Accuracy of blood glucose meters for self-monitoring affects glucose control and hypoglycemia rate in children and adolescents with type 1 diabetes

Diagnosis and management of post-bariatric surgery hypoglycemia

INTRODUCTION

While bariatric surgery remains the most effective treatment for obesity that allows substantial weight loss with improvement and possibly remission of obesity-associated comorbidities, some postoperative complications may occur. Managing physicians need to be familiar with the common problems to ensure timely and effective management. Of these complications, postoperative hypoglycemia is an increasingly recognized complication of bariatric surgery that remains underreported and underdiagnosed.

AREA COVERED

This article highlights the importance of identifying hypoglycemia in patients with a history of bariatric surgery, reviews pathophysiology and addresses available nutritional, pharmacological and surgical management options. Systemic evaluation including careful history taking, confirmation of hypoglycemia and biochemical assessment is essential to establish accurate diagnosis. Understanding the weight-dependent and weight-independent mechanisms of improved postoperative glycemic control can provide better insight into the causes of the exaggerated responses that lead to postoperative hypoglycemia.

EXPERT OPINION

Management of post-operative hypoglycemia can be challenging and requires a multidisciplinary approach. While dietary modification is the mainstay of treatment for most patients, some patients may benefit from pharmacotherapy (e.g. GLP-1 receptor antagonist); Surgery (e.g. reversal of gastric bypass) is reserved for unresponsive severe cases. Additional research is needed to understand the underlying pathophysiology with a primary aim in optimizing diagnostics and treatment options.

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.

System accuracy evaluation of 18 CE-marked current-generation blood glucose monitoring systems based on EN ISO 15197:2015

OBJECTIVE

Accuracy of 18 current-generation blood glucose monitoring systems (BGMS) available in Europe was evaluated applying criteria adapted from EN ISO 15197:2015 with one reagent system lot. BGMS were selected based on market research data.

RESEARCH DESIGN AND METHODS

The BGMS ABRA, Accu-Chek Guide, AURUM, CareSens Dual, CERA-CHEK 1CODE, ContourNext One, eBsensor, FreeStyle Freedom Lite, GL50 evo, GlucoCheck GOLD, GlucoMen areo 2K, GluNEO, MyStar DoseCoach, OneTouch Verio Flex, Pic GlucoTest, Rightest GM700S, TRUEyou, and WaveSense JAZZ Wireless were tested using capillary blood from 100 different subjects and assessing the percentage of results within ±15 mg/dL (0.83 mmol/L) or 15% of comparison method results for BG concentrations below or above 100 mg/dL (5.55 mmol/L), respectively. In addition, the minimal deviation from comparison method results within which ≥95% of results of the respective BGMS were found was calculated.

RESULTS

In total, 14 BGMS had ≥95% of results within ±15 mg/dL (0.83 mmol/L) or ±15% and 3 BGMS had ≥95% of results within ±10 mg/dL (0.55 mmol/L) or ±10% of the results obtained with the comparison method. The smallest deviation from comparison method results within which ≥95% of results were found was ±7.7 mg/dL (0.43 mmol/L) or ±7.7%; the highest deviation was ±19.7 mg/dL (1.09 mmol/L) or ±19.7%.

CONCLUSIONS

This accuracy evaluation shows that not all CE-labeled BGMS fulfill accuracy requirements of ISO 15197 reliably and that there is considerable variation even among BGMS fulfilling these criteria. This safety-related information should be taken into account by patients and healthcare professionals when making therapy decisions.

TRIAL REGISTRATION NUMBER

NCT03737188.

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.

