Evaluation and Performance of StatStrip Glucose Meter. 1 These are factors known to affect the performance of glucose

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1 ORIGINAL ARTICLE Evaluation and Performance of StatStrip Glucose Meter Megan Amelia Rensburg, MBChB, FC Path (SA), MMed (Chem Path), Careen Hudson, MBChB, FC Path (SA), MMed (Chem Path), and Rajiv Timothy Erasmus, MBBS, FMC Path (Nig), FWACP (W. Af), DABCC (Am Board Certified), DHSM (Natal), FC Path (SA) Abstract: Point-of-care testing glucose meter use is on the increase and is widely used in monitoring hospitalized patients as well as by patients for selfmonitoring. A major concern is the accuracy of glucose meters in different clinical settings. StatStrip (Nova Biomedical, Waltham, Mass) is a new generation glucose and quantitative ketone meter designed to correct for common biochemical interferences and to measure and correct hematocrit. Our aim was to assess the analytical performance of the StatStrip (Xpress and Connectivity) to Accu-Chek Active meters (Roche Diagnostics, Mannheim, Germany) and assess the glucose meters in a clinical setting. Hematocrit interference and chemical interferences (ascorbic acid, maltose, xylose, and acetaminophen) were evaluated at different glucose levels and different interferent concentrations. Whole blood samples collected from patients attending the medical outpatients department were measured and compared with the reference method (Siemens Advia glucose oxidase) and assessed by comparison with the ISO glucose performance criteria. Diabetic patients were included for this evaluation. Finger-prick (capillary) glucose (obtained from diabetic patients) measured on the glucose meters was compared with plasma glucose measured in the laboratory (Siemens Advia glucose oxidase). Minimal hematocrit and chemical interference were observed on the StatStrip meters, whereas the Accu-Chek Active meters were significantly affected by both abnormal hematocrit and chemical interference. StatStrip correlated best to the reference method and demonstrated the lowest bias. The StatStrip glucose meters demonstrated acceptable correlation when compared with the reference method, were not susceptible to common interferences observed on currently used glucose meters, and performed well in the clinical setting. Key Words: point-of-care testing, glucose meters, hematocrit, method validation (Point of Care 2014;13: ) The globally increasing incidence of diabetes mellitus and the accompanying metabolic syndrome has forced much attention on the early diagnosis and treatment thereof. Screening of patients at risk is recommended. The use of point-of-care testing (POCT) using glucose meters has been on the increase, especially in the primary health care setting. The use of POCT devices ensures a rapid turnaround and timely treatment of patients in the intensive care setting. The accuracy of POCT devices compared with laboratory glucose measurements is therefore an important factor to consider. The advantages of POCT, for example, low sample volume and decreased turnaround time, are negated if the results compare poorly From the Division of Chemical Pathology, National Health Laboratory Service, Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa. Reprints: Megan Amelia Rensburg, MBChB, FC Path (SA), MMed (Chem Path), Division of Chemical Pathology, National Health Laboratory Service, Tygerberg Hospital, Stellenbosch University, PO Box 19113, Tygerberg, 7505 Cape Town, South Africa. rensburg@sun.ac.za. The authors declare no conflict of interest. Copyright 2014 by Lippincott Williams & Wilkins ISSN: X with laboratory-based measurements. The variability between the laboratory and glucose meter results is partly caused by userrelated issues, which can be addressed by training and appropriate quality control. Patient factors, such as hemodynamics and drug treatment, become important if glucose meters used are prone to interferences. Although monitoring of glucose is common among those with diabetes mellitus, it is not restricted to this population. Recent studies have suggested that tight glucose control decreases mortality in critically ill patients. Intensive care patients are on multiple medications and often have abnormal hematocrit and/or oxygenation. 1 These are factors known to affect the performance of glucose meters. Many studies have found that glucose meters demonstrate a positive bias at low hematocrit and a negative bias at high hematocrit. 2 In the South African setting, there is a high incidence of infectious diseases, especially human immunodeficiency virus with a prevalence of 17.9% (15 49 years), as reported in The increasing use of antiretroviral drugs with their adverse effects leads to a further increase in the metabolic disease burden in this patient population. Metabolic abnormalities, such as dyslipidemia and insulin resistance, are most commonly observed. 