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

Lam, Mandy; Louie, Richard F.; Curtis, Corbin M.; Ferguson, William J.; Vy, John H.; Truong, Anh Thu; Sumner, Stephanie L.; Kost, Gerald J.

doi:10.1177/1932296813514325

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…[7][8][9][10][11][12] With both simulated and real data, the SEG showed results that were intuitively clear and corresponding well to previous findings.…”

Section: Discussionsupporting

confidence: 75%

“…In this article we introduce the computation of the SEG implemented in Excel-based macro, and illustrate its use with computer-simulated meter errors corresponding to different levels of International Organization for Standardization (ISO) accuracy, and with real data sets collected during experiments testing BGM devices under various, previously described normal-use and stress conditions. [7][8][9][10][11][12] The software for computing the SEG in this article is copyrighted by the University of Virginia (Charlottesville, VA) and will be available through the Diabetes Technology Society website: at www.diabetestechnology.org/SEGsoftware.…”

mentioning

confidence: 99%

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Computing the Surveillance Error Grid Analysis

Kovatchev

Wakeman

Breton

et al. 2014

J Diabetes Sci Technol

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The surveillance error grid (SEG) analysis introduced in this issue of the Journal of Diabetes Science and Technology1 is based on a carefully crafted set of questions answered by 206 clinicians with expertise in the treatment of diabetes, and on advanced mathematical interpretation of the obtained results transforming the SEG into a modern tool for the analysis of errors associated with blood glucose monitoring (BGM). While the predecessors of the SEG-the now classic Clarke 2 and Parkes 3 error grid analyses-were amenable to straightforward visual interpretation and pen-and-paper classification of meter errors into several zones, the SEG introduces a new dimension to the evaluation of the accuracy of BGM devices-the perceived level of risk associated with meter errors. The logic behind such a risk evaluation is self-evident: as shown in a recent report, even the best contemporary BGM systems are prone to more or less frequent errors, 4 potentially resulting in deterioration of glycemic control parameters as shown by in silico experiments. These experiments suggested a threshold effect between 10% and 15% permitted BGM error for most parameters, except for HbA1c, which appeared to be increasing relatively linearly with increasing BGM error above 10%. 5The missing link between the magnitude of BGM errors and the ensuing deterioration of glycemic control is therefore the level of risk created by BGM inaccuracy, which occasionally translates into inappropriate treatment decisions and, eventually, into inferior treatment outcomes. The keyword The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. Methods:The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo-or hyperglycemia, respectively. Results:To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published ...

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“…[7][8][9][10][11][12] With both simulated and real data, the SEG showed results that were intuitively clear and corresponding well to previous findings.…”

Section: Discussionsupporting

confidence: 75%

mentioning

confidence: 99%

Computing the Surveillance Error Grid Analysis

Kovatchev

Wakeman

Breton

et al. 2014

J Diabetes Sci Technol

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“…37 A study employing an environmental chamber was conducted to assess the impact of short-term exposure (15-60 minutes) to high temperature and humidity on POC glucose test strips (in original vial packaging) and BG meters performance. 38 Even after a relatively short exposure (15 minutes) at 42ºC with 83% relative humidity, the tested BG systems produced significantly elevated BG results. 38 Measurement discrepancies of 20 mg/dL (BG meter) and 13 mg/dL (test strip) have been reported, the summed increase of 33 mg/dL can be assumed to potentially lead to inadequate treatment decisions.…”

Section: Temperature and Humiditymentioning

confidence: 90%

“…38 Measurement discrepancies of 20 mg/dL (BG meter) and 13 mg/dL (test strip) have been reported, the summed increase of 33 mg/dL can be assumed to potentially lead to inadequate treatment decisions. 38 Importantly, as in the previous study, the conditions used in these studies were not in accordance with the approved conditions for usage.…”

Section: Temperature and Humiditymentioning

confidence: 99%

Interferences and Limitations in Blood Glucose Self-Testing

Erbach¹,

Freckmann²,

Hinzmann³

et al. 2016

J Diabetes Sci Technol

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Self-monitoring of blood glucose (SMBG) both in insulintreated and non-insulin-treated people with diabetes is supported by recently published trials, reviews, meta-analyses, and guidelines. [1][2][3][4][5][6][7] SMBG is recommended to be performed in a structured approach. 2,5,8,9 It is reported to be only useful when blood glucose (BG) data are interpreted and utilized for immediate therapeutic actions. 3,4,[10][11][12][13] For instance, the need for adequate dosing of insulin heavily depends on reliable glucose information. 8 In particular, patients with insulintreated diabetes perform SMBG as a substantial element of daily management of diabetes.14,15 The term "BG system" denotes the combination of a BG meter and test strips, and both determine analytical performance. 8 The analytical and handling performance of BG systems has largely improved over the past decades. In addition, the implementation of inmeter safety features (ie, validity of test strips check) has further increased the safety of these devices. 16 Consequently, patients with appropriate training and a good performance of BG testing can typically rely on the precision of BG measurement results. However, in the daily practice a range of factors with potential impact on the reliability of BG measurement needs to be considered. In fact, this is an important aspect in field of point-of-care (POC) testing. 17 Members of the diabetes team and patients should be well informed about all factors potentially falsifying BG measurement results: human, meter-inherent, test-strip-inherent, environmental, physiological, and medication-related impact factors (Table 1).The risk of misinterpretation of BG readings can be minimized by detailed information on the factors potentially affecting BG measurement. Hence, the aim of this publication is to review the current knowledge on limitations and interferences significant for reliable BG testing.Nonetheless, due to the rapid technological progress, it should be kept in mind that performance of most more recent BG meters may not always be reflected by the reviewed literature, since it reports on data generated with older BG generations. 17 Moreover, it is important to note that some studies on limitations of BG meters were performed under extreme conditions which do not comply with the approved conditions of usage. AbstractIn general, patients with diabetes performing self-monitoring of blood glucose (SMBG) can strongly rely on the accuracy of measurement results. However, various factors such as application errors, extreme environmental conditions, extreme hematocrit values, or medication interferences may potentially falsify blood glucose readings. Incorrect blood glucose readings may lead to treatment errors, for example, incorrect insulin dosing. Therefore, the diabetes team as well as the patients should be well informed about limitations in blood glucose testing. The aim of this publication is to review the current knowledge on limitations and interferences in blood glucose testing with the perspective of the...

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“…Moreover, we did not explore the impact of the condition of conservation of strips and meters on the accuracy and precision, whereas it has been recently shown to be important factors for meter's performance [22].…”

Section: Discussionmentioning

confidence: 99%