Can one point-of-care glucose meter be used for all pediatric and adult hospital patients? Evaluation of three meters, including recently modified test strips

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.

Approach to the interpretation of unexpected laboratory results arising in the care of patients with inborn errors of metabolism (IEM)

Endocrinologists may encounter abnormal results in routine laboratory tests while caring for patients with inborn errors of metabolism. This article provides a framework for understanding these abnormalities as: a) part of the pathophysiology of the exceptional disease, b) exceptional laboratory errors related to the exceptional disease, or c) routine laboratory errors to which any patient sample is susceptible.

Critical Care Glucose Point-of-Care Testing

Maintaining blood glucose concentration within an acceptable range is a goal for patients with diabetes mellitus. Point-of-care glucose meters initially designed for home self-monitoring in patients with diabetes have been widely used in the hospital settings because of ease of use and quick reporting of blood glucose information. They are not only utilized for the general inpatient population but also for critically ill patients. Many factors affect the accuracy of point-of-care glucose testing, particularly in critical care settings. Inaccurate blood glucose information can result in unsafe insulin delivery which causes poor glucose control and can be fatal. Healthcare professionals should be aware of the limitations of point-of-care glucose testing. This chapter will first introduce glucose regulation in diabetes mellitus, hyperglycemia/hypoglycemia in the intensive care unit, importance of glucose control in critical care patients, and pathophysiological variables of critically ill patients that affect the accuracy of point-of-care glucose testing. Then, we will discuss currently available point-of-care glucose meters and preanalytical, analytical, and postanalytical sources of variation and error in point-of-care glucose testing.

Challenges of inpatient blood glucose monitoring: standards, methods, and devices to measure blood glucose

Glucose control in the hospital setting is very important. There is a high incidence of hyperglycemia, hypoglycemia, and glycemic variability in hospitalized patients. Safe insulin delivery and glucose control is dependent on reliable glucose meters and monitoring systems in the hospital. Different glucose monitoring systems use arterial, venous, central venous, and capillary blood samples. It is important for clinicians to be aware that there are limitations of specific point-of-care (POC) glucose meters and that situations exist whereby POC glucose meters as the sole measurement device should be avoided. POC meter devices are not approved by the Food and Drug Administration for use in critical care, although POC meter devices are commonly used in critical care settings and elsewhere. This review focuses on glucose assay principles, instrument technology, influences on glucose measurement, standards for glucose measurement, and an evaluation of different methods to measure blood glucose in the hospital setting.

Comparison of Point of Care (POC) Testing of Glucose by B Braun Glucometer and Hemocue Glucose 201+ Analyser Versus Centralised Testing in Neonatal Intensive Care Unit (NICU)

BACKGROUND

Neonatal hypoglycemia is the most common carbohydrate metabolic disturbance seen in case of neonates and especially in preterm neonates. Accurate and rapid determination of hypoglycemia and its prompt treatment is of utmost importance to decrease morbidity and mortality of neonates.

AIMS

To estimate blood glucose in neonates and test the efficacy of HemoCue Glucose 201+ analyser and B Braun Glucometer by comparing with centralised laboratory testing. To compare the blood glucose in capillary and venous blood samples of neonates.

SETTINGS AND DESIGN

Hospital setting; Comparative Study Materials and Methods: After obtaining informed consent, all neonates admitted to Neonatal Intensive Care Unit (NICU) were screened for blood sugar. Capillary and venous blood glucose was estimated employing HemoCue Glucose 201+ analyser and B Braun Glucometer. Simultaneously, the same venous sample was collected in fluoride tube and sent to central clinical biochemistry laboratory for glucose estimation. When anaemia or polycythemia was clinically suspected the same venous sample was sent for estimation of Hematocrit (Hct).

STATISTICAL ANALYSIS

Comparison of blood glucose concentration of B Braun glucometer, HemoCue Glucose 201+ analyser and centralis/ed plasma glucose levels was done by using students test. All the statistical analysis were done using software SPSS 6 version.

RESULTS

Mean values of blood glucose (100.2 + 48.4) with B Braun glucometer was significantly higher (p=0.003) when compared to plasma glucose values (76.95 + 45.99) estimated in central laboratory and HemoCue glucose 201+ analyser (82.9 + 51.4). HemoCue glucose 201+ analyser did not show significant difference (p=0.463) with central laboratory testing. There was no significant difference between the capillary and venous sample estimated in both the instruments. Estimation with HemoCue glucose 201+ analyser correlated well with central laboratory testing in neonates with blood glucose <55mg/dl,

CONCLUSION

We conclude that HemoCue glucose 201+ analyser appears to be a suitable point of care (POC) blood glucose measurement device in neonates on both capillary and venous blood samples, as it showed a good correlation with central laboratory values without significant interference from Hct.

Total adiponectin, but not inflammatory markers C-reactive protein, tumor necrosis factor-α, interluekin-6 and monocyte chemoattractant protein-1, correlates with increasing glucose intolerance in pregnant Chinese-Americans

BACKGROUND

Elevated insulin, C-reactive protein (CRP), tumor necrosis factor (TNF)-α, interleukin (IL)-6, and monocyte chemoattractant protein (MCP)-1 levels and decreased high molecular weight adiponectin (HMW-APN) levels have been reported in Caucasians with gestational diabetes mellitus (GDM). No similar studies have been performed in Chinese women.

METHODS

Serum samples were obtained 1 h after a 50-g glucose challenge test (1HGCT) from Chinese-American women at 24-28 gestational weeks and total adiponectin (T-APN), HMW-APN, CRP, TNF-α, IL-6, and MCP-1 concentrations were measured. Correlation coefficients for glucose (1HGCT), HbA1c, insulin, and body mass index (BMI) were calculated against T-APN, HMW-APN, CRP, TNF-α, IL-6, and MCP-1. Significant P-values were determined using Bonferroni adjustments.

