Technical evaluation of five glucose meters with data management capabilities

Comparative Accuracy Evaluation of a Blood Glucose Meter With Novel Hematocrit Correction Technology, With Three Currently Used Commercially Available Blood Glucose Monitoring Systems

Hematocrit is known to influence glucose values obtained on some blood glucose meters, with bias observed especially at low and high hematocrit levels. We evaluated the performance of a meter with hematocrit correction technology alongside 3 other commercially available meters. Capillary blood samples from 100 subjects were analyzed in duplicate and compared to the plasma values obtained by reference laboratory analyzer. Bias, error grid, and sensitivity to hematocrit analyses were performed for each meter. Average percentage bias was similar for all meters, however the evaluated meter performed best with respect to error grid analysis, with 100% of values falling within the "no effect on clinical action" and "no risk" categories and did not display any hematocrit associated bias.

Impact of Glucose Meter Error on Glycemic Variability and Time in Target Range During Glycemic Control After Cardiovascular Surgery

BACKGROUND

We retrospectively studied the impact of glucose meter error on the efficacy of glycemic control after cardiovascular surgery.

METHOD

Adult patients undergoing intravenous insulin glycemic control therapy after cardiovascular surgery, with 12-24 consecutive glucose meter measurements used to make insulin dosing decisions, had glucose values analyzed to determine glycemic variability by both standard deviation (SD) and continuous overall net glycemic action (CONGA), and percentage glucose values in target glucose range (110-150 mg/dL). Information was recorded for 70 patients during each of 2 periods, with different glucose meters used to measure glucose and dose insulin during each period but no other changes to the glycemic control protocol. Accuracy and precision of each meter were also compared using whole blood specimens from ICU patients.

RESULTS

Glucose meter 1 (GM1) had median bias of 11 mg/dL compared to a laboratory reference method, while glucose meter 2 (GM2) had a median bias of 1 mg/dL. GM1 and GM2 differed little in precision (CV = 2.0% and 2.7%, respectively). Compared to the period when GM1 was used to make insulin dosing decisions, patients whose insulin dose was managed by GM2 demonstrated reduced glycemic variability as measured by both SD (13.7 vs 21.6 mg/dL, P < .0001) and CONGA (13.5 vs 19.4 mg/dL, P < .0001) and increased percentage glucose values in target range (74.5 vs 66.7%, P = .002).

CONCLUSIONS

Decreasing glucose meter error (bias) was associated with decreased glycemic variability and increased percentage of values in target glucose range for patients placed on intravenous insulin therapy following cardiovascular surgery.

Factors interfering with the accuracy of five blood glucose meters used in Chinese hospitals

BACKGROUND

The prevalence of diabetes is increasing in China. Glucose control is very important in diabetic patients. The aim of this study was to compare the accuracy of five glucose meters used in Chinese hospitals with a reference method, in the absence and presence of various factors that may interfere with the meters.

METHODS

Within-run precision of the meters was evaluated include Roche Accu-Chek Inform®, Abbott Precision PCx FreeStyle®, Bayer Contour®, J&J LifeScan SureStep Flexx®, and Nova Biomedical StatStrip®. The interference of hematocrit level, maltose, ascorbic acid, acetaminophen, galactose, dopamine, and uric acid were tested in three levels of blood glucose, namely low, medium, and high concentrations. Accuracy (bias) of the meters and analytical interference by various factors were evaluated by comparing results obtained in whole blood specimens with those in plasma samples of the whole blood specimens run on the reference method. Impact of oxygen tension on above five blood glucose meters was detected.

RESULTS

Precision was acceptable and slightly different between meters. There were no significant differences in the measurements between the meters and the reference method. The hematocrit level significantly interfered with all meters, except StatStrip. Measurements were affected to varying degrees by different substances at different glucose levels, e.g. acetaminophen and ascorbic acid (Freestyle), maltose and galactose (FreeStyle, Accu-Chek), uric acid (FreeStyle, Bayer Contour), and dopamine (Bayer Contour).

CONCLUSIONS

The measurements with the five meters showed a good correlation with the plasma hexokinase reference method, but most were affected by the hematocrit level. Some meters also showed marked interference by other substances.