A Review of Blood Glucose Monitor Accuracy

The aim of this study was to assess the accuracy of blood glucose monitors (BGMs) from studies reported in the medical literature. A literature review was performed of publications between 2010 and 2017 that presented data about the accuracy of BGMs using ISO 15197 2003 and/or ISO 15197 2013 as target standards. We found 58 publications describing the performance of 143 unique BGM systems, 59 of which were Food and Drug Administration (FDA) cleared. When compared with non-FDA-cleared BGMs, FDA-cleared BGMs were significantly more likely to pass both ISO 15197 2003 (OR = 2.39, CI 1.45-3.92, P < 0.01) and ISO 15197 2013 standards (OR = 2.20, CI 1.51-3.27, P < 0.01). Newer meters were more likely to pass both ISO 15197 2003 and ISO 15197 2013 standards. Many of the studies were supported by BGM manufacturers, and when compared with independent studies, an FDA-cleared BGM was significantly more likely to pass in a manufacturer-supported study for both ISO 15197 2003 (OR = 22.4, CI 8.73-21.57, P < 0.001) and ISO 15197 2013 (OR = 23.08, CI 10.16-60.03, P < 0.001). BGM accuracy should be assessed independently following regulatory clearance to ensure accurate performance. Failure to meet performance levels mandated by standards can result in deleterious clinical and economic effects.

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.

Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 and Type 2 Diabetes in England

OBJECTIVE

The objective was to model clinical and economic outcomes of self-monitoring blood glucose (SMBG) devices with varying error ranges and strip prices for type 1 and insulin-treated type 2 diabetes patients in England.

METHODS

We programmed a simulation model that included separate risk and complication estimates by type of diabetes and evidence from in silico modeling validated by the Food and Drug Administration. Changes in SMBG error were associated with changes in hemoglobin A1c (HbA1c) and separately, changes in hypoglycemia. Markov cohort simulation estimated clinical and economic outcomes. A SMBG device with 8.4% error and strip price of £0.30 (exceeding accuracy requirements by International Organization for Standardization [ISO] 15197:2013/EN ISO 15197:2015) was compared to a device with 15% error (accuracy meeting ISO 15197:2013/EN ISO 15197:2015) and price of £0.20. Outcomes were lifetime costs, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs).

RESULTS

With SMBG errors associated with changes in HbA1c only, the ICER was £3064 per QALY in type 1 diabetes and £264 668 per QALY in insulin-treated type 2 diabetes for an SMBG device with 8.4% versus 15% error. With SMBG errors associated with hypoglycemic events only, the device exceeding accuracy requirements was cost-saving and more effective in insulin-treated type 1 and type 2 diabetes.

CONCLUSIONS

Investment in devices with higher strip prices but improved accuracy (less error) appears to be an efficient strategy for insulin-treated diabetes patients at high risk of severe hypoglycemia.

Accuracy Beyond ISO: Introducing a New Method for Distinguishing Differences Between Blood Glucose Monitoring Systems Meeting ISO 15197:2013 Accuracy Requirements

BACKGROUND

Diabetes treatment is intended to maintain near-normal glycemic levels. Self-monitoring of blood glucose (SMBG) allows patients to track their BG levels compared with glycemic targets and is associated with improved health outcomes. Because of the importance of SMBG, it is essential that results are accurate to prevent errors in nutritional intake and drug dosing. This study presents a new methodology to evaluate the accuracy of BG monitoring systems (BGMSs).

METHODS

Sensitivity analyses were performed using real and simulated BGMS data to compute probabilities that, for any BG value, the BGMS result would be within prescribed error bounds and confidence limits compared with laboratory reference values. Multiple BG value ranges were used.

RESULTS

Probability curves were created using data from 3 simulated BGMSs and anonymized data from 3 real-world BGMSs. Accuracy probability curves from capillary fingertip blood samples (actual clinical data) showed that all 3 real-world BGMSs met EN ISO 15197:2015 accuracy criteria, since 99.63%, 99.63%, and 99.81% of results from the 3 BGMSs were within ±15 mg/dL or ±15% of reference for BG <100 mg/dL and ≥100 mg/dL, respectively. However, there was identifiable variability between BGMSs if BG was <70 mg/dL; one BGMS showed further reductions in accuracy if BG was <50 mg/dL.

