Glucose Meter Performance Research Articles

Commutable Blood Materials from the Fixed-Cell Method for Performance Evaluation of Blood Glucose by a Glucose Meter.

Glucose meters provide a rapid blood glucose status for evidence-based diagnosis, monitoring, and treatment of diabetes mellitus. We aimed to evaluate the commutability of processed blood materials (PBMs) and their use in the performance evaluation of glucose meters. Two PBMs obtained by the fixed-cell method were analyzed for homogeneity, stability, and commutability. The compatibility of ten pairs between mass spectrometry and each glucose meter was categorized as compatible (mean paired difference ≤ 5%) and incompatible (mean paired difference > 5%). The performance of glucose meter 1 (n = 767) and glucose meter 2 (n = 266) was assessed. The glucose in the PBMs remained homogenized and stable for at least 180 days. Six out of ten pairs had commutable PBMs. Commutability of PBMs was observed in both well-compatible and incompatible glucose results. Target glucose values from mass spectrometry were significantly different (p ≤ 0.05) from consensus values in one group of glucose meters. When commutable PBMs were used, glucose meter 1 showed better performance than glucose meter 2, and the percentage of satisfaction was associated when using target values for glucose from mass spectrometry and consensus values, but the performance of glucose meter 2 was not associated. PBM from a fixed-cell method could be mass produced with acceptable homogeneity and stability. Commutability testing of PBMs is required prior to use in the performance evaluation of glucose meters, as the commutability of glucose in the PBMs obtained by a fixed-cell method was variable and depended on the individual glucose meter.

Comparison of POCT glucose meters and analysis of the interference factor

Abstract Objectives Not many reports have covered large-scale point of care testing (POCT) blood glucose comparisons, and many interfering factors affect detection. This study aims to verify the performance of POCT blood glucose meters and discusses the factors that interfere with detection. Methods Accuracy and precision verification in five glucose concentration groups-high 1 (H1), High 2 (H2), medium 1 (M1), medium 2 (M2), and low (L); comparison of different test methods and specimens; and also the influence of iodophor was investigated in a dilution experiment. Results A total of 58 out of 64 Accu-Chek Inform II POCT blood glucose meters (ACI II) qualified for testing. A proportional significant difference in the relative bias was observed with the POCT instruments in the intermediate and high glucose concentration groups (H=15.364, p=0.02). There were significant differences among the five groups with compliance rates (χ 2=21.03, p=0.00); Group L showed higher values than groups H1 and H2. The precision verification met the requirements issued by the Consensus. Significant differences were found between the three detection methods. The measurement of the Glucose Oxidase Method (Cobas B 123) was lower than that of the HITACHI Plasma Hexokinase Method and the Glucose Dehydrogenase Method on the ACI II (p=0.005 and 0.003) in the preliminary study. No differences were seen among the three types of specimens (p>0.05). The glucose results were incorrect in the presence of iodophor interference. Conclusions The ACI II and Cobas B123 (with a slightly negative bias) provide sufficiently accurate measurements, and all types of blood specimens can be applied. Iodophor, a disinfectant, interferes with glucose measurement.

User competency is still a major factor affecting analytical performance of glucose meters in patient service

