The Financial Impact of Inaccurate Blood Glucose Monitoring Systems

User Performance Evaluation and System Accuracy Assessment of Four Blood Glucose Monitoring Systems With Color Coding of Measurement Results

BACKGROUND

Blood glucose monitoring systems (BGMSs) are a cornerstone in diabetes management. They have to provide sufficiently accurate results in the hands of lay users, particularly in insulin-treated patients. The aim of this study was user performance evaluation and system accuracy assessment of four BGMSs with color coding of results.

METHODS

Study procedures were based on ISO 15197:2013. User performance evaluation included data from 100 participants, each of whom used every system with one reagent lot. Study personnel observed user techniques. For the system accuracy assessment, 100 capillary samples were obtained for measurement in duplicate with each of three reagent system lots per system, resulting in 600 results per system.

RESULTS

All assessed BGMSs exhibited a sufficient level of accuracy, with small differences between them. In the user performance evaluation, study personnel observed the smallest total number of user errors with Contour Next (Ascensia), followed by Accu-Chek Instant (Roche), Medisafe Fit Smile (Terumo), and OneTouch Ultra Plus Reflect (LifeScan). Approximately 90% of participants stated that a consistent color scheme, eg, for low blood glucose (BG) values, should be used across all BGMSs. There was no clear preference among the four tested BGMSs regarding the specific way of displaying color coding.

CONCLUSIONS

The four BGMSs assessed in this study showed only small differences in an overall sufficient level of accuracy. User handling errors, as observed by study personnel, differed between the systems.

Evaluation of System Accuracy, Precision, Hematocrit Influence, and User Performance of Two Blood Glucose Monitoring Systems Based on ISO 15197:2013/EN ISO 15197:2015

INTRODUCTION

Sufficiently high analytical quality of blood glucose monitoring systems (BGMS) is a prerequisite for efficient diabetes therapy. In this study we assessed system accuracy, measurement repeatability, intermediate measurement precision, user performance, and the influence of hematocrit on two CE-marked blood glucose monitoring systems. For one BGMS, measurement accuracy using venous samples was additionally investigated.

METHODS

Study procedures were based on the International Organization of Standardization (ISO) 15197:2013/EN ISO 15197:2015 ("ISO 15197"). User performance included data from 100 subjects who used one test strip lot, whereas for all other analyses three different reagent system lots were used. For system accuracy assessment, 100 capillary samples were measured in duplicate with each of three reagent system lots per system, resulting in 600 results per system.

RESULTS

CareSens S Fit and CareSens H Beat both fulfilled the ISO 15197 accuracy criteria with 97.5-100% of each test strip lot's results falling within ± 15 mg/dL or ± 15% of the results of the comparison method and 100% of results in consensus error grid (CEG) zone A for all three lots. User performance evaluation revealed sufficient accuracy in the hands of lay users although some handling errors were documented by study staff. Assessment of measurement repeatability and intermediate measurement precision is given by standard deviation (SD) (glucose levels < 100 mg/dL) and by coefficient of variation (CV) (glucose concentrations ≥ 100 mg/dL). SD was ≤ 4.1 mg/dL and CV ≤ 4.2% for measurement repeatability and SD was ≤ 2.2 mg/dL and CV ≤ 2.6% for intermediate measurement precision. In case of hematocrit influence, both BGMS complied with all three tested lots with the defined criteria.

CONCLUSION

Both BGMS analyzed in this study fulfilled the required accuracy criteria of ISO 15197. They showed high precision, good performance in the hands of lay users, and the influence of hematocrit was acceptable in the labeled range.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Clinical Study of a High Accuracy Green Design Blood Glucose Monitor Using an Innovative Optical Transmission Absorbance System

BACKGROUND

Blood glucose monitoring (BGM) is essential for glycemic control in diabetic therapy. Followingly, accurate sensors are required for both daily personal and clinical use. The frequency of sensor use in patients with diabetes facilitates the use of disposable components. However, BGM systems are not exempt from green innovation sustainability initiatives.

METHODS

Clinical study of a high-accuracy green design blood glucose monitor using an innovative optical transmission absorbance system was carried out. Venous blood samples were collected from 104 patients with type II diabetes. The heat resistance of sensor strips was evaluated by storing sensor strips at 25℃ and 60℃ for approximately 3 months. Accuracy of the BGM system was evaluated via the ISO 15197:2013 protocol.

RESULTS

The BGM system achieved ±7.1% accuracy in glycemic level measurement, with 84% of all measurements within ±5% of the reference values. Furthermore, the sensor strip demonstrated heat resistance for more than 3 months when stored at 60℃.

CONCLUSIONS

A new, highly accurate BGM system was developed based on the latest optical measurement system, introducing a rare metal-free "green-strip." The developed BGM system achieved the highest reported accuracy in clinical research, using venous blood from patients with diabetes. The sensor strip also exhibited high heat resistance, reducing limitations on storage conditions.

