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research-article2014
DSTXXX10.1177/1932296814563351Journal of Diabetes Science and TechnologyCeriotti et al
Original Article
Comparative Performance Assessment of Point-of-Care Testing Devices for Measuring Glucose and Ketones at the Patient Bedside
Journal of Diabetes Science and Technology 2015, Vol. 9(2) 268–277 © 2014 Diabetes Technology Society Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1932296814563351 dst.sagepub.com
Ferruccio Ceriotti, MD1, Ewa Kaczmarek1, Elena Guerra, PhD1, Fabrizio Mastrantonio, PhD2, Fausto Lucarelli, PhD2, Francesco Valgimigli, PhD2, and Andrea Mosca, PhD3
Abstract Point-of-care (POC) testing devices for monitoring glucose and ketones can play a key role in the management of dysglycemia in hospitalized diabetes patients. The accuracy of glucose devices can be influenced by biochemical changes that commonly occur in critically ill hospital patients and by the medication prescribed. Little is known about the influence of these factors on ketone POC measurements. The aim of this study was to assess the analytical performance of POC hospital wholeblood glucose and ketone meters and the extent of glucose interference factors on the design and accuracy of ketone results. StatStrip glucose/ketone, Optium FreeStyle glucose/ketone, and Accu-Chek Performa glucose were also assessed and results compared to a central laboratory reference method. The analytical evaluation was performed according to Clinical and Laboratory Standards Institute (CLSI) protocols for precision, linearity, method comparison, and interference. The interferences assessed included acetoacetate, acetaminophen, ascorbic acid, galactose, maltose, uric acid, and sodium. The accuracies of both Optium ketone and glucose measurements were significantly influenced by varying levels of hematocrit and ascorbic acid. StatStrip ketone and glucose measurements were unaffected by the interferences tested with exception of ascorbic acid, which reduced the higher level ketone value. The accuracy of Accu-Chek glucose measurements was affected by hematocrit, by ascorbic acid, and significantly by galactose. The method correlation assessment indicated differences between the meters in compliance to ISO 15197 and CLSI 12-A3 performance criteria. Combined POC glucose/ketone methods are now available. The use of these devices in a hospital setting requires careful consideration with regard to the selection of instruments not sensitive to hematocrit variation and presence of interfering substances. Keywords point-of-care testing, glucose, ketones, performance evaluation
Point-of-care testing (POCT) devices for monitoring glucose and ketones can play a key role in the management of dysglycemia in hospitalized diabetes patients. It is well recognized that POC monitoring of blood glucose levels in hospitalized acute and critical care patients is important for managing and maintaining normal glycemia to reduce patient morbidity and mortality and improve patient recovery.1-3 The use of handheld glucose meters for intermittent monitoring of patient glucose levels in hospitalized patients requiring intravenous insulin allows for rapid clinical decision making and immediate treatment responses.4 Similarly the measurement of whole-blood ketone (beta hydroxybutyrate [BHB]) plays an important role in the management of adult and pediatric diabetes ketoacidosis (DKA).5 Recent guidelines have advocated the use of whole-blood measurement of BHB for the management of DKA and for monitoring target directed therapy.6,7
Combined testing for glucose and BHB is often used in accident and emergency departments8,9 and can be an aid on ambulances and in a prehospital setting to identify and triage severely hyperglycemic patients.10 A high level of accuracy is required for POC glucose and BHB devices to ensure that patients are managed optimally. However, it has 1
Istituto Scientifico Ospedale San Raffaele, Servizio di Medicina di Laboratorio, Milan, Italy 2 A.Menarini Diagnostics, Florence, Italy 3 Dip. di Fisiopatologia Medico-Chirurgica e dei Trapianti, Centro per la Riferibilità Metrologica in Medicina di Laboratorio (CIRME), Università degli Studi di Milano, Milan, Italy Corresponding Author: Ferruccio Ceriotti, MD, Istituto Scientifico Ospedale San Raffaele, Servizio di Medicina di Laboratorio, Via Olgettina 60, Milan 20132, Italy. Email: [email protected]
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Ceriotti et al been well reported that the accuracy of many hospital glucose meters can be affected by the biochemical and physiological changes that commonly occur in acute, chronic, and critically ill hospital patients and also by the medication prescribed.11,12 A wide range of endogenous and exogenous substances present in the blood of critically ill patients can donate electrons to nonspecifically initiate the electrochemical reaction and cause interference with glucose results, giving elevated values.13 An increasingly large number of interfering factors have been associated with inaccurate glucose readings including abnormal hematocrit, oxygen tension and pH, uric acid, sodium, lipids, and administration of drugs (ascorbic acid, acetaminophen, dopamine). It is also well known that some of the enzymes used in glucose meters are not specific for glucose and can give falsely elevated results when other nonglucose sugars are present in patient samples.14 POC meters for wholeblood ketone testing are based on the same design principle as commonly established glucose meter. Little has been reported on the influence of known glucose meter interferences on the accuracy of ketone meters. The aim of this study was to undertake an analytical assessment of POC hospital whole-blood glucose and ketone meters to determine performance reliability and to determine the extent of glucose interference factors on the design and accuracy of ketone results.