ObjectivesThis study investigated the analytical performance of glucose meters while in use for 1.5 years in a hospital setting. The effect of user competency, strip stability and strip lot number variation on the performance of blood glucose meters was investigated. Design and methodsThe studies for linearity, accuracy, imprecision, and method comparison were conducted according to Clinical and Laboratory Standards Institute (CLSI) guidelines Point of Care Testing (POCT) 12-A3. Proficiency testing results were used for meter-meter comparison. Quality control (QC) data, strip lot and users' identification were extracted from the data management system 1.5 years after the new systems were implemented. The frequency of users QC testing was used as the indicator of user competency. ResultsThe average total imprecision based on 20 glucose meters with QC materials targeting 2.5 mmol/L and 17.05 mmol/L increased by 2.4 fold and 1.83 fold, respectively, over that from initial evaluation studies. When the glucose levels were <5.6 mmol/L, the absolute bias ranged from −0.75 to +0.55 mmol/L relative to the core laboratory analyzer and the accuracy goal was met by 96.3% of samples. When the glucose levels were ≥5.6 mmol/L, the percentage bias ranged from −17.8% to +9.4% and 79.3% of samples met the accuracy goal. The users who performed QC checks less frequently (<10 times/year) showed significantly different QC mean and greater variation than users who performed QC checks >20 times/year, especially for QC targeting 2.5 mmol/L. ConclusionsPoorer analytical imprecision of POCT blood glucose meters following introduction to patient service is mainly due to the low level of user competency.

Assessment of analytical performance of glucose meter in pediatric age group at tertiary care referral hospital

BackgroundGlucometers are the excellent tools for self-monitoring of blood glucose (SMBG). They are important especially in the circumstances where continuous monitoring is mandatory and at decision making levels. Tight glycemic control protocols are important for preventing the ill effects of fluctuating glucose levels. This increases the use of glucometers in various healthcare settings. As technology advances, glucometers are getting better in terms of quality of results. But still some lacunae are there.MethodsPresent study was conducted in the tertiary care referral hospital. One hundred twenty five patients were recruited from pediatric wards. Bland-Altman plot, Parke error grid and Surveillance error grid analysis were used for comparing results of glucose meter with that of standard laboratory method.ResultsIt is found that there is significant difference between the results by two methods. Though minimal but glucose meter results deviate from the results of standard lab method. This will affect the overall patient care especially in emergency conditions.ConclusionsThis study is the first of its kind as no similar studies have been reported in the pediatric population. For effective use of glucose meter it should give as accurate as possible estimate of actual glucose levels. Results should not only be accurate but also precise without which critical errors may be possible. We recommend that for any glucose meter there should be regular maintenance as well as calibration is to be done. So that agreement with reference laboratory method is maintained and effective medical decisions are made.

Bedside Glucose Monitoring-Is it Safe? A New, Regulatory-Compliant Risk Assessment Evaluation Protocol in Critically Ill Patient Care Settings.

New data have emerged from ambulatory and acute care settings about adverse patient events, including death, attributable to erroneous blood glucose meter measurements and leading to questions over their use in critically ill patients. The U.S. Food and Drug Administration published new, more stringent guidelines for glucose meter manufacturers to evaluate the performance of blood glucose meters in critically ill patient settings. The primary objective of this international, multicenter, multidisciplinary clinical study was to develop and apply a rigorous clinical accuracy assessment algorithm, using four distinct statistical tools, to evaluate the clinical accuracy of a blood glucose monitoring system in critically ill patients. Observational study. Five international medical and surgical ICUs. All patients admitted to critical care settings in the centers. None. Glucose measurements were performed on 1,698 critically ill patients with 257 different clinical conditions and complex treatment regimens. The clinical accuracy assessment algorithm comprised four statistical tools to assess the performance of the study blood glucose monitoring system compared with laboratory reference methods traceable to a definitive standard. Based on POCT12-A3, the Clinical Laboratory Standards Institute standard for hospitals about hospital glucose meter procedures and performance, and Parkes error grid clinical accuracy performance criteria, no clinically significant differences were observed due to patient condition or therapy, with 96.1% and 99.3% glucose results meeting the respective criteria. Stratified sensitivity and specificity analysis (10 mg/dL glucose intervals, 50-150 mg/dL) demonstrated high sensitivity (mean = 95.2%, SD = ± 0.02) and specificity (mean = 95. 8%, SD = ± 0.03). Monte Carlo simulation modeling of the study blood glucose monitoring system showed low probability of category 2 and category 3 insulin dosing error, category 2 = 2.3% (41/1,815) and category 3 = 1.8% (32/1,815), respectively. Patient trend analysis demonstrated 99.1% (223/225) concordance in characterizing hypoglycemic patients. The multicomponent, clinical accuracy assessment algorithm demonstrated that the blood glucose monitoring system was acceptable for use in critically ill patient settings when compared to the central laboratory reference method. This clinical accuracy assessment algorithm is an effective tool for comprehensively assessing the validity of whole blood glucose measurement in critically ill patient care settings.

