation of CGM performance. As such, only few approaches that examine the trend accuracy have been put forward. In this article, we review existing approaches and propose the clinical trend concurrence analysis (CTCA) which is an adaptation of the conventional trend concurrence analysis. The CTCA is intended to directly evaluate the trend arrows displayed by the CGM systems by characterizing their agreement to suitably categorized comparator RoCs. Here, we call on manufactures of CGM systems to provide the displayed trend arrows for retrospective analysis. The CTCA classifies any deviations between the CGM trend and comparator RoC according to their risk for an adverse clinical event arising from a possibly erroneous treatment decision. For that, the existing rate error grid analysis and a specific set of trend arrow-based insulin dosing recommendations were used. The results of the CTCA are presented in an accessible graphical display and exemplified on data from three CGM systems. With this article, we hope to increase the awareness for the importance and challenges of assessing the accuracy of trend information displayed by CGM systems.

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Effect of Arterialization on Venous Blood Glucose Concentrations and Implications for Observed Continuous Glucose Monitoring Accuracy.

Background: Heating of the arm and/or hand ("arterialization") is sometimes used in continuous glucose monitoring (CGM) performance studies with the reported aim of reducing differences between venous and capillary glucose concentrations. In this study, the effect of heating on venous glucose concentrations and CGM accuracy was investigated. Methods: A heating pad set to 50°C (122°F) was used with 20 participants to heat either the dominant or nondominant arm and hand. Venous and capillary samples were obtained every 15 min on both arms throughout each of three 6-h glucose challenges. CGM sensors were worn on each upper arm for each of the three visits. Results: Heating of the arm led to a median increase in venous glucose concentrations of +1.4%. No similar effect on capillary concentrations was observed. As a result, the median capillary to venous difference decreased from +5.9% in the nonheated arm to +4.2% in the heated arm. CGM accuracy observed in this study was affected by the selection of heated venous, nonheated venous, or capillary glucose concentrations as comparator data. The heating effect was more pronounced with rapidly decreasing glucose concentrations. Temperatures on the skin did not exceed 40°C (104°F). No adverse events or protocol deviations were associated with the use of the heating pad. Conclusions: Heating of the arm led to a small increase in venous glucose concentrations, but venous concentrations did not reach the level of capillary glucose concentrations. CGM accuracy observed in this study varied depending on the selected comparator data. This study was registered at the German Clinical Trials Register (DRKS00031197).

Performance Assessment of Three Continuous Glucose Monitoring Systems in Adults With Type 1 Diabetes.

BACKGROUND: FIND, the global alliance for diagnostics, identified the nonmarket-approved continuous glucose monitoring (CGM) system, FiberSense system (FBS), as a potential device for use in low- and middle-income countries. Together with two market-approved, factory-calibrated CGM systems, namely, the FreeStyle Libre 2 (FL2) and the GlucoRx AiDEX (ADX), the FBS was subjected to a clinical performance evaluation. METHODS: Thirty adult participants with type 1 diabetes were enrolled. The study was mainly conducted at home, with three in-clinic sessions conducted over the study period of 28 days. Comparator measurements were collected from capillary samples, using a high-quality blood glucose monitoring system. RESULTS: Data from 31, 70, and 78 sensors of FBS, FL2, and ADX, respectively, were included in the performance analysis. The mean absolute relative differences between CGM and comparator data for FBS, FL2, and ADX were 14.7%, 9.2%, and 21.9%, and relative biases were -2.1%, -2.5%, and -18.5%, respectively. Analysis of individual sensor accuracy revealed low, moderate, and high sensor-to-sensor variability for FBS, FL2, and ADX, respectively. Sensor survival probabilities until the end of sensor life were 47.2% for FBS (28 days), 71.3% for FL2 (14 days), and 48.4% for ADX (14 days). CONCLUSIONS: The results of FBS were encouraging enough to conduct further performance and usability evaluations in a low- and middle-income country. The results of FL2 mainly agreed with existing studies, whereas ADX showed substantial deviations from previously reported results.

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" [...].

Impact of Blood Glucose Monitoring System Accuracy on Clinical Decision Making for Diabetes Management.