Interference studies with two hospital-grade and two home-grade glucose meters

BACKGROUND

Interference studies of four glucose meters (Nova Biomedical [Waltham, MA] StatStrip [hospital grade], Roche Diagnostics [Indianapolis, IN] Accu-Chek Aviva [home grade], Abbott Diabetes Care [Alameda, CA] Precision FreeStyle Freedom [home grade], and LifeScan [Milpitas, CA] SureStep Flexx [hospital grade]) were evaluated and compared to the clinical laboratory plasma hexokinase reference method (Roche Hitachi 912 chemistry analyzer). These meters were chosen to reflect the continuum of care from hospital to home grade meters commonly seen in North America.

METHODS

Within-run precision was determined using a freshly prepared whole blood sample spiked with concentrated glucose to give three glucose concentrations. Day-to-day precision was evaluated using aqueous control materials supplied by each vendor. Common interferences, including hematocrit, maltose, and ascorbate, were tested alone and in combination with one another on each of the four glucose testing devices at three blood glucose concentrations.

RESULTS

Within-run precision for all glucose meters was <5% except for the FreeStyle (up to 7.6%). Between-day precision was <6% for all glucose meters. Ascorbate caused differences (percentage change from a sample without added interfering substances) of >5% with pyrroloquinolinequinone (PQQ)-glucose dehydrogenase-based technologies (Aviva and Freestyle) and the glucose oxidase-based Flexx meter. Maltose strongly affected the PQQ-glucose dehydrogenase-based meter systems. When combinations of interferences (ascorbate, maltose, and hematocrit mixtures) were tested, the extent of the interference was up to 193% (Aviva), 179% (FreeStyle), 25.1% (Flexx), and 5.9% (StatStrip). The interference was most pronounced at low glucose (3.9-4.4 mmol/L).

CONCLUSIONS

All evaluated glucose meter systems demonstrated varying degrees of interference by hematocrit, ascorbate, and maltose mixtures. PQQ-glucose dehydrogenase-based technologies showed greater susceptibility than glucose oxidase-based systems. However, the modified glucose oxidase-based amperometric method (Nova StatStrip) was less affected in comparison with the glucose oxidase-based photometric method (LifeScan SureStep Flexx).

Evaluation of the impact of hematocrit and other interference on the accuracy of hospital-based glucose meters

BACKGROUND

Most glucose meter comparisons to date have focused on performance specifications likely to impact subcutaneous dosing of insulin. We evaluated four hospital-based glucose meter technologies for accuracy, precision, and analytical interferences likely to be encountered in critically ill patients, with the goal of identifying and discriminating glucose meter performance specifications likely to impact intensive intravenous insulin dosing.

METHODS

Precision, both within-run and day-to-day, was evaluated on all four glucose meters. Accuracy (bias) of the meters and analytical interference were evaluated by comparing results obtained on whole blood specimens to plasma samples obtained from these whole blood specimens run on a hexokinase reference method.

RESULTS

Precision was acceptable and differed little between meters. There were significant differences in the degree to which the meters correlated with the reference hexokinase method. Ascorbic acid showed significant interference with three of the four meters. Hematocrit also affected the correlation between whole blood and plasma hexokinase glucose on three of the four glucose meters tested, with the magnitude of this interference also varying by glucose meter technology.

CONCLUSIONS

Correlation to plasma hexokinase values and hematocrit interference are the main variables that differentiate glucose meters. Meters that correlate with plasma glucose measured by a reference method over a wide range of glucose concentrations and minimize the effects of hematocrit will allow better glycemic control for critically ill patients.

Reduction of the interferences of biochemicals and hematocrit ratio on the determination of whole blood glucose using multiple screen-printed carbon electrode test strips

A practical approach to reduce the interferences of biochemicals and hematocrit ratio (Hct%) in the determination of whole blood glucose using multiple screen-printed carbon electrode (SPCE) test strips is described. SPCE test strips with and without glucose oxidase [i.e., GOD(+)-SPCEs and GOD(-)-SPCEs] were used and the chronoamperometric currents of test glucose solutions with various spiked uric acid concentrations and Hct% were measured. By establishing the interference relationships between glucose concentrations and uric acid concentrations as well as Hct% values and with appropriate corrections, the whole blood glucose determinations could be made to be more accurate and comparable to those determined by the reference YSI method. Specifically, the use of the DeltaI value, i.e., the current difference between GOD(+)-SPCE and GOD(-)-SPCE measurements, would reduce most of the uric acid/biochemical interferences. An interpolation method was also established to correct for the glucose determinations with Hct% interferences. The Hct% corrections using the interpolation method are especially important and necessary for those blood samples with glucose concentrations higher than 110 mg dL(-1) and Hct% values lower than 35%. This approach should also be applicable to other biochemical determinations using similar electrochemical techniques.