WITHDRAWN: Performance of Community Blood Glucose Meters in Calgary, Alberta: An Analysis of Quality Assurance Data.

The self-monitoring of blood glucose plays a critical role in management of diabetes mellitus. Although laboratory comparisons of glucose meter accuracy are often acceptable, clinical comparisons show frequent inaccuracies. In this paper, we evaluate the accuracy of self-monitoring blood glucose meters using glucose meter and serum comparisons from a large Canadian laboratory. This study was performed using secondary data obtained from the Laboratory Information System of Calgary Services, the sole provider of laboratory testing to Calgary and surrounding areas. We examined anonymous quality assurance data for glucose meter comparisons performed on home glucose meters between January 1, 2010, and April 30, 2013. A total of 39 542 comparisons were recorded on 18 540 different subjects. Overall, 6.7% of differences were greater than the current International Standards Organization standard of 15%, and 3.7% exceeded the Canadian guideline of 20%. Glucose meter checks were infrequently performed (on average, once per 1.6 years). A significant subset of meter results was inaccurate.

Advances in Patient Self-Monitoring of Blood Glucose.

In 2 articles of the present issue, Bendini et al report about performance results obtained with 2 blood glucose monitoring systems of the Contour Next platform. Using several analysis methods, the authors demonstrate a very high accuracy, which meets all actual regulatory performance criteria. With consistent MARD results < 5% under daily routine conditions, this meter platform is finally fulfilling the accuracy request as set forth by the American Diabetes Association already in the late 1980s. This meter platform is representative for the successful effort of the device manufacturers who were consequently improving the analytical performance of blood glucose meters during the Past 2 decades, starting with an MARD of 12-15% at the end of the past century and reaching an excellent accuracy < 5% today.

Short-Term Thermal-Humidity Shock Affects Point-of-Care Glucose Testing

The objective was to assess the effects of short-term (≤1 hour) static high temperature and humidity stresses on the performance of point-of-care (POC) glucose test strips and meters. Glucose meters are used by medical responders and patients in a variety of settings including hospitals, clinics, homes, and the field. Reagent test strips and instruments are potentially exposed to austere environmental conditions. Glucose test strips and meters were exposed to a mean relative humidity of 83.0% (SD = 8.0%) and temperature of 42°C (107.6°F, SD = 3.2) in a Tenney BTRC environmental chamber. Stressed and unstressed glucose reagent strips and meters were tested with spiked blood samples (n = 40 measurements per time point for each of 4 trials) after 15, 30, 45, and 60 minutes of exposure. Wilcoxon's signed rank test was applied to compare measurements test strip and meter measurements to isolate and characterize the magnitude of meter versus test strip effects individually. Stressed POC meters and test strips produced elevated glucose results, with stressed meter bias as high as 20 mg/dL (17.7% error), and stressed test strip bias as high as 13 mg/dL (12.2% error). The aggregate stress effect on meter and test strips yielded a positive bias as high as 33 mg/dL (30.1% error) after 15 minutes of exposure. Short-term exposure (15 minutes) to high temperature and humidity can significantly affect the performance of POC glucose test strips and meters, with measurement biases that potentially affect clinical decision making and patient safety.