BACKGROUND: The accuracy of blood glucose monitoring systems (BGMS) is crucial for the safe and effective management of diabetes mellitus. Despite standardization of accuracy assessment procedures and requirements, various studies have shown that the accuracy of BGMS on the market can vary considerably. This article therefore provides health care professionals and users with an intuitive illustration of the impact of BGMS accuracy on clinical decision making. MATERIAL AND METHODS: Several hypothetical patient scenarios based on blood glucose (BG) levels in the low, normal, and high BG range are devised. Using data from a recent BGMS accuracy study, a method for calculating the expected range of BG readings from four examined BGMS at the selected BG levels is introduced. Based on these ranges, it is illustrated how clinical decisions and subsequent outcomes of the hypothetical patients are affected by the expected inaccuracies of the BGMS. RESULTS: The range of expected BGMS readings for the same true BG level can vary considerably between different BGMS. The discussion of hypothetical patient scenarios revealed that the use of some BGMS could be associated with an increased risk of adverse events such as failure to detect hypoglycemia, driving with an unsafe BG level, delay of treatment intervention in diabetes during pregnancy, or the failure to prevent diabetic ketoacidosis. CONCLUSIONS: This article can support both health care professionals and patients to understand the impact of BGMS accuracy in a relatable, clinical context. Furthermore, it is suggested that current accuracy requirements might be insufficient for the prevention of adverse clinical outcomes in certain circumstances.

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.

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.

Improving the Bias of Comparator Methods in Analytical Performance Assessments Through Recalibration.

BACKGROUND: In analytical performance studies, the choice of comparator method plays an important role, as studies have shown that there exist relevant systematic differences (bias) between laboratory analyzers. The feasibility of retrospective recalibration of measurement results through comparison with methods or materials of higher metrological order to minimize bias was therefore assessed. METHOD: Existing data from performance studies of continuous and blood glucose monitoring systems were retrospectively analyzed. Comparison with a higher-order method was performed for two different data sets. In both cases, subject samples were measured, and a subset was also measured on a higher-order method. Recalibration based on higher-order materials (standard reference material [SRM]) was conducted for two different data sets containing results from SRM and subject samples. Linear regression analysis was performed for each device separately. Resulting equations were applied to the respective complete data set of subject samples. Bias between devices in a data set across all subject samples was assessed before and after recalibration. RESULTS: Bias between devices was reduced from -3.6% to +0.6% in one data set and from +11.0% to +0.3% in the other by recalibration based on higher-order method. Using higher-order materials, bias was also reduced by recalibration, but mixed results were found: Bias was reduced from -3.1% to -0.1% in one data set and from -4.3% to -2.7% in the other. CONCLUSIONS: Recalibration did lead to a decrease in bias and thus can reduce the impact of the choice of comparator method. The procedure should be verified in a prospectively designed setting.

A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance.

BACKGROUND: Current mathematical models of postprandial glucose metabolism in people with normal and impaired glucose tolerance rely on insulin measurements and are therefore not applicable in clinical practice. This research aims to develop a model that only requires glucose data for parameter estimation while also providing useful information on insulin sensitivity, insulin dynamics and the meal-related glucose appearance (GA). METHODS: The proposed glucose-only model (GOM) is based on the oral minimal model (OMM) of glucose dynamics and substitutes the insulin dynamics with a novel function dependant on glucose levels and GA. A Bayesian method and glucose data from 22 subjects with normal glucose tolerance are utilised for parameter estimation. To validate the results of the GOM, a comparison to the results of the OMM, obtained by using glucose and insulin data from the same subjects is carried out. RESULTS: The proposed GOM describes the glucose dynamics with comparable precision to the OMM with an RMSE of 5.1 ± 2.3 mg/dL and 5.3 ± 2.4 mg/dL, respectively and contains a parameter that is significantly correlated to the insulin sensitivity estimated by the OMM (r = 0.7) Furthermore, the dynamic properties of the time profiles of GA and insulin dynamics inferred by the GOM show high similarity to the corresponding results of the OMM. CONCLUSIONS: The proposed GOM can be used to extract useful physiological information on glucose metabolism in subjects with normal glucose tolerance. The model can be further developed for clinical applications to patients with impaired glucose tolerance under the use of continuous glucose monitoring data.

Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance.

BACKGROUND AND OBJECTIVE: The oral minimal model (OMM) of glucose dynamics is a prominent method for assessing postprandial glucose metabolism. The model yields estimates of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose challenge. Despite its success, the OMM approach has several weaknesses that this paper addresses. METHODS: A novel procedure introducing three methodological adaptations to the OMM approach is proposed. These are: (1) the use of a fully Bayesian and efficient method for parameter estimation, (2) the model identification from non-fasting conditions using a generalised model formulation and (3) the introduction of a novel function to represent the meal-related glucose appearance based on two superimposed components utilising a modified structure of the log-normal distribution. The proposed modelling procedure is applied to glucose and insulin data from subjects with normal glucose tolerance consuming three consecutive meals in intervals of four hours. RESULTS: It is shown that the glucose effectiveness parameter of the OMM is, contrary to previous results, structurally globally identifiable. In comparison to results from existing studies that use the conventional identification procedure, the proposed approach yields an equivalent level of model fit and a similar precision of insulin sensitivity estimates. Furthermore, the new procedure shows no deterioration of model fit when data from non-fasting conditions are used. In comparison to the conventional, piecewise linear function of glucose appearance, the novel log-normally based function provides an improved model fit in the first 30 min of the response and thus a more realistic estimation of glucose appearance during this period. The identification procedure is implemented in freely accesible MATLAB and Python software packages. CONCLUSIONS: We propose an improved and freely available method for the identification of the OMM which could become the future standardard for the oral minimal modelling method of glucose dynamics.

Automated sleep stage classification based on tracheal body sound and actigraphy.

The current gold standard for assessment of most sleep disorders is the in-laboratory polysomnography (PSG). This approach produces high costs and inconveniences for the patients. An accessible and simple preliminary screening method to diagnose the most common sleep disorders and to decide whether a PSG is necessary or not is therefore desirable. A minimalistic type-4 monitoring system which utilized tracheal body sound and actigraphy to accurately diagnose the obstructive sleep apnea syndrome was previously developed. To further improve the diagnostic ability of said system, this study aims to examine if it is possible to perform automated sleep staging utilizing body sound to extract cardiorespiratory features and actigraphy to extract movement features. A linear discriminant classifier based on those features was used for automated sleep staging using the type-4 sleep monitor. For validation 53 subjects underwent a full-night screening at Ulm University Hospital using the developed sleep monitor in addition to polysomnography. To assess sleep stages from PSG, a trained technician manually evaluated EEG, EOG, and EMG recordings. The classifier reached 86.9% accuracy and a Kappa of 0.69 for sleep/wake classification, 76.3% accuracy and a Kappa of 0.42 for Wake/REM/NREM classification, and 56.5% accuracy and a Kappa of 0.36 for Wake/REM/light sleep/deep sleep classification. For the calculation of sleep efficiency (SE), a coefficient of determination r2 of 0.78 is reached. Additionally, subjects were classified into groups of SEs (SE≥40%, SE≥60% and SE≥80%). A Cohen's Kappa >0.61 was reached for all groups, which is considered as substantial agreement. The presented method provides satisfactory performance in sleep/wake and wake/REM/NREM sleep staging while maintaining a simple setup and offering high comfort. This minimalistic approach may address the need for a simple yet reliable preliminary sleep screening in an ambulatory setting.

A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus.

Modelling of the gluco-regulatory system in response to an oral glucose tolerance test (OGTT) has been the subject of research for decades. This paper presents an adaptation to the well-established oral minimal model that is identifiable from glucose data only and is able to capture the dynamics of glucose following both OGTT and mixed meal consumption. The model is in the form of low-dimensional differential equations with a recently introduced input function consisting of Gaussian shaped components. It was identified from glucose data recorded from six subjects without diabetes, prediabetes and type 2 diabetes under controlled conditions. The inferred parameters of the model are shown to have physiological meaning and produce realistic steady state behavior. This model may be useful in the development of clinical advisory tools for the treatment and prevention of non-insulin dependent type 2 diabetes mellitus.

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders.

Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r 2 = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.