CONCLUSIONS

Probability curves highlight the importance of BGMS accuracy to help achieve optimal glycemic control while avoiding hypoglycemia or hyperglycemia. This may be especially significant in very low BG ranges where small errors in BGMS measurements can have substantial impacts on patient-related outcomes, including hypoglycemia risk.

Analysis of Risk Factors for Hypoglycemic Coma in 194 Patients with Type 2 Diabetes

Background

The present study was conducted to analyze possible risk factors in patients with type 2 diabetes who are in hypoglycemic coma.

Material/Methods

A total of 194 patients with type 2 diabetic hypoglycemic coma who were admitted to our hospital between January 2010 and January 2016 were included. The patients were all in coma on admission, and their blood glucose levels were lower than 2.8 mmol/L. None of the patients had type I diabetes, specific types of diabetes, or gestational diabetes. Multiple linear regression analysis was used to determine possible factors associated with hypoglycemic coma.

Results

Among the patients, 82 were male and 112 were female (mean age, 66.88±10.62 years). In addition, 72 patients lived in urban areas and 122 lived in rural areas. Occurrence of hypoglycemic coma was correlated with difference between urban and rural residence, glycosylated hemoglobin (HbA1c) level, combined hypertension, and combined neural complications. Self-purchased drugs resulted in significantly lower blood glucose level at the onset of hypoglycemic coma than insulin, secretagogue, or non-secretagogue drugs. Blood glucose level at onset was correlated with season. Patients living in rural areas or with combined macrovascular or microvascular complications had prolonged hospital stay and poor prognosis.

Conclusions

Our results demonstrate that rural residence, higher HbA1c level, combined hypertension, and combined neural complications increase the incidence of hypoglycemic coma. Use of self-purchased drugs and colder seasons may result in lower blood glucose levels in patients with hypoglycemic coma.

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

BACKGROUND

Patients with diabetes rely on blood glucose (BG) monitoring devices to manage their condition. As some self-monitoring devices are becoming more and more accurate, it becomes critical to understand the relationship between system accuracy and clinical outcomes, and the potential benefits of analytical accuracy.

METHODS

We conducted a 30-day in-silico study in type 1 diabetes mellitus (T1DM) patients using continuous subcutaneous insulin infusion (CSII) therapy and a variety of BG meters, using the FDA-approved University of Virginia (UVA)/Padova Type 1 Simulator. We used simulated meter models derived from the published characteristics of 43 commercial meters. By controlling random events in each parallel run, we isolated the differences in clinical performance that are directly associated with the meter characteristics.

RESULTS

A meter's systematic bias has a significant and inverse effect on HbA1c ( P < .01), while also affecting the number of severe hypoglycemia events. On the other hand, error, defined as the fraction of measurements beyond 5% of the true value, is a predictor of severe hypoglycemia events ( P < .01), but in the absence of bias has a nonsignificant effect on average glycemia (HbA1c). Both bias and error have significant effects on total daily insulin (TDI) and the number of necessary glucose measurements per day ( P < .01). Furthermore, these relationships can be accurately modeled using linear regression on meter bias and error.

CONCLUSIONS

Two components of meter accuracy, bias and error, clearly affect clinical outcomes. While error has little effect on HbA1c, it tends to increase episodes of severe hypoglycemia. Meter bias has significant effects on all considered metrics: a positive systemic bias will reduce HbA1c, but increase the number of severe hypoglycemia attacks, TDI use, and number of fingersticks per day.

Both the frequency of HbA1c testing and the frequency of self-monitoring of blood glucose predict metabolic control: A multicentre analysis of 15 199 adult type 1 diabetes patients from Germany and Austria

BACKGROUND

The objective of this study was to examine the association between metabolic control and frequency of haemoglobin A_1c (HbA_1c ) measurements and of self-monitoring of blood glucose, as well as the interaction of both.