Performance of blood glucose meters in compliance with current and future clinical ISO15197 accuracy criteria

Since 2003, blood glucose meters for patient self testing are approved in Europe based on the accuracy performance criteria as defined by the ISO15197 guideline. A new draft ISO guideline is currently under regulatory review, which suggests more strict accuracy acceptance criteria, and which may not be entirely fulfilled by currently commercialized blood glucose meter systems. In order to investigate the compliance of BG*Star and iBG*Star and several other blood glucose meters with the new draft ISO guideline, we performed a post-hoc analysis of data obtained from a recently performed ISO-conforming clinical accuracy performance study. This study was performed with 106 patients, clinically presenting with blood glucose levels distributed over the entire measurement range and in line with the glucose distribution requirements as demanded by the guideline. The YSI 2300 STAT Plus analyzer (glucose oxidase) served as reference method.While all tested meters had been in a high degree of compliance with the current ISO criteria, performance was lower when analyzed in accordance with the new acceptance criteria (95% of readings have to be within ±15 mg/dL for values <100 mg/dL, and within ±15% for values ≥100 mg/dL). The following meters met the new criteria: Accu-Chek Aviva (95.5%/98.6%), BG*Star (98.5%/97.3%), iBG*Star (98.5%/97.3%), FreeStyle Freedom Lite (95.5%/96.6%), and OneTouch Ultra2 (95.5%/96.5%). One meter failed with low blood glucose values (Contour: 90.9%/95.9%).In conclusion, BG*Star and iBG*Star and several other branded meters met the new draft ISO15197 acceptance criteria, when tested in accordance with the instructions for use and with the ISO accuracy testing protocol in a clinical setting.

Two site evaluation of the performance of a new generation point-of-care glucose meter for use in a neonatal intensive care unit

Monitoring of blood glucose in neonatal intensive care unit (NICU) patients is important in maintaining normoglycaemia and reducing the risk of hypoglycaemia. Point-of-care testing (POCT) glucose meters provide short turnaround times but some have been reported to be affected by haematocrit interference and other biochemical or biological substances in their accuracy and performance. The aim of this study was to assess the performance of a new POCT glucose meter in a challenging preterm neonatal population. The new Nova Biomedical StatStrip™ (Nova Biomedical) was tested on 159 heparinised whole blood samples from NICU patients obtained for blood gas analysis. Accuracy (bias) of the meter and analytical interferences were evaluated by comparing the results of the meter with the results of the blood gas analyser routinely used for glucose measurements in this NICU setting. The results of the StatStrip glucose meter correlated very well with the reference routine method across a wide glucose concentration range (13-389 mg/dL) and were not affected by the level of haematocrit, by sample pH or by medication. The StatStrip meter showed good clinical accuracy and performance for measuring and monitoring glucose levels in NICU patients, with special respect to preterm infants, and therefore can act as a perfect alternative to a blood gas analyser for measuring blood glucose in NICU patients.

Evaluation of the Analytical Performance of the Coulometry-Based Optium Omega Blood Glucose Meter

The goal of diabetes treatment is maintaining near normoglycemia based on self-monitoring of blood glucose (SMBG). In this study, an evaluation of the analytical performance of the coulometry-based Optium Omega™ glucose meter designed for SMBG has been carried out. The assessment of precision and between-lot variability was based on glucose measurements in ethylene-diaminetetraacetic acid venous blood samples. Glucose concentrations measured in 289 fresh capillary blood samples using the Omega glucose meter and the Biosen C_line analyzer were compared. Within-run imprecision coefficient of variation for the lower and higher glucose concentrations amounted to 5.09 and 2.1%, respectively. The relative lot-dependent differences found for the lower and higher glucose concentrations were equal to 6.8 and 2.6%, respectively. The glucose meter error calculated for various concentration ranges amounted from 2.22 to 4.48%. The glucose meter error met the accuracy criteria recommended by the International Organization for Standardization and the American Diabetes Association. The Passing-Bablok agreement test and error grid analysis with 96% of results in zone A indicated good concordance of results, including glucose concentrations below 100 mg/dl. The evaluated Optium Omega glucose meter fits the analytical requirements for its use in blood glucose monitoring in diabetes patients.