Materials and Methods Instrumentation Two POC devices offering both glucose and ketone testing were assessed: StatStrip glucose/ketone (Nova Biomedical Waltham, MA, U.S.A) and Optium FreeStyle glucose/ketone (Abbott Diabetes Care Roma, Italy). In addition, the AccuChek Performa glucose only meter (Roche Diagnostic Milan, Italy) was also assessed. All 3 meter strip technologies represented the latest version provided for hospital use at the time of the study available. The StatStrip glucose strip technology is a modified glucose oxidase-based amperometric test system, and the ketone strip technology uses a BHB dehydrogenase enzyme based amperometric strip. Both strips incorporate hematocrit and other interference correction; Optium Freestyle uses an electrochemical glucose dehydrogenase/coenzyme nicotinamide adenine dinucleotide–based amperometric strip, and the ketone strip technology uses a BHB dehydrogenase enzyme based amperometric strip; Accu-Chek uses a glucose dehydrogenase/coenzyme pyrroloquinolone quinone–based amperometric strip recently modified to reduce the influence of maltose interference. The calibration of the StatStrip strips is predetermined by the manufacturer and requires no additional calibration code, whereas the Accu-Chek and Optium meters require the use of a calibration code for calibration of each strip lot. The ADVIA 2400 (Siemens Healthcare Diagnostics Milan, Italy)
laboratory analyzer plasma hexokinase method was used as the reference method for glucose measurements. For BHB testing the Ranbut enzymatic reagents (Randox Laboratories Ltd London, UK) run on Uvikon 940 spectrophotometer (Tegimenta Basel, Switzerland) was used as the reference method.
Precision Testing This was performed as according to Clinical and Laboratory Standards Institute (CLSI) guideline EP05-A2.15 Within-day precision was assessed using quality control material specific for each meter supplier and with 3 whole-blood samples containing low, medium, and high levels of glucose or BHB. For glucose testing lithium heparin whole blood was collected from a volunteer and mixed continuously 18 to 24 hours before commencing the study. In this specimen, red blood cell glycolytic activity reduced the glucose concentration to less than 35 mg/dL. For BHB testing lithium heparin whole blood was collected from a volunteer just prior to performing the precision testing. The 3 different levels of samples were prepared by adding varying amounts of a concentrated glucose or BHB stock solution to aliquots of the donated sample. For within-day precision 20 replicates of each of the 3 blood sample levels and quality control samples were tested by each of the meters. Day-to-day precision was assessed according to the CLSI EP5-A2 protocol using quality control (QC) material specific to each meter. Each sample was measured in duplicate 2 times a day for 20 days on each meter and with the reference method.
Method Comparison The comparison was performed according to CLSI guideline EP09-A3.16 Forty lithium heparin samples were collected from hospitalized patients. Each sample was measured in duplicate on each POC meter and for each analyte. The order of testing was randomized, and testing was completed in 5 minutes to reduce the influence of glycolsis on the pattern of results. This approach was taken throughout the analytical studies undertaken. The remainder of each aliquot was immediately centrifuged to prepare a plasma sample for glucose and BHB measurement with the respective reference methods. To ensure glucose and BHB sample concentrations reflected the entire analytical range, higher level samples were prepared by spiking varying amounts of a concentrated glucose (20% wt/vol) solution or BHB (0.5 mol/L) solution to samples. Low-level glucose samples were obtained by leaving samples mixing for 12 to 24 hours.