4 These are further increasing the burden of cardiovascular disease in the human immunodeficiency virus population. A new generation point-of-care glucose meter, the StatStrip glucose meter (Nova Biomedical, Waltham, Mass), has many unique features. It has a turnaround time of 6 seconds, only needs 1.2 μl of blood per measurement, and has the unique advantage of no hematocrit interference. StatStrip glucometer technology allows for simultaneous measurement of the hematocrit and the subsequent correction for abnormal measurements together with the glucose on the same strip. 5 Our aim was to assess the analytical performance of the StatStrip (Xpress and Connectivity) to 2 Accu-Chek Active meters (Roche Diagnostics, Mannheim, Germany), which are currently used in monitoring glucose in hospital and by patients in the public health sector in South Africa. Plasma glucose by the glucose oxidase method on the Siemens Advia 1800 (Siemens Diagnostics, Munich, Germany) was used as the reference method. Our second aim was to assess the glucose meters in a clinical setting. MATERIALS AND METHODS Setting Our laboratory is based in Tygerberg Academic Hospital (a 1310-bedded tertiary hospital) in Cape Town, South Africa, and serves approximately 3.6 million people in the Cape Metropole and surrounding rural areas. Instrumentation The reference assay used was the glucose oxidase method on the Siemens Advia 1800 (Siemens Diagnostics). Four glucose meters were used in this evaluation: 2 Accu-Chek Active meters Point of Care Volume 13, Number 4, December

2 Rensburg et al Point of Care Volume 13, Number 4, December 2014 TABLE 1. Correlation Data for Glucose Meters Versus Siemens Advia 1800 (n = 155)* Glucose Meter Slope Intercept, mmol/l R 2 Median Bias, mmol/l StatStrip Xpress StatStrip Connectivity Accu-Chek Accu-Chek *Data of only 1 lot number displayed as similar result obtained with other lot number strips. (Roche Diagnostics) that uses the glucose dehydrogenasepyrroloquinolinquinone electrochemical method and the StatStrip Xpress and Connectivity (Nova Biomedical) that use a modified glucose oxidase based amperometric technology with hematocrit correction. Two different strip lot numbers were analyzed on all 4 glucose meter instruments. Precision Studies Quality control material (3 levels with ranges ; ; and mmol/l) and spiked heparinized whole blood (3 levels with ranges ; ; and mmol/l) were used to assess the meters' precision. Laboratory-Based Method Correlation Donated patient/volunteer specimens (consented adults from the laboratory and medical outpatient department) heparinized venous whole blood were obtained and analyzed within 30 to 60 minutes of collection. A total of 155 blood samples were analyzed on the 4 glucose meters and then immediately centrifuged for measurement on the laboratory instrument Siemens Advia 1800 (Siemens Diagnostics). Twenty of these samples were spiked with various volumes of glucose concentrate to extend the glucose range. Clinical Method Correlation A group of 110 patients attending the diabetic outpatient clinic was recruited for this leg of the evaluation. Venous whole blood collected in a sodium fluoride tube was collected for analysis on the reference laboratory method (Siemens Advia 1800; glucose oxidase method) and a simultaneous finger-prick capillary blood sample was obtained and measured on the glucose meters (only the StatStrip Xpress and Connectivity meters; only 1 lot of strips was evaluated). FIGURE 1. Difference plots with ISO standard limits. A, StatStrip Xpress versus reference method (glucose oxidase). B, StatStrip Connectivity versus reference method (glucose oxidase). C, Accu-Chek Active 1 versus reference method (glucose oxidase). D, Accu-Chek Active 2 versus reference method (glucose oxidase) Lippincott Williams & Wilkins

3 Point of Care Volume 13, Number 4, December 2014 Performance of StatStrip Glucose Meter TABLE 2. Clinical Correlation Data for Glucose Meters Versus Siemens Advia 1800 (n = 110) Glucose Meter Slope Intercept, mmol/l R 2 Median Bias, mmol/l StatStrip Xpress StatStrip Connectivity Interference Testing and Hematocrit Study A single-donor volunteer (healthy, consented laboratory staff member) with normal hematocrit levels was selected for the evaluation of interfering substances and hematocrit effect. One hundred fifty milliliters of venous heparinized whole blood samples were collected from the single donor and allowed to sit at room temperature for 24 hours on a tube roller. Afterward, these samples were spiked with various concentrations of glucose and interfering substances. Interferences studied included maltose, xylose, ascorbic acid, and paracetamol (acetaminophen). The samples were spiked with various concentrations of glucose, and then, the interfering substances were added at various concentrations. The glucose was then determined using the glucose meters, followed by immediate centrifugation and measurement of plasma glucose on the laboratory instrument. Various hematocrit levels were prepared from the single-donor sample using fresh heparinized whole blood, which was also allowed to sit at room temperature for approximately 24 hours. This sample was divided into 3 aliquots and spiked with concentrated glucose to achieve 3 different glucose levels. These were then further divided into 5 aliquots of 1 ml, which were centrifuged (Corning LMS Mini Microcentrifuge, Tewksbury, Mass) and plasma adjusted (taking plasma from 1 tube and adding to another), to obtain 5 samples each with a different hematocrit (percent) level for each of the 3 glucose levels. The samples were rocked for 10 minutes, analyzed on the glucose meters, and afterward, centrifuged for plasma glucose determination on the Siemens Advia 1800 (Siemens Diagnostics). Hematocrit values were measured on these samples using the Mini- Hematocrit centrifuge (Hawksley, England). Ethical Consideration Ethical approval was obtained from the ethics and research committee of the Stellenbosch University, and all patients and volunteer donors were consented before blood collection. Statistical Analysis Interference and Hematocrit Experiment The results were expressed as mean change from the baseline glucose (meter glucose with interfering substance meter glucose at baseline) expressed as a percentage change. Method Correlation Correlation statistics including regression analysis and Bland- Altman statistics were analyzed using the Analyse-it software version 2.30 and Microsoft Excel The ISO standard was used to confirm accuracy. 6 The standard states that glucose values should fall within 0.8 mmol/l of the clinical laboratory reference method for 95% of values below 4.2 mmol/l or within 20% of the clinical laboratory reference method for values above 4.2 mmol/l. (Since this study, the standard has been revised to ISO 15197:2013.) RESULTS Precision Within-run and between-run precisions were assessed using 3 levels of quality control and 3 levels of spiked whole blood samples. All of the glucose meters achieved precision of less than 5% at all levels. Laboratory Correlation Experiment Correlation between the glucose meter results and plasma glucose oxidase reference method was performed by analyzing 155 fresh lithium heparin venous blood specimens. Linear regression equations and R 2 statistics are displayed in Table 1. The Xpress meter showed an R 2 of and and the Connectivity meter showed an R 2 of and for both strip lots. The excellent correlations were achieved using both normal FIGURE 2. Difference plots with ISO standard limits (clinical correlation). A, StatStrip Xpress versus reference method (glucose oxidase). B, StatStrip Connectivity versus reference method (glucose oxidase) Lippincott Williams & Wilkins 139

4 Rensburg et al Point of Care Volume 13, Number 4, December 2014 FIGURE 3. Ascorbic acid interference plot. and abnormal samples and using samples that had potential glucose-interfering substances. Overall, the Nova meters averaged less than 2% accuracy error and had slopes averaging approximately 0.960, with Y intercepts of close to 0 across both strip lots tested. These data confirm that the Nova meters had almost perfect correlation and agreement to the laboratory reference method with no interference effects. The Roche Active meters used in the testing showed R 2 values of and for the first meter and and for the second meter over both strip lots. These correlation coefficients were much lower than those for the Nova StatStrip, and in addition, they averaged approximately 15% accuracy error across both strip lots tested. The Roche meters seem to have an overall positive results bias versus the reference method. The StatStrip meters met the recommended ISO clinical accuracy standard on 100 % of all specimens with no bias data trend observed. The Roche Active meters used in the study were only able to meet the ISO standard for 80% of the samples tested for results up to 4.2 mmol/l and only for 91% of the samples tested for samples more than 4.2 mmol/l. In addition, the Roche devices showed a positive result bias overall for low through high samples comparing with the laboratory reference system (Figs. 1A D). Clinical Correlation Study The Xpress meter showed an R 2 of 0.97 and the Connectivity meter showed an R 2 of Linear regression equation and R 2 statistics are displayed in Table 2. The StatStrip meters met the recommended ISO clinical accuracy standard on more than 95% of the specimens (Figs. 2A, B). Interfering Substances The Roche Active meters were slightly affected by paracetamol (acetaminophen) present in normal level glucose whole blood samples. Accuracy errors for the reference range glucose samples ranged from 8% to 19% across both strip lots tested. Unlike the Roche meters, the StatStrip meters were not affected at any levels by the presence of acetaminophen in the blood samples. Nova accuracy errors averaged less than 5% versus the reference method across both strips lots tested and all glucose levels. The Roche data however indicated falsely elevated glucose results on normal level glucose samples because of high-level interference from ascorbic acid (Fig. 3). Acetaminophen interference at different concentration levels (0.29 and 0.59 mmol/l) was tested at 5 different glucose levels (range, mmol/l). The Roche Active meters were affected by the presence of ascorbic acid in the low and normal whole blood samples. Accuracy errors for the low and reference range glucose samples ranged from as little as 10% up to 81% across both strip lots tested. Unlike the Roche meters, the StatStrip meters were not affected by the presence of