Plasma-equivalent glucose at the point-of-care: evaluation of Roche Accu-Chek Inform and Abbott Precision PCx glucose meters

BACKGROUND

Glucose testing at the bedside has become an integral part of the management strategy in diabetes and of the careful maintenance of normoglycemia in all patients in intensive care units. We evaluated two point-of-care glucometers for the determination of plasma-equivalent blood glucose.

METHODS

The Precision PCx and the Accu-Chek Inform glucometers were evaluated. Imprecision and bias relative to the Vitros 950 system were determined using protocols of the Clinical Laboratory Standards Institute (CLSI). The effects of low, normal, and high hematocrit levels were investigated. Interference by maltose was also studied.

RESULTS

Within-run precision for both instruments ranged from 2-5%. Total imprecision was less than 5% except for the Accu-Chek Inform at the low level (2.9 mmol/L). Both instruments correlated well with the comparison instrument and showed excellent recovery and linearity. Both systems reported at least 95% of their values within zone A of the Clarke Error Grid, and both fulfilled the CLSI quality criteria. The more stringent goals of the American Diabetes Association, however, were not reached. Both systems showed negative bias at high hematocrit levels. Maltose interfered with the glucose measurements on the Accu-Chek Inform but not on the Precision PCx.

CONCLUSIONS

Both systems showed satisfactory imprecision and were reliable in reporting plasma-equivalent glucose concentrations. The most stringent performance goals were however not met.

A commercial whole blood glucose biosensor with a low sensitivity to hematocrit based on an impregnated porous carbon electrode

Erythrocytes (red blood cells) are a major source of response variation in biosensor electrodes expected to operate in whole blood. Such a blood-to-plasma difference (hematocrit effect) must be minimized for those sensors directed towards the hospital market where wide variations in hematocrit can be seen. Typically, many current glucose sensors demonstrate a decreasing response to the analyte in the presence of increasing hematocrit levels. A sensor electrode for glucose is described which displays a reduced sensitivity to changes in hematocrit. The working electrode comprises a base porous conducting carbon layer, which is impregnated with a mixture including glucose oxidase and a ferrocene redox mediator. The base carbon layer has a void volume of 50%, an average pore diameter of less than 0.1 microm and a thickness of about 20 microm. The interior void volume of the base carbon layer is filled entirely with a substantial proportion of the impregnating mixture such that very little remains on the exterior. The resulting impregnated porous electrode excludes erythrocytes and is consequently capable of operating acceptably in venous, capillary, arterial and neonatal blood over a wide hematocrit range of 20-70%.

Point-of-care testing in diabetes mellitus

Diabetes mellitus is an excellent case study of the evolution, and successful application, of point-of-care testing. It offers a valuable history of the way in which technology has evolved, and continues to evolve, to meet the needs of patients whilst also providing for a more optimal delivery of care. Diabetes mellitus is also a good exemplar of where test and treatment regimes must operate in complete harmony in order to achieve the greatest benefit. Thus whilst the measurement of blood glucose is central to the screening, diagnosis and management of diabetes, it is in the latter use, largely related to supporting compliance with therapy, that point-of-care testing is of greatest relevance. In addition, there are other tests, more associated with the management of diabetes and early detection of the complications associated with diabetes, that are appropriate to the point-of-care testing modality. This Review will focus on the developments in technology and the harnessing of this innovation to support the delivery of clinical, organisational and economic benefits in the care of patients with diabetes mellitus.