Hematocrit Interference A lithium heparin whole-blood specimen was collected, as previously described. Three glucose and BHB levels were
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Table 1. Within-Day Precision for Ketone POC Devices. Whole blood sample 1
Whole blood sample 2
Whole blood sample 3
Quality control materials
Method
N
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
StatStrip meter 1 StatStrip meter 2 Optium meter 1 Optium meter 2 Reference
20 20 20 20 5
1.09 1.12 0.83 0.81 0.86
0.04 0.06 0.04 0.02 0.02
3.38 5.27 5.38 2.78 2.74
4.22 4.17 3.95 3.85 3.53
0.14 0.11 0.14 0.11 0.08
3.29 2.62 3.44 2.98 2.17
6.52 5.99 6.52 6.23 5.39
0.22 0.29 0.16 0.11 0.09
3.42 4.87 2.40 1.80 1.67
2.64 3.11 0.70 0.77 N/A
0.10 0.16 0.05 0.02 N/A
3.95 5.17 6.4 2.78 N/A
prepared by adding the appropriate volume of a concentrated glucose (20% wt/vol) solution or BHB (0.5 mol/L) solution to achieve target glucose values of 20-60, 200-275, and 325-400 mg/dL and target ketone values of approximately 0.3-0.7, 2.2-2.8, and 3.7-4.3 mmol/L. For each glucose and BHB sample, 5 further aliquots were prepared and the hematocrit levels were adjusted after centrifugation and dilution to provide hematocrit levels of 28%, 38%, 48%, 59%, and 68% across each of the 3 concentrations. The actual hematocrit value for each of the aliquot preparations was confirmed using a StatSpin MP microhematocrit centrifuge (Iris Sample Processing, Westwood, MA). Each sample was tested 6 times by each strip meter system. The remainder of the sample was centrifuged immediately, and the plasma glucose and BHB level were tested by the respective reference methods.
Specificity Interference Studies This was performed according to CLSI guideline EP07-A2.17 Three different interfering substances (acetaminophen, ascorbic acid, and acetoacetate) were assessed for influence on the accuracy of BHB measurements, and 6 different interfering substances (acetaminophen, ascorbic acid, acetoacetate, galactose, maltose monohydrate, uric acid, and sodium) were assessed for influence on the accuracy of glucose measurements. Three samples at increasing concentration of BHB or glucose were prepared as described previously. For each concentration level a further 3 samples were prepared with increasing concentrations of each interfering substance. Each whole-blood sample was analyzed 6 times with each of the meters. The remainder of the aliquot was centrifuged, and the plasma glucose and BHB levels were assayed by the laboratory reference methods.
Linearity This was performed according to CLSI guideline EP06-A.18 For glucose linearity testing 11 samples were prepared by scalar dilutions of a high-concentrated sample with a lowlevel one. For BHB linearity testing 7 samples were prepared by adding varying amounts of a concentrated BHB (0.5 mol/L) stock solution to aliquots of a donated sample. Each sample was measured 10 times on each POCT meter.
Data Analysis All calculations were performed on MS Excel. For an assessment of the accuracy of each glucose device, the percentage bias of each glucose meter result compared with the respective reference method result was calculated and assessed by comparison to performance criteria specified in ISO 15197 201319 and CLSI guideline POCT12-A3.20
Results Precision Testing All ketone and glucose testing showed good within-day precision for whole-blood samples as well as for the manufacturers QC materials (Tables 1 and 2). The ketone and glucose meters also showed good day-to-day reproducibility (Tables 3 and 4). For glucose, the 3.3% desirable coefficient of variation (CV) according to biological variability was reached in most of the cases.
Method Comparison For the comparison of BHB methods, 48 samples were tested and the mean plasma BHB value was 2.17 mmol/L (range 0.11-5.65 mmol/L). Both methods showed a good correlation to the laboratory reference method (Table 5 and Figure 1), but both also demonstrated a positive bias. In the case of StatStrip there was a significant constant bias of about 0.2 mmol/L (Figure 1A), while Optium showed a relevant proportional bias (Figure 1B). For Optium the positive bias increased at higher levels of BHB, and this resulted in a higher level of discordance when assessing concordance of results to International Society for Pediatric and Adolescent Diabetes (ISPAD) decision-making criteria (Table 5).7 For the comparison of glucose methods, 40 samples were tested and the mean plasma reference glucose value was 109 mg/dL (range, 28-398 mg/dL). All 3 POC glucose methods showed a good correlation to the reference method (Table 6). Although the method correlation wasn’t set up to assess accuracy in compliance to current standards and guidance, there was some differences seen between the meters in comparison to ISO 1519719 and CLSI 12-A320 performance criteria (Table 6 and Figure 2).