Evaluation of the Analytical Performance of the Coulometry-Based Optium Omega Blood Glucose Meter: What Do Such Evaluations Show?

The article entitled "Evaluation of the Analytical Performance of the Coulometry-Based Optium Omega Blood Glucose Meter", by Solnica and colleagues in this issue of Journal of Diabetes Science and Technology demonstrates that the Optium Omega blood glucose meter meets the analytical requirements for glucose meter performance and it is stated that the results are clinically useful. The authors studied precision, bias, and reagent lot-to-lot error sources. The ultimate goal of an evaluation is to estimate the distribution of errors (from any source) that will be experienced in routine use. The data collection and analysis methods to achieve this are discussed, as are the standards used to compare the results. Claiming clinical usefulness is almost a boilerplate statement in evaluations but meeting standards does not prove clinical usefulness.

Suboptimal Performance of Blood Glucose Meters in an Antenatal Diabetes Clinic

OBJECTIVEThe objective of this study was to evaluate the performance of blood glucose meters in diabetes associated with pregnancy (DP).RESEARCH DESIGN AND METHODSFinger-prick blood glucose levels measured using six different glucose meters on 102 patients with DP attending an antenatal clinic were compared with laboratory plasma glucose results. HbA1c and hematocrit were also measured.RESULTSThe plasma glucose range was 2.2–9.4 mmol/L with hematocrit 33–37% and mean HbA1c 5.5% ± 0.56 (SD). All meters provided plasma equivalent results except one, which reported whole blood glucose that was adjusted to plasma equivalent values. The absolute glucose difference [meter − plasma glucose] was 0.232 ± 0.69 to 0.725 ± 0.62 mmol/L mean ± SD and bias ranged from 6.1 to 15.8%. Two meters were affected by hematocrit <36% (P < 0.05).CONCLUSIONSBlood glucose meters in current use are not optimally accurate when compared with plasma glucose measurement in DP. Recognition of this deviation is essential to prevent inappropriate treatment of DP.

A mathematical model to assess the influence of hematocrit on point of care glucose meter performance

Objectives To develop and apply a mathematical model of the relationship between hematocrit, meter glucose and reference glucose concentrations as a tool for evaluation of whole blood glucose meters. Design and Methods Patient blood gas specimens were used to compare the LifeScan SureStep Flexx and the Nova StatStrip glucose meters with reference results obtained from a Radiometer 725 blood gas analyzer. Linear regression analysis was conducted to determine the extent that patient hematocrit and reference glucose concentrations predicted the performance of the glucose meters. Results Bland-Altman graphic analysis depicted that both glucose meters showed variance with the reference method. A mathematical model was derived from IFCC consensus equations that relate glucose meter results, hematocrit and plasma glucose values. Using the model, multivariate regression depicted that hematocrit affected the results of the SureStep Flexx meter in a manner dependent on the glucose concentration, whereas the StatStrip meter was not affected by hematocrit. Conclusions Linear regression can be used to quantify the extent of hematocrit interference on the performance of glucose meters.

Comparison in the Performance of Glucose Meters in Blood Glucose Monitoring during Pregnancy

Aims: To compare the performance of the four latest models of glucose meters in capillary blood glucose monitoring during pregnancy. Methods: 208 pregnant women with gestational diabetes were recruited. Each subject had simultaneous capillary glucose monitoring by two study glucose meters and venous plasma glucose assay. The performance of four glucose meters was compared using error grid analysis (EGA) and the agreement between the meter readings and plasma glucose by Bland-Altman plot analysis. Results: Elite, Advantage II and CareSens had more than 90% of readings in the acceptable target range of EGA. CareSens had the lowest mean bias by Bland-Altman analysis while Advantage II had the highest proportion of readings within 5% difference from plasma glucose. Readings from all glucose meters except Optium were not influenced by the change in maternal hematocrit levels. Conclusions: The performance of four study glucose meters appeared very similar.