Glukosemessung in der Diabetesdiagnostik und -therapie: Laboratoriumsmedizinische Untersuchung inkl. patientennaher Sofortdiagnostik, Blutglukoseselbstmessung und kontinuierliches Glukosemonitoring

Die Glukose-„Messlandschaft“ in Deutschland deckt viele verschiedene Einsatzzwecke und -gebiete ab. Sie reicht von hochstandardisiert messenden Laboranalysesystemen mit einem großen Gesamtportfolio von Messgrößen bis hin zu einfachen Systemen für den Patientengebrauch. Aus klinischer Sicht muss die Qualität von Glukosemessungen ausreichend hoch sein, um die vorgesehenen Zwecke, also Diagnosestellung und Verlaufskontrolle, zu erfüllen.

In der Heilkunde – also bei Messungen im Rahmen der Gesundheitsversorgung durch medizinisches Personal – definiert die Richtlinie der Bundesärztekammer zur Qualitätssicherung laboratoriumsmedizinischer Untersuchungen Vorgaben an die Qualitätssicherung. Es werden sowohl interne als auch externe (herstellerunabhängige) Qualitätskontrollen gefordert und mit spezifischen Vorgaben versehen. Diese Regelungen gelten demnach auch für alle Glukosemessungen in der Heilkunde, d. h. im Zentrallabor ebenso wie in der patientennahen Sofortdiagnostik.

Bei dem Haupteinsatzgebiet von Glukosemessungen, der Verlaufskontrolle durch Patienten mit Diabetes im Rahmen der Blutglukoseselbstmessung, fehlen i. d. R. Kontrollen der Messqualität. Für das kontinuierliche Glukosemonitoring im interstitiellen Gewebe sind Qualitätskontrollen nach dem aktuellen technischen Stand sogar überhaupt nicht möglich. Hinzu kommt, dass eine regelmäßige herstellerunabhängige Kontrolle der Messqualität nicht vorgeschrieben ist. Ziel der Arbeit ist es, einen Überblick über die unterschiedlichen Glukosemesssysteme sowie praktische Aspekte und Probleme bei der Glukosemessung zu geben.

Portable Device Based on Microwave Resonator for Noninvasive Blood Glucose Monitoring

A portable device for noninvasive blood glucose monitoring is presented. The device is based on a microwave open-loop microstrip resonator, acting as glucose sensor, following the results of a previous study. This work shows the design and development of the driving electronics, signal generation system, data processing, measurement setup and graphical user interface, to integrate the resonator into a device suitable for further experimentation in clinical scenarios. The measurement principle relies in the idea of relating the unloaded Q factor to the user's blood glucose level. An initial assessment is shown, whose results highlight some successful cases of blood glucose level tracking, and indicate the need for further research in clinical scenarios.

Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept

Self-management of blood glucose level is part and parcel of diabetes treatment, which involves invasive, painful, and uncomfortable methods. A proper non-invasive blood glucose monitor (NIBGM) is therefore desirable to deal better with it. Microwave resonators can potentially be used for such a purpose. Following the positive results from an in vitro previous work, a portable device based upon a microwave resonator was developed and assessed in a multicenter proof of concept. Its electrical response was analyzed when an individual's tongue was placed onto it. The study was performed with 352 individuals during their oral glucose tolerance tests, having four measurements per individual. The findings revealed that the accuracy must be improved before the diabetes community can make real use of the device. However, the relationship between the measuring parameter and the individual's blood glucose level is coherent with that from previous works, although with higher data dispersion. This is reflected in correlation coefficients between glycemia and the measuring magnitude consistently negative, although small, for the different datasets analyzed. Further research is proposed, focused on system improvements, individual calibration, and multitechnology approach. The study of the influence of other blood components different to glucose is also advised. Graphical abstract.

Diabetes Technological Revolution: Winners and Losers?

Over recent years there has been an explosion in availability of technical devices to support diabetes self-management. But with this technology revolution comes new hurdles. On paper, the available diabetes technologies should mean that the vast majority of people with type 1 diabetes have optimal glycemic control and are using their preferred therapy choices. Yet, it does not appear to be universally the case. In parallel, suboptimal glycemic control remains stubbornly widespread. Barriers to improvement include access to technology, access to expert diabetes health care professionals, and prohibitive insurance costs. Until access can be improved to ensure the technologies are available and usable by those that need them, there are many people with diabetes who are still losing out.

Modeling of Diabetes and Its Clinical Impact

Understanding all aspects of diabetes treatment is hindered by the complexity of this chronic disease and its multifaceted complications and comorbidities, including social and financial impacts. In vivo studies as well as clinical trials provided invaluable information for unraveling not only metabolic processes but also risk estimations of, for example, complications. These approaches are often time- and cost-consuming and have frequently been supported by simulation models. Simulation models provide the opportunity to investigate diabetes treatment from additional viewpoints and with alternative objectives. This review presents selected models focusing either on metabolic processes or risk estimations and financial outcomes to provide a basic insight into this complex subject. It also discusses opportunities and challenges of modeling diabetes.