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Ceriotti et al Table 2. Within-Day Precision for Glucose POC Devices. Whole blood sample 1
Whole blood sample 2
Whole blood sample 3
Quality control 1
Quality control 2
Method
N
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
StatStrip meter 1 StatStrip meter 2 Optium meter 1 Optium meter 2 Accu-Chek 1 Accu-Chek 2 Reference
20 20 20 20 20 20 5
46.0 46.8 31.3 30.1 37.5 37.6 48.4
1.62 2.35 1.59 1.76 0.94 1.05 0.55
3.53 5.03 5.07 5.86 2.52 2.8 1.13
116.3 115.5 116.1 115.5 110.3 110.0 119.6
2.30 2.19 2.83 4.46 1.97 2.28 0.55
1.97 1.90 2.44 3.86 1.79 2.08 0.46
244.6 243.5 226.6 225.6 229.0 225.8 250.2
3.76 3.24 5.35 5.07 3.89 4.12 0.4
1.54 1.33 2.36 2.25 1.70 1.82 0.2
61.3 64.5 61.4 61.9 45.1 42.3 N/A
2.22 1.96 2.33 2.94 1.52 0.66 N/A
3.63 3.04 3.79 4.75 3.37 1.55 N/A
282.3 294.2 270.5 277.5 307.4 292.5 N/A
6.33 9.36 8.91 13.3 4.22 3.56 N/A
2.24 3.18 3.30 4.80 1.37 1.22 N/A
Table 3. Day-to-Day Precision for POC Ketone Devices. StatStrip 1
StatStrip 2
Optium 1
2.73 7.4 8.0 3.4 1.0 10.2
2.76 5.5 6.1 3.5 0.6 8.0
0.72 4.7 3.6 2.4 0.0 5.5
Mean (mmol/L) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%)
Optium 2 0.69 4.3 4.1 1.5 0.1 5.3
Reference method 1.11 3.8 4.1 1.4 0.1 5.1
CV = overall imprecision; CVbd = between-day imprecision, CVbr = between-run imprecision, CVr = repeatability of duplicate; CVw = within-lab imprecision.
Table 4. Day-to-Day Precision for POC Glucose Devices.
Level 1 Mean (mg/dl) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%) Level 2 Mean (mg/dl) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%)
StatStrip 1
StatStrip 2
Optium 1
Optium 2
Accu-Chek 1
Accu-Chek 2
Reference method
61.2 3.0 2.9 1.3 2.5 3.8
63.5 3.2 2.2 1.3 0.0 3.4
60.3 3.1 3.2 2.5 0.2 4.6
62.6 3.7 5.1 1.3 0.7 5.9
44.6 1.9 1.4 1.5 0.1 2.4
43.2 2.0 1.3 1.0 0.0 2.1
86.0 0.4 0.9 1.7 0.7 1.8
287.6 1.8 2.2 0.7 9.2 2.6
293.0 1.8 2.0 1.5 6.9 2.8
276.8 2.1 3.6 2.3 1.7 4.5
283.2 1.9 2.3 3.3 0.5 4.2
302.2 1.3 0.9 1.4 0.3 1.9
294.6 2.5 2.0 0.8 3.1 2.8
229.2 0.5 0.6 1.5 0.4 1.6
CV = overall imprecision; CVbd = between-day imprecision, CVbr = between-run imprecision, CVr = repeatability of duplicate; CVw = within-lab imprecision.
Hematocrit Interference The final adjusted hematocrit levels were confirmed to be 24%, 38%, 43%, 56%, and 66% for the BHB samples and 24%, 36%, 45%, 56%, and 62% for the glucose samples. For StatStrip over the hematocrit range of 38-56% there was a minimal effect on BHB measurements across the 3 levels tested, while at a hematocrit level of 24% the StatStrip BHB reading was 20% lower and at a hematocrit level of 66% the
BHB reading was 12% higher compared to 43% hematocrit level (Figure 3A). For glucose there was a minimal effect of varying hematocrit levels over the range tested (Figure 3B) The influence of hematocrit was pronounced for Optium device for both glucose and ketone measurements across all concentration levels tested (Figure 3A). For the glucose samples there is up to a 60% difference between the lowest and highest hematocrit levels, for the BHB samples up to 300%.