Variable Classifications of Glycemic Index Determined by Glucose Meters

The study evaluated and compared the differences of glucose responses, incremental area under curve (IAUC), glycemic index (GI) and the classification of GI values between measured by biochemical analyzer (Fuji automatic biochemistry analyzer (FAA)) and three glucose meters: Accue Chek Advantage (AGM), BREEZE 2 (BGM), and Optimum Xceed (OGM). Ten healthy subjects were recruited for the study. The results showed OGM yield highest postprandial glucose responses of 119.6 ± 1.5, followed by FAA, 118.4 ± 1.2, BGM, 117.4 ± 1.4 and AGM, 112.6 ± 1.3 mg/dl respectively. FAA reached highest mean IAUC of 4156 ± 208 mg × min/dl, followed by OGM (3835 ± 270 mg × min/dl), BGM (3730 ± 241 mg × min/dl) and AGM (3394 ± 253 mg × min/dl). Among four methods, OGM produced highest mean GI value than FAA (87 ± 5) than FAA, followed by BGM and AGM (77 ± 1, 68 ± 4 and 63 ± 5, p<0.05). The results suggested that the AGM, BGM and OGM are more variable methods to determine IAUC, GI and rank GI value of food than FAA. The present result does not necessarily apply to other glucose meters. The performance of glucose meter to determine GI value of food should be evaluated and calibrated before use.

Analytical and Clinical Performance of Blood Glucose Monitors

The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature. Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors. Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed. When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.

An Evaluation of the Analytical Performance of a New-Generation Hospital-Based Glucose Meter and an Assessment of Its Clinical Reliability in a Neonatal Care Unit

Introduction: Glucose meters are widely used in hospitals for point-of-care monitoring of blood glucose. However, concerns about their accuracy and reliability in a neonatal setting have been expressed. We evaluated the analytical performance and clinical reliability of a new-generation glucose monitoring system (StatStrip; Nova Biomedical, Waltham, Mass) that corrects for interference factors by comparing results with a laboratory plasma hexokinase reference method, along with 3 other glucose meter systems. Methods: Analytical studies for assessing precision and method correlation in the laboratory were performed comparing glucose to plasma glucose results from a reference hexokinase method. Challenge studies were performed with interfering substances including hematocrit, maltose, ascorbate, and β-hydroxybutyrate (βHB) that have previously been shown to affect measurements from current glucose meter technologies. Clinical reliability was assessed with 109 capillary blood specimens collected from neonatal intensive care unit (NICU) patients with varying hematocrit levels. Results: Within-run imprecision was similar for all 4 meter systems, with the coefficient of variation generally less than 5%. Of the 4 meter systems, 2 (StatStrip and Contour [Bayer Healthcare Diabetes Care, Newbury, UK]) showed closer correlation to the reference hexokinase method after regression analyses on 75 spiked whole blood specimens. Linear regression analysis demonstrated a regression line slope of 0.960 for Contour and 0.920 for StatStrip, with lower slope values of 0.705 and 0.791 for Optium (Abbott Diabetes, Alameda, Calif) and Advantage (Roche Diagnostics, Indianapolis, Ind), respectively. The Contour and StatStrip glucose meter systems had the lowest mean biases compared with the hexokinase method. Of the 4 strip meter systems tested for interference, only the StatStrip and Optium systems remained within 0.55 mmol/L of their initial value (at a glucose concentration <5.55 mmol/L) and 10% (at a glucose concentration >5.55 mmol/L) after the addition of ascorbate, maltose, or βHB. Maltose had a marked effect on the Advantage system, whereas ascorbate and βHB had only minor effects on the Contour system. Increasing hematocrit values significantly lowered the glucose value readings for samples with medium to high glucose values tested by the Optium and Advantage systems. Varying hematocrit levels had minor and clinically insignificant effects on both the StatStrip and Contour systems. This was further substantiated after bias plot analysis of hematocrit levels in the NICU patient population. When applied to NICU patient samples, StatStrip showed close accordance with ISO 15197 performance criteria and good accuracy in comparison with the laboratory hexokinase method. Conclusions: Managing glucose levels in a neonatal critical care setting is dependent on using reliable and accurate glucose meters with good specificity. The StatStrip and Contour meters performed well based on evaluation, with no interference observed for hematocrit, maltose, βHB, and ascorbate. In the clinical study on the NICU patients, the StatStrip meter showed good correlation with the hexokinase method across the analytical range.