Biosensors in clinical chemistry

Biosensors are analytical devices composed of a recognition element of biological origin and a physico-chemical transducer. The biological element is capable of sensing the presence, activity or concentration of a chemical analyte in solution. The sensing takes place either as a binding event or a biocatalytical event. These interactions produce a measurable change in a solution property, which the transducer converts into a quantifiable electrical signal. Present-day applications of biosensors to clinical chemistry are reviewed, including basic and applied research, commercial applications and fabrication techniques. Recognition elements include enzymes as biocatalytic recognition elements and immunoagents and DNA segments as affinity ligand recognition elements, coupled to electrochemical and optical modes of transduction. The future will include biosensors based on synthetic recognition elements to allow broad applicability to different classes of analytes and modes of transduction extending lower limits of sensitivity. Microfabrication will permit biosensors to be constructed as arrays and incorporated into lab-on-a-chip devices.

Comparison of point-of-care-testing glucose meters with standard laboratory measurement of the 50g-glucose-challenge test (GCT) during pregnancy

OBJECTIVES

Although glucose meters are well-established instruments for self-monitoring blood glucose levels, diagnostic and screening procedures should be performed using standard laboratory methods. In addition to standard laboratory methods, HemoCue is authorized for screening and diagnostic purposes in Germany. The rapid development of other glucose meters makes it necessary to re-evaluate this recommendation. Our objective was to test the usefulness of glucose meters in screening pregnant patients for gestational diabetes.

METHODS

The 50-g glucose challenge test was administered to one hundred and ninety-three pregnant patients whose blood glucose levels were then simultaneously measured with five portable meters and the HemoCue. The results were compared to our standard method (Hexokinase). A cut-off of 7.8 mmol/L was used and sensitivity, specificity, accuracy, the Youden index, and the Kappa index were calculated. The tests were performed by well-trained personnel (C.D. and U.S.).

RESULTS

1212 measurements were performed on 193 patients. All glucose meters showed a very good correlation (r > 0.90). None of the measurements showed an extreme deviation necessitating the error grid analysis. The GlucoTouch (5.93% +/- 7.4) and the HemoCue (-9.04% +/- 5.9) showed a mean deviation greater than 5%. None of the meters had a mean deviation greater than 10%. The accuracy fluctuated between 0.85 and 0.94. The Kappa index was between 66 to 85. In our clinical trial, the Accu-Chek, Glucotouch, OneTouch, and Precision demonstrated greater accuracy and a higher Kappa index than the HemoCue.

CONCLUSIONS

Our data showed good concordance in statistical and clinical parameters for most of the six glucose meters. The HemoCue, recommended as a standard method in several countries, did not show better concordance than most of the tested glucose meters. When used by well-trained personnel, the accuracy of the Accu-Chek, Glucotouch, One-Touch, and Precision was acceptable for use in gestational diabetes screening.

Comparison of several point-of-care testing (POCT) glucometers with an established laboratory procedure for the diagnosis of type 2 diabetes using the discordance rate. A new statistical approach

The applicability of point-of-care testing (POCT) glucometers for monitoring blood glucose concentrations has been demonstrated. However, their use in diagnosing type 2 diabetes is still debated. Therefore, a new statistical procedure for estimating discordance rates (DRs) was applied in comparing a well-established laboratory method (Ebio) with another laboratory method (Cobas Integra 700) and with several POCT glucometers (Accu-Chek, Accutrend, Elite, HemoCue, Omni) in detecting glucose intolerance states. All procedures led to parallel glucose concentration patterns in capillary blood, venous plasma, and venous blood during oral glucose tolerance tests. However, the mean concentrations differed more or less. The Ebio and Integra results agreed within a maximal deviation of 3%. In blood samples, the HemoCue and Accutrend results were closest to the laboratory procedures (Ebio and Integra) and the highest differences were obtained with the Elite. Comparing whole blood values with those obtained in the aqueous blood compartment (Omni), even greater differences were observed. When all procedures were referred to the same glucose standard, the Ebio, Integra, Accutrend, and Omni results remained almost unchanged, whereas the Elite "moved" toward the Ebio results, and the Accu-Chek results toward the Omni results. Thus, traceability to an aqueous standard was observed with the Ebio, Integra, Accutrend, and Elite in all three sample systems. The Accu-Chek was only traceable in the presence of albumin, and HemoCue was not traceable at all. The clinical relevance of the differences observed between Ebio and POCT glucometers was tested by comparing the relative number of discordant classifications. The highest DRs were observed in the fasting state. They were higher in capillary blood than in the other sample systems. The DRs were found higher with POCT glucometers than with the other established laboratory procedure (Integra). Thus, at least in the fasting state, all POCT glucometers were less reliable than the established laboratory procedures and above the chosen criteria of clinical acceptability (DR < or = 5%). After transforming all glucometer results with a regression function (bias correction), the DRs were less than 5% if compared with the Ebio procedure in all sample systems. In conclusion, the WHO recommendation not to use POCT glucometers for diagnosing type 2 diabetes must be supported. However, after proper recalibration, the tested systems were acceptable. Therefore, manufacturers should reconsider their calibration procedure. Those POCT procedures should be preferred that can be referred to aqueous glucose solutions.