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Table 5. Method Comparison for POC Ketone Methods and Concordance of Classification According to International Society for Pediatric and Adolescent Diabetes (ISPAD) Decision-Making Criteria. ISPAD performance criteria number of results within each range
Regression parameters Meter system
N
Slope
Intercept (mmol/L)
R2
1.5-36 mmol/L
>36 mmol/L
Reference method StatStrip 1 StatStrip 2 Optium 1 Optium 2
48 48 48 48
0.9950 0.9761 1.4028 1.3677
0.15 0.19 –0.29 –0.28
.9776 .9767 .9764 .9769
4 4 4 4 4
19 17 16 19 18
16 17 19 8 10
9 10 9 17 16
Figure 1. Method comparison between reference method and beta hydroxybutyrate concentrations measured by StatStrip (A) and Optium (B) (device 1 closed symbols, device 2 open symbols).
For Accu-Chek there was a minimal effect of varying hematocrit for the low-level glucose sample, but at higher glucose levels there was an influence on the accuracy of results, with up to a 15% difference between the lowest and highest hematocrit levels (Figure 3B).
Effect of Interfering Substances Acetoacetate at concentration of 5 and 10 mmol/L had no influence on the 3 levels of BHB measurements for both StatStrip and Optium (Figure 4). Acetaminophen at concentrations of 0.33 and 0.66 mmol/L had only a slight negative influence on the 3 levels of BHB and no influence on glucose measurements for both StatStrip and Optium (Figures 4 and 5). The influence of ascorbic acid at a concentration of 0.29 and 0.59 mmol/L was pronounced on BHB measurements for both StatStrip and Optium, with falsely elevated BHB measurements for Optium and falsely reduced measurements for StatStrip (Figure 4). For the glucose samples only the lower level glucose was affected with Optium and Accu-Chek (up to 39% and 31% increase, respectively, as the ascorbic acid concentration increased) (Figure 5).
Ascorbic acid had no influence on StatStrip glucose measurements. For StatStrip and Optium none of the other interferences assessed (galactose 5.6 and 11.1 mmol/L, maltose 2.8 and 5.6 mmol/L, sodium 25 and 50 mmol/L, and uric acid 0.47 and 1.18 mmol/L) affected the accuracy of glucose measurements (Figure 5). For Accu-Chek maltose, sodium and uric acid at the concentrations tested had little effect on the accuracy of glucose measurements. However galactose had a significant effect across all 3 glucose levels with up to an 84% difference at low glucose levels and up to an 18% difference at higher levels (Figure 5).
Discussion The benefit of using POC testing as an alternative to centralized laboratory testing is often debated, but it is recognized that the availability of faster results leads to more efficient diagnosis and treatment, particularly when managing critically ill patients or patients with dysglycemia.21,22 Whole-blood POCT for BHB is widely accepted as part of the management of diabetes type 1 patients with
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Ceriotti et al Table 6. Method Comparison and Accuracy Indication for POC Glucose Methods. Meter system
N
Slope
Intercept (mg/dL)
R2
Meeting ISO 15197 (%)
Meeting CLSI 12A3 (%)
StatStrip 1 StatStrip 2 Optium 1 Optium 2 Accu-Chek 1 Accu-Chek 2
40 40 40 40 40 40
0.932 0.950 1.017 1.000 0.947 0.933
2.0 1.8 –7.6 –6.1 –0.47 –0.61
.9934 .9935 .9821 .9767 .9952 .9949
100.0 (40/40) 100.0 (40/40) 87.5 (35/40) 87.5 (35/40) 100.0 (40/40) 100.0 (40/40)
100.0 (40/40) 97.5 (39/40) 60.0 (24/40) 67.5 (27/40) 97.5 (39/40) 90.0 (36/40)
Figure 2. Bias distribution for glucose method correlation data: Difference of glycemia values between each POCT system (A StatStrip, B Optium, and C Accu-Chek) and the reference method plotted against the reference method, with solid lines representing ISO 15197 2013 and broken lines representing CLSI POCT 12-A3 limits.