METHODS

Data of 15 199 adult type 1 diabetes patients registered in a standardized electronic health record (DPV) were included. To model the association between metabolic control and frequency of HbA_1c testing or of self-monitoring of blood glucose, multiple hierarchic regression models with adjustment for confounders were fitted. Tukey-Kramer test was used to adjust P values for multiple comparisons. Vuong test was used to compare non-nested models.

RESULTS

The baseline variables of the study population were median age 19.9 [Q1; Q3: 18.4; 32.2] years and diabetes duration 10.4 [6.8; 15.7] years. Haemoglobin A_1c was 60.4 [51.5; 72.5] mmol/mol. Frequency of HbA_1c testing was 8.0 [5.0; 9.0] within 2 years, and daily self-monitoring of blood glucose frequency was 5.0 [4.0; 6.0]. After adjustment, a U-shaped association between metabolic control and frequency of HbA_1c testing was observed with lowest HbA_1c levels in the 3-monthly HbA_1c testing group. There was an inverse relationship between self-monitoring of blood glucose and HbA_1c with lower HbA_1c associated with highest frequency of testing (>6 daily measurements). Quarterly HbA_1c testing and frequent self-monitoring of blood glucose were associated with best metabolic control. The adjusted Vuong Z statistic suggests that metabolic control might be better explained by HbA_1c testing compared to self-monitoring of blood glucose (P < .0001).

CONCLUSION

This research reveals the importance of quarterly clinical HbA_1c monitoring together with frequent self-monitoring of blood glucose in diabetes management to reach and maintain target HbA_1c .

Commentary Regarding Shapiro, "Nonadjunctive Use of Continuous Glucose Monitors for Insulin Dosing: Is It Safe?"

The FDA recently expanded the approved use of Dexcom's G5 Mobile continuous glucose monitoring (CGM) system to allow for diabetes treatment decisions. This decision is expected to reduce the burden of SMBG testing and increase the adoption and persistent use of CGM. The safety of nonadjunctive CGM use was questioned because of sporadic large discrepancies between CGM and SMBG values. These data were viewed in the context of complaints found in the FDA MAUDE database and social media postings. This commentary provides additional perspective on the inferences that can be drawn from these reports and the risk of nonadjunctive use of CGM data.

Factory-Calibrated Continuous Glucose Sensors: The Science Behind the Technology

The use of commercially available continuous glucose monitors for diabetes management requires sensor calibrations, which until recently are exclusively performed by the patient. A new development is the implementation of factory calibration for subcutaneous glucose sensors, which eliminates the need for user calibrations and the associated blood glucose tests. Factory calibration means that the calibration process is part of the sensor manufacturing process and performed under controlled laboratory conditions. The ability to move from a user calibration to factory calibration is based on several technical requirements related to sensor stability and the robustness of the sensor manufacturing process. The main advantages of factory calibration over the conventional user calibration are: (a) more convenience for the user, since no more fingersticks are required for calibration and (b) elimination of use errors related to the execution of the calibration process, which can lead to sensor inaccuracies. The FreeStyle Libre^™ and FreeStyle Libre Pro^™ flash continuous glucose monitoring systems are the first commercially available sensor systems using factory-calibrated sensors. For these sensor systems, no user calibrations are required throughout the sensor wear duration.

Clinical review: the misreporting of logbook, download, and verbal self-measured blood glucose in adults and children with type I diabetes

Despite advances in technology, the frequent self-measurement of blood glucose (SMBG) remains fundamental to the management of 1 diabetes mellitus (T1DM). Once measured, SMBG results are routinely reported back to health professionals and other interested parties, either verbally, via a logbook, or electronically downloaded from a pump or meter. The misreporting of SMBG using various techniques represents a classic non-adherence behavior and carries with it both acute and chronic dangers. In addition, while this behavior appears very prevalent, many aspects remain largely unstudied. With this in mind, we aimed to summarize literature addressing the misreporting of SMBG in T1DM via a detailed literature search. This produced both recent and past literature. While most of these studies examined the prevalence of deliberate misreporting in a verbal or logbook context, others focused on the motivations behind this behavior, and alternative forms of misreporting, including deliberate manipulation of meters to produce inaccurate results and true technological errors. This timely review covers all aspects of misreporting and highlights multiple patient techniques, which are clearly adapting to advances in technology. We believe that further understanding and attention to this aspect of adherence may lead not only to improvements in glycemic control and safety, but also to the psychological well-being of those affected by type 1 diabetes.

Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions

While self-monitoring of blood glucose (SMBG) is the current standard used by people with diabetes to manage glucose levels, recent improvements in accuracy of continuous glucose monitoring (CGM) technology are making it very likely that diabetes-related treatment decisions will soon be made based on CGM values alone. Nonadjunctive use of CGM will lead to a paradigm shift in how patients manage their glucose levels and will require substantial changes in how care providers educate their patients, monitor their progress, and provide feedback to help them manage their diabetes. The approval to use CGM nonadjunctively is also a critical step in the pathway toward FDA approval of an artificial pancreas system, which is further expected to transform diabetes care for people with type 1 diabetes. In this article, we discuss how nonadjunctive CGM is expected to soon replace routine SMBG and how this new usage scenario is expected to transform health outcomes and patient care.

Diagnostic tools for post-gastric bypass hypoglycaemia

In spite of its evident success, several late complications can occur after gastric bypass surgery. One of these is post-gastric bypass hypoglycaemia. No evidence-based guidelines exist in the literature on how to confirm the presence of this syndrome. This study aims to describe and compare the tests aimed at making a diagnosis of post-gastric bypass hypoglycaemia and to provide a diagnostic approach based upon the available evidence. A search was conducted in PubMed, Cochrane and Embase. A few questionnaires have been developed to measure the severity of symptoms in post-gastric bypass hypoglycaemia but none has been validated. The gold standard for provocation of a hypoglycaemic event is the oral glucose tolerance test or the liquid mixed meal tolerance test. Both show a high prevalence of hypoglycaemia in post-gastric bypass patients with and without hypoglycaemic complaints as well as in healthy volunteers. No uniformly established cut-off values for glucose concentrations are defined in the literature for the diagnosis of post-gastric bypass hypoglycaemia. For establishing an accurate diagnosis of post-gastric bypass hypoglycaemia, a validated questionnaire, in connection with the diagnostic performance of provocation tests, is the most important thing missing. Given these shortcomings, we provide recommendations based upon the current literature.

Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 Diabetes in Canada

OBJECTIVE

The objective was to simulate and compare clinical and economic outcomes of self-monitoring of blood glucose (SMBG) devices along error ranges and strip price.

METHODS

We programmed a type 1 diabetes natural history and treatment cost-effectiveness model. In phase 1, using past evidence from in silico modeling validated by the Food and Drug Administration, we associated changes in SMBG error to changes in hemoglobin A1c (HbA1c) and separately, changes in severe hypoglycemia requiring an inpatient stay. In phase 2, using Markov cohort simulation modeling, we estimated clinical and economic outcomes from the Canadian payer perspective. The primary comparison was a SMBG device with strip price $0.73 Canadian dollars (CAD) and 10% error (exceeding accuracy requirements by International Organization for Standardization (ISO) 15197:2013) versus a SMBG device with strip price $0.60 CAD and 15% error (accuracy meeting ISO 15197:2013). Outcomes for the average patient, were quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), and budget impact.

RESULTS

Assuming benefits translate into HbA1c improvements only, the ICER with 10% error versus 15% was $11 500 CAD per QALY. Assuming the benefits translate into reduced severe hypoglycemia requiring an inpatient stay only, an SMBG device with 10% error dominated (ie, less costly, more effective) an SMBG device with 15% error. The 3-year budget impact findings ranged from $0.004 CAD per member per month for HbA1c improvements to cost-savings for severe hypoglycemia reductions.

CONCLUSIONS

From efficiency (cost-effectiveness) and affordability (budget impact) payer perspectives, investing in devices with improved accuracy (less error) appears to be an efficient and affordable strategy.