Performance evaluation of blood glucose monitoring devices

Many new technologies are being applied to measure blood glucose concentrations, but there is a lack of a standardized approach to evaluate performance of these devices. We sought to identify the key elements in evaluating the performance of devices for measuring blood glucose. We examined these elements in a multicenter study of four brands of glucose meters that are commonly used by diabetic patients. We tested control materials, spiked whole blood specimens, and 461 heparinized whole blood specimens in triplicate by each of the four brand glucose meters, and analyzed the plasma glucose concentrations of these specimens by a hexokinase (HK) method that incorporated reference materials developed by National Institute of Standards and Technology. Testing with glucose meters was performed at three sites, with multiple operators, meters, and representative lots of reagents. We evaluated the systematic bias, random error, and clinical significance of glucose meters. Meters were precise with a coefficient of variation of <4% across a wide range of glucose concentrations. Slopes significantly different from 1.0 were observed for two meters with 11-13% and -11% to -13% at the 95% confidence interval level by the linear regression of meter results versus the HK method from 33 to 481 mg/dL (correlation coefficient >0.98 and standard error of estimation S(y/x) <13 mg/dL for both meters). Analysis of the clinical significance of bias by Clarke Error Grid showed that results of the four meters were outside the accurate zone (26.5%, 2.4%, 1.5%, and 5.6%). Only a small number of the results showed clinically significant bias, mostly in the hypoglycemic range. Meters performed consistently throughout the study and, generally, were precise, although precision varied at extremely high or low glucose concentrations. Two of the glucose meters had substantial systematic bias when compared with an HK method, indicating a need for improving calibration and standardization. Analytical performance varied over the physiological range of glucose values so that separate accuracy and precision goals should be defined for hypoglycemic, normoglycemic, and hyperglycemic ranges. This study describes the current state of performance of blood glucose monitoring devices and points out those factors that should be assessed during evaluation of new devices.

An evaluation of three glucose meter systems and their performance in relation to criteria of acceptability for neonatal specimens

BACKGROUND

Prior to making a selection for our hospital, a pediatric institution, we deemed it necessary to evaluate, concurrently, three recently available glucose meter systems, claimed to be suitable for use with neonatal samples.

METHODS

Comparisons were with laboratory plasma analyses. Linearity and precision were also determined.

RESULTS

All meters gave linear responses. Precision determined using quality control material was acceptable. For an in-laboratory side-by-side evaluation, meter 1 showed a small bias but significant result scatter while meter 3 showed a negative bias; mean differences from reference (S.D.) were: -0.30 mmol/l (0.56), 0.06 mmol/l (0.39) and -0.49 mmol/l (0.35) for meters 1, 2 and 3, respectively. Clinical unit testing results gave mean differences from reference (S.D.) of: -0.19 mmol/l (0.56), 0.06 mmol/l (0.48) and -0.12 mmol/l (0.48) for meters 1, 2 and 3, respectively. Using +/- 15% of reference as acceptability thresholds, 61%, 79% and 72% of results for meters 1, 2 and 3 respectively, were within limits. At +/- 20%, the corresponding figures were 81%, 90% and 91%, respectively. All meters showed a sample-hematocrit effect with either negative (meters 1 and 3) or positive (meter 2) bias.

CONCLUSIONS

Regardless of the performance criteria chosen, meter 1 had the worst performance while meter 2 was slightly better in overall than meter 3. Based on performance, general characteristics and user feedback, meter 2 was selected by us. In light of our results, we nonetheless suggest that performance of the meters tested is less than ideal, especially in the context of clinical utility in neonates.