hyperglycemia and DKA. A number of guidelines now recommend the use of whole-blood BHB testing,6 and recent
UK Joint British Diabetes guidelines also advocate goaldirected treatment of adult DKA to lower BHB levels and follow-up BHB testing to monitor BHB levels.7 Many handheld POC devices for markers such as BHB are often based on the design of POC glucose methods, as it is well recognized that the accuracy of whole-blood POC glucose methods can be influenced by a number of interfering factors that may be present in patients with critical or chronic diseases;13 the same limitations may affect the accuracy of measurement results for the other markers. This analytical study was performed on 2 commercially available combined glucose/BHB POC devices (StatStrip and Optium Freestyle), 1 of which (StatStrip) corrected for interferences including hematocrit. The interference study data showed that the hematocrit interference correction of StatStrip did ensure that the accuracy of glucose and BHB measurements was unaffected across a wide hematocrit range. The influence of hematocrit on the accuracy of glucose measurements of Optium Freestyle has previously been reported.23,24 In this study the BHB device also was affected, resulting in the characteristic interference pattern seen with glucose measurements, that is, falsely elevated BHB values when hematocrit is low and vice versa (Figures 3A, 3B). Although the Accu-Chek strip technology has recently been modified, there is still evidence of an influence of hematocrit as previously reported.24 The association of hematocrit interferences on glucose measurements directly leading to adverse risk in hospitalized patients managed as part of a tight glycemic control policy has recently been reported.25,26 In DKA patients it is not clear if the combined use of a POC Glucose/BHB method that exhibits hematocrit interference will lead to adverse decision making. A recent study indicated that venous blood POC error was not considered clinically significant in patients with DKA/HHS.27 The authors concluded that there is a low risk of causing hypoglycemia from excess insulin administration based on POC glucose error but stated that patient harm could occur by delaying correction of metabolic disturbances in patients with DKA/HHS. Therefore further investigation is required to determine if POC BHB methods affected by hematocrit will influence the management of ketonemia particularly in patients following a goal directed targeted treatment for DKA.7 A recent Canadian study28 indicated that the range of hematocrit levels seen in patients
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Figure 3. Influence of varying levels of hematocrit on beta hydroxybutyrate (BHB) and glucose values. (A) BHB values measured with StatStrip and Optium for 3 different levels of BHB samples. (B) Glucose values measured with StatStrip, Optium, and Accu-Chek for 3 different levels of glucose samples.
managed in the primary care varied from 20% to 60% and from 10% to 70% in a hospital setting. Both primary care and hospital populations included patients with diabetes, and this reiterates the importance of considering hematocrit as a risk factor for both glucose and BHB monitoring. As disease progresses in diabetes patients, they may become more exposed to hospitalization and increased uptake of medication. We looked at the influence of acetaminophen and ascorbic acid as medications that have been reported to adversely influence the accuracy of handheld POC glucose measurements. Acetaminophen at the concentrations assessed had no or very small influence, whereas ascorbic acid did. Intravenous ascorbic acid is used in the management of burns patients29 and has been advocated for use in managing oncology patients30 and also in diabetes patients on hemodialysis.31 A recent report highlighted a spurious elevation of glucose measurements with a POC device during administration of high-dose ascorbic acid in a patient with type 2 diabetes on hemodialysis, which masked a hypoglycemic event in the patient.31 In our study the Optium device exhibited falsely elevated readings for both BHB and glucose measurements with ascorbic acid (Figures 4 and 5), while only the BHB measurement was affected on StatStrip causing an underestimation of values (Figures 4 and 5). The enzymes used in some handheld POC glucose devices have been reported not to be specific for glucose and shown to react in the presence of maltose and galactose.14 Similarly it was recently reported that Optium BHB measurements were adversely influenced by acetoacetate, suggesting poor specificity of the BHB dehydrogenase enzyme used.32 This was contradicted in this study as acetoacetate at the concentrations tested did not influence the accuracy of BHB measurements (Figure 4). The Accu-Chek glucose strip technology has been recently modified to eliminate maltose interference. This
