Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study

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.

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.

The Effectiveness of Insulin Pump Therapy Versus Multiple Daily Injections in Children With Type 1 Diabetes Mellitus in a Specialized Center in Riyadh

Objectives

Comparison of continuous subcutaneous insulin infusion (CSII) with multiple daily injections (MDI) in achieving glycemic control in youths with type 1 diabetes mellitus (T1DM).

Methods

Retrospective cohort study including 2 matched groups of youths with T1DM treated by CSII or MDI in a tertiary specialized children's hospital in Saudi Arabia. Children and adolescents aged up to 18 years, diagnosed with T1DM and using CSII or MDI, from the period 2016 to 2018. Patients on MDI were newly-diagnosed patients with T1DM who had the disease for only 1 year duration; all CSII patients had at least 1 to 2 years of T1DM but who had just started on pumps in the past 3 months. We excluded patients with other autoimmune diseases, non-ambulatory patients and those admitted to hospital for non-diabetes reasons. Primary outcome was HbA1c at 1, 2, and 3 years, with weight gain as a secondary outcome. Ambulatory glycemic profile was analyzed from a subset of patients using intermittently scanned continuous glucose monitoring (isCGM).

Results

A total of 168 youths with T1DM (n = 129 in the MDI group, n = 39 in the CSII group) were included. The CSII group consistently had lower HbA1c levels compared to the MDI group throughout a 3-year follow up period: 8.1% versus 10.1, P-value < .001 at 1 year, 7.5% versus 10.1% at 2 years, P-value  < .001, 8.9% versus 10.3% at 3 years, P-value = .033. Body mass index significantly increased in both groups at 1 year, although greater in CSII group. In a subgroup using isCGM (n = 37 on MDI and n = 29 on CSII), the CSII group had a lower average blood glucose (194 mg/dL vs 228 mg/dL, P-value = .028) and a lower estimated HbA1c level (8.4% vs 9.6%, P-value = .022).

Conclusion

Treatment with CSII resulted in lower HbA1c compared to MDI in our cohort, which was sustained over a 3-year period.

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.

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.

A comparison of bolus insulin dose errors based on results of a clinical trial of five blood glucose monitoring systems

Aim: Inaccurate blood glucose monitoring system (BGMS) results may lead to insulin dosing errors and adverse clinical outcomes. Results & methodology: This post-hoc analysis used a model to estimate the bolus insulin dose error associated with each of the five BGMSs, for a hypothetical person with diabetes (assuming a standardized meal and target blood glucose of 100 mg/dl). Differences in dose-error distribution between BGMSs were statistically tested. The 95% dose-error range for each BGMS was (insulin units): CONTOUR^®PLUS, -1.1-0.7; Accu-Chek^® Active, -2.4-0.7; Accu-Chek^® Performa, -2.9-0.8; FreeStyle Freedom, from -5.5 to -0.5; OneTouch^® SelectSimple^™, -4.1-3.0. Conclusion: The CONTOUR^®PLUS BGMS was associated with a statistically significantly smaller model-estimated median bolus insulin dose-error and dosing error range, compared with the other BGMSs.

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.

Analysis of "Use of Blood Glucose Meters Featuring Color Range Indicators Improves Glycemic Control and Patients With Diabetes in Comparison to Blood Glucose Meters Without Color (ACCENTS Study)"

Self-monitoring of blood glucose is a part of integral care of patients with diabetes mellitus. Understanding and appropriately responding to glucose levels is a fundamental part of self-management. Grady et al's work, published in the current issue of Journal of Diabetes Science and Technology, investigated whether switching people with diabetes from their usual meter to a meter featuring color range indicator (CRI) could improve glycemic control, by facilitating improved understanding of blood glucose targets. In this small but well-designed study, the authors have shown that meters with CRI features offer a potential advantage and may improve glucose control in patients with diabetes, both with T1D and T2D, across the therapy spectrum from oral agents to insulin therapy.

Continuous Glucose Monitoring (CGM) or Blood Glucose Monitoring (BGM): Interactions and Implications

At the 2017 10th annual International Conference on Advanced Technologies and Treatments for Diabetes (ATTD) in Paris, France, four speakers presented their perspectives on the roles of continuous glucose monitoring (CGM) and of blood glucose monitoring (BGM) in patient management within one symposium. These presentations included discussions of the differences in the accuracy of CGM and BGM, a clinical perspective on the physiological reasons behind differences in CGM and BGM values, and an overview of the impact of variations in device accuracy on patients with diabetes. Subsequently a short summary of these presentations is given, highlighting the value of good accuracy of BGM or CGM systems and the ongoing need for standardization. The important role of both BGM and CGM in patient management was a theme across all presentations.

The Financial Impact of Inaccurate Blood Glucose Monitoring Systems

OBJECTIVE

An in silico study of type 1 diabetes (T1DM) patients utilized the UVA-PADOVA Type 1 Diabetes Simulator to assess the effect of patient blood glucose monitoring (BGM) system accuracy on clinical outcomes. We applied these findings to assess the financial impact of BGM system inaccuracy.

METHODS

The study included 43 BGM systems previously assessed for accuracy according to ISO 15197:2003 and ISO 15197:2013 criteria. Glycemic responses for the 100 in silico adult T1DM subjects were generated, using each meter. Changes in estimated HbA1c, severe hypoglycemic events, and health care resource utilization were computed for each simulation. The HbA1c Translator modeling approach was used to calculate the financial impact of these changes.

RESULTS

The average cost of inaccuracy associated with the entire group of BGM systems was £155 per patient year (PPY). The average additional cost of BGM systems not meeting the ISO 15197:2003 standard was an estimated £178 PPY more than an average system that fulfills the standard and an estimated £235 PPY more than an average system that appears to meet the ISO 15197:2013 standard.

CONCLUSION

There is a clear relationship between BGM system accuracy and cost, with the highest costs being associated with BGM systems not meeting the ISO 15197:2003 standard. Lower costs are associated with systems meeting the ISO 15197:2013 system accuracy criteria. Using BGM systems that meet the system accuracy criteria of the ISO 15197:2013 standard can help reduce the clinical and financial consequences associated with inaccuracy of BGM devices.

The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

BACKGROUND

A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described.

METHOD

Intraday variability of insulin sensitivity (S_I) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily S_I. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added.

RESULTS

One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data.

CONCLUSIONS

The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.