Instruments for Self-Monitoring of Blood Glucose: Comparisons of Testing Quality Achieved by Patients and a Technician

Background: Instruments for self-monitoring of blood glucose (SMBG) are increasingly used by patients with diabetes. The analytical quality of meters in routine use is poorly characterized.

Methods: We compared SMBG performance achieved by patients and by a medical laboratory technician. Imprecision was calculated from duplicate measurements, and deviation as the difference between the first measurement and the mean of duplicate laboratory-method results (calibrated with NIST material). Analytical quality for five groups of SMBG instruments was compared with quality specifications for BG measurements. All participants completed a questionnaire assessing both SMBG training and use of the meters.

Results: We recruited 159 SMBG users from a hospital outpatient clinic and 263 others from 65 randomly selected general practices (total of 422). Most (two thirds) used insulin. CVs for the five meter types were 7%, 11%, 18%, 18%, and 20% in the hands of patients and 2.5–5.9% for the technician. For three of five meter types, patients’ BG measurements had larger deviations from the laboratory results than did the technician’s results. The technician’s performance could not predict the patients’. No instrument when used by patients (but two operated by the technician) met published quality specifications. The analytical quality of patients’ results was not related to whether they had chosen the instruments on advice from healthcare personnel (one-third of patients), were only self-educated in SMBG (50%), or performed SMBG fewer than seven times/week (62%).

Conclusions: The analytical quality of SMBG among patients was poorer than, and could not be predicted from, the performance of the meters in the hands of a technician. We suggest that new instruments be tested in the hands of patients who are trained on meter use in a routine way.

Accuracy and precision of point-of-care testing for glucose and prothrombin time at the critical care units

The use of point-of-care testing (POCT) in critical care patient units has continued to increase since the 1980s. This increase is due to the need for prompt therapeutic interventions that may impact mortality and morbidity, and reduce the overall cost of healthcare for critically ill patients. The diagnostic manufacturing industry has risen to this challenge by introducing portable and/or handheld analyzers for use at the point-of-care. In order to ensure the public safety in the USA, the Food and Drug Administration (FDA) must approve the use of each POCT analyzer. The FDA approval is based on established performance criteria that includes relative accuracy and precision documentation. This study evaluated the precision and accuracy of the POCT prothrombin time and glucose analyzers relative to the manufacturers' specifications, to the internal QC in the main laboratory, and to the results of the external proficiency-testing program. The QC for the prothrombin time had a precision that ranged from 2.84% to 3.45% (POCT) and from 1.27-1.66% (main laboratory). The precision for the glucose QC ranged from 5% to 5.2% (POCT) and 0.9-2.7% (main laboratory). Using the results of the external proficiency testing, the inter-laboratory CV% for the POCT prothrombin time ranged from 3.5% to 5.0% and the main laboratory had a range of 2.5-2.9%. The inter-laboratory CV% ranges for glucose POCT and the main laboratory were 4.9-10.6% and 1.8-3.5%, respectively. The main laboratory analyzers proved to be more accurate than the POCT analyzers as indicated by comparison to the mean prothrombin time and glucose results of all participating laboratories in the proficiency testing program.

Oxygen effects on glucose meter measurements with glucose dehydrogenase- and oxidase-based test strips for point-of-care testing

OBJECTIVES

To determine the effects of different oxygen tensions (Po2) on glucose measurements with glucose dehydrogenase (GD)-based and glucose oxidase (GO)-based test strips, to quantitate changes in glucose measurements observed with different Po2 levels, and to discuss the potential risks of oxygen-derived glucose errors in critical care.

DESIGN

Venous blood from healthy volunteers was tonometered to create different oxygen tensions simulating patient arterial Po2 levels. Venous blood from diabetic patients was exposed to air to alter oxygen tensions simulating changes in Po2 during sample handling. Whole-blood glucose measurements obtained from these samples with six glucose meters were compared with reference analyzer plasma glucose measurements. Glucose differences were plotted vs. different Po2 levels to identify error trends. Error tolerances were as follows: a) within +/-15 mg/dL of the reference measurement for glucose levels <or=100 mg/dL; and b) within +/-15% of the reference measurement for glucose levels >100 mg/dL.

SETTING AND SUBJECTS

Five healthy volunteers in the bench study and 11 diabetic patients in the clinical study.