study confirmed that the presence of maltose at the concentrations tested had no influence on the accuracy of results. As previously reported however,33 the modification has not altered the specificity for other nonglucose sugars as glucose measurements were falsely elevated in the presence of galactose (Figure 5). The consequences of inaccurate readings in terms of managing neonates with galactosemia have previously been reported.34,35 This study focused primarily on assessing the current versions of combined glucose/BHB meter combinations with interferences reported to be associated with glucose meter adverse events. We did not assess other pathophysiological factors such as pH, pO2, and electrolytes in the in-laboratory specificity study as these are more appropriate to assess in patient clinical samples. StatStrip utilizes a modified glucose oxidase enzyme that potentially could be influenced by oxygen tension. This has previously been assessed in adult and neonatal critical care patients exhibiting a wide range of pO2 levels, and the accuracy of StatStrip glucose was unaffected.36,37 In an adult ICU study36 it was reported that StatStrip achieved a much higher level of accuracy than the GDH-based meters across 4 partial pressure oxygen ranges. In the NICU study37 abnormal levels of pH and electrolytes were also assessed and shown not to influence the accuracy of StatStrip. In a recent study it was reported that Accu-Chek POC glucose accuracy was influenced by acidemia, which can be present in DKA/HHS patients,27 and as such further investigation into the role of fluctuating pH ranges on POC meter performance is required. The study was not set up to assess the accuracy of the devices because the glucose measurements do not reflect the distribution of samples as outlined in new standards published since completion of the study. However an assessment of the correlation data in comparison to the new CLSI POCT 12 A320 performance criteria indicates that it will be
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Figure 4. Influence of acetaminophen, acetoacetate, and ascorbic acid on the beta hydroxybutyrate (BHB) values measured with StatStrip and Optium for 3 different levels of BHB samples
Figure 5. Influence of acetaminophen, ascorbic acid, galactose, maltose, uric acid, and sodium on the glucose values measured with StatStrip, Optium, and Accu-Chek for 3 different levels of glucose.
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challenging for some of the current POC glucose methods (Figure 2). At present there are no performance criteria established for BHB testing. Although StatStrip and Optium BHB methods correlated closely to the laboratory method, both methods showed a positive bias, even if of a different nature (constant for StatStrip, proportional for Optium) (Figure 1). If required, this can be compensated for with StatStrip as it allows a calibration adjustment to be implemented. The bias of Optium has previously been reported in a correlation assessment to a liquid chromatography–mass spectrometry BHB method.38 The specificity deficiencies for some of the POC glucose methods evaluated in this analytical study parallel those of similar studies, and there is evidence emerging of the direct consequences of these deficiencies on clinical decision making.23,24 However, although this analytical study also demonstrated that hematocrit and ascorbic acid can influence the accuracy of BHB results, the clinical significance and relevance of this to patient decision making require further investigation. In addition, we assessed only a small number of known glucose interference factors, and further investigation of other known factors may help determine the extent of the inaccuracy problem.
Conclusions In summary, combined POC glucose/BHB methods are now available to aid the management of dysglycemia in hospitalized diabetes patients. Their use in a hospital setting requires careful selection of instruments not sensitive to hematocrit variation and presence of interfering substances. Abbreviations BHB, beta hydroxybutyrate; CLSI, Clinical and Laboratory Standards Institute; DKA, diabetes ketoacidosis; ISO, International Organization for Standardization; ISPAD, International Society for Pediatric and Adolescent Diabetes; POC, point of care; QC, quality control.
Acknowledgments We thank Dr Andrei Malic for his valuable input in data evaluation and discussion.
Declaration of Conflicting Interests The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: FM, FL, and FV are full-time employees of A. Menarini Diagnostics.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by A. Menarini Diagnostics, Florence, Italy.