ascorbic acid in the blood samples. StatStrip accuracy errors averaged approximately 5% to 8% versus the reference method across both strips lots tested and all glucose levels. Maltose with final concentrations of 2.8 and 5.6 mmol/l and xylose (final concentrations, 5.6 and 11.1 mmol/l) were added to donor blood samples with concentrations of 1.9, 6.6, and 23.5 mmol/l. The Roche Active meters were severely affected bythepresenceofbothmaltoseandxyloseinthewholeblood samples. The accuracy errors varied, depending on glucose level. The Roche meters showed accuracy errors of between 12% and 42% across midrange- and high-glucose samples and showed errors of 77% to 652% across reference- and low-range glucose samples. Unlike the Roche meters, the StatStrip meters were not affected by nonglucose sugars in the blood samples. The Nova accuracy errors averaged approximately 2% versus the reference method across both maltose and xylose levels at all glucose levels (Figs. 4A, B). Hematocrit Effect Results from this study showed that the Nova StatStrip had average accuracy errors compared with the reference method of less than 5%, whereas the Roche Active had accuracy errors of up to 17% versus the reference method at low-hematocrit levels and up to 21% accuracy errors at high-hematocrit levels across low- through high-glucose levels. In addition, the Roche meters results at low- and high-hematocrit levels differed from itself at normal hematocrit level by up 22% on the same glucose samples. FIGURE 4. A and B, Maltose and xylose interference plots Lippincott Williams & Wilkins

5 Point of Care Volume 13, Number 4, December 2014 Performance of StatStrip Glucose Meter FIGURE 5. Influence of hematocrit (percent) on glucose measurement. A, Glucose is equal to 21 mmol/l. B, Glucose is equal to 13.2 mmol/l. The results were confirmed by testing on 2 different strip lots of Nova and Roche strips (Fig. 5). DISCUSSION Glucose meters are commonly used in an array of different clinical setting and patients. The accuracy and precision of these and most other POCT instruments are an often-neglected area. Our findings have highlighted the importance of having knowledge on the instrument's performance and possible interferences. These glucose meters are used in settings where clinicians and patients will use results to guide therapy. It is therefore imperative that the glucose meter produces a result that is comparative with central laboratory test results. In our evaluation, all the meters displayed an acceptable imprecision. The meters however differed in performance when compared with the reference laboratory method (glucose oxidase). This difference has been observed in previous studies. 5,7,8 The StatStrip meters demonstrated the closest correlation with the laboratory method. It met the ISO criteria because 100% of all data points were within the limits of this standard. The Accu-Chek meters did not meet the ISO criteria and overall showed a positive bias. This positive bias can become problematic in both low- and high-glucose ranges, leading to undertreatment and possibly missed hypoglycemia in the low-glucose range. Overtreatment in the higher glucose range poses the danger of inducing hypoglycemia, when treatment (eg, insulin) is adjusted in response to high-glucose levels. Boyd and Bruns 8 have demonstrated that, at a 10% total error, 16% to 45% of sliding-scale insulin doses would be in error, although small, but larger dosing errors were common when the total error was between 10% and 15%. 9 The effects of various commonly used medications have been observed. Paracetamol showed minimal interference on the Accu- Chek meters, whereas no interference was observed on the StatStrip instruments. Similarly, the StatStrip meters displayed no influence on high ascorbic acid concentrations but displayed significant interference with the Accu-Chek meters. Maltose interference is known to meters using the glucose dehydrogenase pyrroloquinolinquinone methodology and was found significant for the Accu-Chek meters. 10 The effect of hematocrit on the glucose meters was demonstrated by preparing samples with varying hematocrit values (22% 67%) and 3 different glucose concentrations. This exercise demonstrated clearly that the StatStrip meters were unaffected by hematocrit, whereas the Accu-Check meters both showed significant influence (inverse association). Hematocrit values can vary widely in the critically ill patients and are known to be high in the neonatal population. Multiple studies have before demonstrated the effect of hematocrit on POCT glucose instruments Awareness of the hematocrit interference is thus crucial when a glucose meter with known interference is being used in these patient groups. In conclusion, the StatStrip glucose meter outperformed the Accu-Check meters and was unaffected or minimally impacted by variables such as hematocrit and interfering substances, such as maltose and xylose. The StatStrip technology may improve the practice of glucose monitoring, especially in the critically ill and neonatal population. ACKNOWLEDGMENT The authors thank Scientific Group (Pty) Ltd. 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