RESULTS

In the bench study, increases in Po2 levels decreased glucose measured with GO-based amperometric test strips, mainly at Po2 levels >100 torr. At nearly constant glucose concentrations, glucose meter systems showed large variations at low (39 torr) vs. high (396 torr) Po2 levels. Glucose measured with GD-based amperometric and GO-based photometric test strips generally were within error tolerances. In the clinical study, 31.6% (Precision PCx), 20.2% (Precision QID), and 23.0% (Glucometer Elite) of glucose measurements with GO-based amperometric test strips, 14.3% (SureStep) of glucose measurements with GO-based photometric test strips, and 4.6% (Accu-Chek Advantage H) and 5.9% (Accu-Chek Comfort Curve) of glucose measurements with GD-based amperometric test strips were out of the error tolerances.

CONCLUSIONS

Different oxygen tensions do not significantly affect glucose measured with the GD-based amperometric test strips, and have minimal effect on GO-based photometric test strips. Increases in oxygen tension lowered glucose measured with GO-based amperometric test strips. We recommend that the effects of different oxygen tensions in blood samples on glucose measurements be minimized by using oxygen-independent test strips for point-of-care glucose testing in critically ill and other patients with high or unpredictable blood Po2 levels.

IFCC recommendation on reporting results for blood glucose

In human beings, glucose is distributed like water between erythrocytes and plasma. The molality of glucose (amount of glucose per unit water mass) is the same throughout the sample. Different water concentrations in calibrator, plasma, and erythrocyte fluid can explain some differences that are dependent on sample type, methods requiring sample dilution, and direct reading biosensors detecting molality. Different devices for the measurement of glucose detect and report fundamentally different analytical quantities. The differences exceed the maximum allowable error of glucose determinations for diagnosing and monitoring diabetes mellitus, and they complicate the treatment. The goal of the International Federation of Clinical Chemistry, Scientific Division, Working Group on Selective Electrodes (IFCC-SD WGSE) is to reach a global consensus on reporting results. The document recommends harmonizing to the concentration of glucose in plasma (with the unit mmol/l), irrespective of sample type or technology. A constant factor of 1.11 will convert measured concentration in whole blood to the equivalent concentration in plasma.

Development of a universal connectivity and data management system

Point-of-care testing (POCT) is an increasingly popular method of delivering laboratory testing. Management of POCT is challenging given the variety of devices, locations, and staff that need to be coordinated to ensure quality results and meet regulatory guidelines. Electronic capture and transfer of data are preferred for managing POCT, but there is currently no standard method of connecting different devices. Johns Hopkins Medical Institutions (JHMI) developed a common data management system with interfaces to all of its POCT devices. All POCT data are collected in one database and analyzed in a similar fashion. Where data were once collected by carrying laptops to each nursing unit, the POCT devices can now connect directly to the database over the Internet. Algorithms have been created to automate the data analysis and review process. Over the several years that this software has been used, JHMI has experienced improved quality, accuracy, and management of its POCT program. The labor saved by increased automation of data review is refocused on enhancing the performance and scope of the program. Current connectivity and analysis algorithms have future application to remote consultation, management of home self-monitoring patients, and examination of real-time data.

Status of point-of-care testing: promise, realities, and possibilities

Point-of-care testing (POCT) has evolved from the demand for analytical information more rapidly than is available from central laboratories. By bringing the analysis closer to the patient several process steps have been eliminated, facilitating a shorter time to result and faster management response with improved outcomes. Thus benefits include better therapeutic turnaround times, decreased blood loss as a result of reduced phlebotomy secondary to clinical improvement, and diminished resource utilization. These advantages depend on acceptable analytical performance in comparison with central laboratory methods and in relation to clinical criteria. Generally these requirements are met but there are problems particularly with atypical specimens. Outcomes and cost-benefit analyses have been difficult to perform and evaluate. Given the multitude of participants, quality assurance and program management are recognized as resource intensive. However, recognition of problem areas is driving continuous improvement and we envisage expansion of this paradigm.

Point-of-care glucose testing: effects of critical care variables, influence of reference instruments, and a modular glucose meter design

OBJECTIVE

To assess the clinical performance of glucose meter systems when used with critically ill patients.