RESULTS

Women with GDM had higher insulin and 1HGCT and lower T-APN. In addition, T-APN was lower in non-GDM subjects who had 1HGCT ≥135 mg/dL with no abnormal or one abnormal glucose value on the 3-h oral glucose tolerance test. There were no significant differences in HMW-APN and inflammatory marker levels between non-GDM and GDM groups. There were negative correlations between T-APN and 1HGCT, insulin, BMI, and HbA1c, as well as between HMW-APN and 1HGCT, insulin, and BMI. No significant correlations were observed between 1HGCT, HbA1c, insulin, or BMI and CRP, TNF-α, IL-6, or MCP-1.

CONCLUSIONS

T-APN is reduced in Chinese women with GDM and those without GDM but with evidence of glucose intolerance. Unlike results reported for Caucasians, Chinese-American women with GDM do not exhibit elevated levels of CRP, TNF-α, IL-6, or MCP-1, possibly because Chinese women are relatively leaner compared with Caucasians.

Pediatric hypoglycemia

Hypoglycemia in the pediatric population is a common finding important to recognize and manage to prevent brain injury. Recent advances in molecular genetics have provided new insight into its biochemical and physiologic basis and have led to more appropriate and specific treatment. Although a major cause of brain injury in pediatrics, the ability to predict the long-term outcome in these patients remains difficult. Identification of these at-risk individuals is important. The physiologic adaptations associated with transition from fetal to neonatal life are now better understood thus allowing for improved surveillance and management. Despite these advances, analytical limitations of point-of-care testing instruments at low glucose concentration continue to persist, This review aims to address these questions and provide an overview of pediatric hypoglycemia and the molecular pathways involved.

Assessing the quality of publications evaluating the accuracy of blood glucose monitoring systems

Many studies determine the performance of blood glucose monitoring (BG) systems. Correct evaluation is, however, complex, and apparent contradiction of results creates confusion. This study aimed to provide an overview of frequently made errors and to develop easy-to-use checklists to verify the quality of such studies. Building on the work from Mahoney and Ellison and subsequent re-evaluation, study designs of accuracy studies were assessed, and best practice and internationally accepted norms were determined. Key issues were collated, and two simplified checklists were developed: one for the assessment of analytical accuracy studies and a second for guidance with studies assessing the influence of interferences. The checklists have been used in a feasibility study with 20 representative studies selected from a literature search between 2007 and 2012. This check revealed that limitations in the designs and methods of studies assessing the performance of BG systems are common. The use of the accuracy checklist with the 20 representative studies showed that only 20% were in agreement with most of the issues deemed important and that 40% showed clear nonconcordance with ISO 15197. The use of the interference checklist showed that only 50% of the publications were in good agreement with the quality checks. In agreement with previous studies, which concluded many evaluations are performed poorly and present questionable conclusions, the use of these checklists demonstrated that few publications adhered to international guidelines and recommendations. Taking this into consideration, it becomes obvious that the publications must be examined in more detail to establish their quality and the validity of conclusions drawn.

Accuracy and precision of glucose monitoring are relevant to treatment decision-making and clinical outcome in hospitalized patients with diabetes

BACKGROUND AND METHODS

The accuracy and precision of three blood glucose meters (BGMs) were evaluated in 600 hospitalized patients with type 1 (n = 200) or type 2 (n = 400) diabetes. Capillary blood glucose values were analyzed with Accu-Chek(®) Aviva [Roche (Hellas) S.A., Maroussi, Greece], Precision-Xceed(®) [Abbott Laboratories (Hellas) S.A., Alimos, Greece], and Glucocard X-Sensor(®) (Menarini Diagnostics S.A., Argyroupolis, Greece). At the same time plasma glucose was analyzed using the World Health Organization's glucose oxidase method.

RESULTS

Median plasma glucose values (141.2 [range, 13-553] mg/dL) were significantly different from that produced by the BGMs (P < 0.001). The Accu-Chek Aviva underestimated hypoglycemia (plasma glucose ≤55 mg/dL) by a mean difference of 4.1 mg/dL (95% confidence interval [CI] 0-28 mg/dL), and the Precision-Xceed did so by a mean difference of 6.2 mg/dL (95% CI 0-29 mg/dL); the same was true for the Glucocard X-Sensor by a mean difference of 9.1 mg/dL (CI 0-57 mg/dL) (P < 0.001 for all BGMs). Hyperglycemia (plasma glucose ≥250 mg/dL) was overestimated with the Accu-Chek Aviva and the Precision-Xceed by a mean difference of 4.8 mg/dL (95% CI 0-41 mg/dL) and 10.4 mg/dL (CI 0-92 mg/dL), respectively; the same was true for the Glucocard X-Sensor by a mean difference of 20.3 mg/dL (95% CI 0-100 mg/dL) (P < 0.001 for all BGMs). Asymptomatic hypoglycemia was detected in 28% of type 1 and in 18% of type 2 diabetes patients. In all cases, the BGMs were unreliable in sensing hypoglycemia. Multivariate linear regression analysis demonstrated that low blood pressure and hematocrit significantly affected glucose measurements obtained with all three BGMs (P < 0.05).

CONCLUSIONS

In hospitalized diabetes patients, all three frequently used BGMs undersensed hypoglycemia and oversensed hyperglycemia to some extent. Patients and caregivers should be aware of these restrictions of the BGMs.