Analytical evaluation of the Optium Xido blood glucose meter

The main prevention of chronic diabetic complications is maintaining near normoglycemia. In this study, evaluation of analytical performance of the Optium Xido blood glucose meter designed for patients' self-monitoring of glycemia was carried out. Glucose concentrations were measured in 118 EDTA blood samples using the Xido glucose meter and the GOD/PAP method on a Modular P clinical analyzer. The results obtained were used for the assessment of accuracy, precision and linearity. Performance of the Xido glucose meter was also assessed using four different reagent strip lots, and the between-lot variation was calculated. The within-run imprecision coefficient of variation (CV) amounted to 4.2%. Good response linearity was found in glucose concentrations of 31-444 mg/dL (1.7-24.7 mmol/L). In the concentration range studied, the glucose meter error was 5.14% and the linear regression equation was y=0.91x+6.2, (r=0.984). The Passing-Bablok agreement test indicated good concordance of results. However, for glucose concentrations<100 mg/dL (5.6 mmol/L) (n=69) the error was 6.82% with regression equation y=0.86x+5.9 (r=0.757). Between-lot differences amounted to 0.7%-18.2%. The Xido glucose meter has good precision and accuracy when compared to the laboratory method and meets the quality recommendations of the National Committee of Clinical Laboratory Standards (NCCLS, currently the Clinical Laboratory Standards Institute), the National Academy of Clinical Biochemistry (NACB) and the International Organization for Standardization (ISO). However, between-lot variability may result in differences between day-to-day results when the device is continuously used. Therefore, the validation of new lots of reagent strips with a laboratory method is recommended.

Educational intervention together with an on-line quality control program achieve recommended analytical goals for bedside blood glucose monitoring in a 1200-bed university hospital

Portable meters for blood glucose concentrations are used at the patients bedside, as well as by patients for self-monitoring of blood glucose. Even though most devices have important technological advances that decrease operator error, the analytical goals proposed for the performance of glucose meters have been recently changed by the American Diabetes Association (ADA) to reach <5% analytical error and <7.9% total error. We studied 80 meters throughout the Virgen Macarena Hospital and we found most devices with performance error higher than 10%. The aim of the present study was to establish a new system to control portable glucose meters together with an educational program for nurses in a 1200-bed University Hospital to achieve recommended analytical goals, so that we could improve the quality of diabetes care. We used portable glucose meters connected on-line to the laboratory after an educational program for nurses with responsibilities in point-of-care testing. We evaluated the system by assessing total error of the glucometers using high- and low-level glucose control solutions. In a period of 6 months, we collected data from 5642 control samples obtained by 14 devices (Precision PCx) directly from the control program (QC manager). The average total error for the low-level glucose control (2.77 mmol/l) was 6.3% (range 5.5-7.6%), and even lower for the high-level glucose control (16.66 mmol/l), at 4.8% (range 4.1-6.5%). In conclusion, the performance of glucose meters used in our University Hospital with more than 1000 beds not only improved after the intervention, but the meters achieved the analytical goals of the suggested ADA/National Academy of Clinical Biochemistry criteria for total error (<7.9% in the range 2.77-16.66 mmol/l glucose) and optimal total error for high glucose concentrations of <5%, which will improve the quality of care of our patients.