DESIGN

Two glucose meter systems (SureStepPro and Precision G) and a modular adaptation (Immediate Response Mobile Analysis-SureStepPro) were assessed clinically using arterial samples from critically ill patients. A biosensor-based analyzer (YSI 2700) and a hospital chemistry analyzer (Synchron CX-7) were the primary and secondary reference instruments, respectively.

PATIENTS AND SETTING

Two hundred forty-seven critical care patients at the University of California, Davis, Medical Center participated in this study.

OUTCOME MEASURES

Error tolerances of +/-15 mg/dL for glucose levels </=100 mg/dL and +/-15% for glucose levels >100 mg/dL were used to evaluate glucose meter performance; 95% of glucose meter measurements should fall within these tolerances.

RESULTS

Compared to the primary reference method, 98% to 100% of SureStepPro and 91% to 95% of Precision G measurements fell within the error tolerances. Paired differences of glucose measurements versus critical care variables (Po(2), pH, Pco(2), and hematocrit) were analyzed to determine the effects of these variables on meter measurements. Po(2) and Pco(2) decreased Precision G and SureStepPro measurements, respectively, but not enough to be clinically significant based on the error tolerance criteria. Hematocrit levels affected glucose measurements on both meter systems. Modular adaptation did not affect test strip performance.

CONCLUSIONS

Glucose meter measurements correlated best with primary reference instrument measurements. Overall, both glucose meter systems showed acceptable performance for point-of-care testing. However, the effects of some critical care variables, especially low and high hematocrit values, could cause overestimated or underestimated glucose measurements.

[Evaluation of the Glucocard Memory 2 analyzer for measuring glucose concentration in capillary blood]

OBJECTIVE

To evaluate the analytical reliability and accuracy as well as the practicability of the Glucocard Memory 2 glucose meter, intended to the control of the diabetic patient.

DESIGN

Descriptive, crossover study. To validate an analytical instrument according to guidelines of the Spanish Society of Clinical Biochemistry and Molecular Pathology.

SETTING

Primary health care, urban setting.

PARTICIPANTS

Ninety-three blood samples from diabetic patients were used. These samples were selected by a consecutive sampling of the tubes received in the laboratory for the diabetes follow-up protocol.

MEASUREMENTS AND MAIN RESULTS

Repeatability of the system was studied analysing the within-run precision at four concentrations of glucose. We obtained coefficients of variation between 2.12% (at 410 mg/dl of glucose) and 4.17% (at 37.2 mg/dl). The linearity study allowed to check experimentally the linear response of the instrument between 27 and 485 mg/dl. The accuracy was evaluated comparing the Glucocard results with the routine procedure of our laboratory (Hitachi 747, GOD-PAP) and calculating the regression parameters with the Passing and Bablok method (y = 1.01 x -2.34) and the intraclass correlation (99%). To evaluate the clinical significance of possible deviations related with the reference laboratory method the "error Grid" analysis was used. This analysis showed that 100% of Glucocard Memory 2 results fell into the clinical accuracy zone. Practicability study showed that the instrument is very simple to use.

CONCLUSIONS

Glucocard Memory 2 is a glucose meter intended to the measurement of glucose both on capillary and venous blood that, besides its extreme simplicity of use, shows very good analytical features.

Oxygen effects on glucose measurements with a reference analyzer and three handheld meters

Oxygen may affect glucose meter and reference analyzer measurements. We evaluated the effects of changes in blood oxygen tension (Po2) on Accu-Chek Comfort Curve (Roche Diagnostics, Indianapolis, IN), Precision G, (Abbott Laboratories, Bedford, MA) and One Touch II (Lifescan, Milpitas, CA) glucose meter measurements, and on Yellow Springs Instruments (YSI) (Yellow Springs, OH) reference analyzer measurements. Venous blood drawn from healthy volunteers was adjusted to three glucose levels of 80, 200, and 400 mg/dL, each tonometered with six different Po2 levels (40, 80, 160, 240, 320, and 400 torr). To quantitate oxygen effects on reference analyzer measurements, glucose differences between test sample (Po2 changed) and control (Po2 80 torr) were calculated (YSItest-YSIcontrol). The threshold for determination of oxygen effects was +/-2 SD, where 2 SD was fro