References 1. Pitkin AD, Rice MJ. Challenges to glycemic measurement in the perioperative and critically ill patient: a review. J Diabetes Sci Technol. 2009;3:1270-1281. 2. Corstjens AM, van der Horst I, Zijlstra JG, et al. Hyperglycaemia in critically ill patients: marker or mediator of mortality? Crit Care. 2006;10:216. 3. Fahy BG, Sheehy AM, Coursin DB. Glucose control in the intensive care unit. Crit Care Med. 2009;37:1769-1776. 4. Alter D, Deines G. Tight glycemic control and point-of-care testing. Clin Lab Med. 2009;29:511-522. 5. Prisco F, Picardi A, Iafusco D, et al. Blood ketone bodies in patients with recent-onset type 1 diabetes (a multicenter study). Pediatr Diabetes. 2006;7:223-228. 6. Savage MW, Dhatariya KK, Kilvert A, et al. Joint British Diabetes Societies guideline for the management of diabetic ketoacidosis. Diabetic Med. 2011;28:508-515. 7. Wolfsdorf J, Craig ME, Daneman D, et al. ISPAD clinical practice consensus guidelines 2009. Diabetic ketoacidosis. Pediatr Diabetes. 2009; 10(suppl 12):118-133. 8. Arora S, Henderson S, Long T, Menchine M. Diagnostic accuracy of point-of-care testing for diabetic ketoacidosis at emergency-department triage: beta-hydroxybutyrate versus the urine dipstick. Diabetes Care. 2011;34:852-854. 9. Charles RA, Bee YM, Eng PHK, Goh SY. Point-of-care blood ketone testing: screening for diabetic ketoacidosis at the emergency department. Singapore Med J. 2007;48:986-989. 10. Holstein A, Plaschke A, Vogel MY, Egberts EH. Prehospital management of diabetic emergencies—a population-based intervention study. Acta Anaesthesiol Scand. 2003;47:610-615. 11. Dungan K, Chapman J, Braithwaite SS, Buse J. Glucose measurement: confounding issues in setting targets for inpatient management. Diabetes Care. 2007;30:403-409. 12. Heinemann L. Quality of glucose measurement with blood glucose meters at the point-of-care: relevance of interfering factors. Diabetes Technol Ther. 2010;12:847-857. 13. Rebel A, Rice M, Fahy B. The accuracy of point-of-care glucose measurements J Diabetes Sci Technol. 2012;6:396-411. 14. Schleis TG. Interference of maltose, icodextrin, galactose or xylose with some blood glucose monitoring systems. Pharmacotherapy. 2007;27:1313-1321. 15. Clinical and Laboratory Standards Institute. Evaluation of Precision Performance of Quantitative Measuring Methods; Approved Guideline—Second Edition. CLSI document EP05-A2. Wayne, PA: CLSI; 2004. 16. Clinical and Laboratory Standards Institute. Measurement Procedure Comparison and Bias Estimation Using Patient Samples; Approved Guideline—Third Edition. CLSI document EP09-A3. Wayne, PA: CLSI; 2013. 17. Clinical and Laboratory Standards Institute. Interference Testing in Clinical Chemistry. Approved Guideline—Second Edition. CLSI document EP07-A2. Wayne, PA: CLSI; 2005. 18. Clinical and Laboratory Standards Institute. Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline. CLSI document EP06-A. Wayne, PA: CLSI; 2003. 19. International Organization for Standardization. In Vitro Diagnostic Test Systems—Requirements for Blood Glucose
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Ceriotti et al Monitoring Systems for Selftesting in Managing Diabetes Mellitus. ISO/DIS 15197:2013. 20. Clinical and Laboratory Standards Institute. Point of Care Blood Glucose Testing in Acute and Chronic Care Facilities. Approved Guideline—Third Edition. CLSI document POCT12-A3. Wayne, PA: CLSI; 2013. 21. Plebani M, Lippi G. Point of care testing, evolving scenarios and innovative perspective. Clin Chem Lab Med. 2014;52: 309-311. 22. Martin J, Blobner M, Busch R, Moser N, Kochs E, Luppa PB. Point-of-care testing on admission to the intensive care unit: lactate and glucose independently predict mortality. Clin Chem Lab Med. 2013;51:405-412. 23. Daves M, Cemin R, Fattor B, et al. Evaluation of hematocrit bias on blood glucose measurement with six different portable glucose meters. Biochem Med. 2011;21:306-311. 24. Dimeski G, Jones BW, Tilley V, Greenslade MN, Russell AW. Glucose meters: evaluation of the new formulation measuring strips from Roche (Accu-Chek) and Abbott (MediSense). Ann Clin Biochem. 2010;47:358-365. 25. Pidcoke H, Wade C, Mann E, et al. Anemia causes hypoglycemia in intensive care unit patients due to error in single-channel glucometers: Methods of reducing patient risk. Crit Care Med. 2010;38;471-476. 26. Denfeld Q, Goodell T, Stafford K, Kazmierczak S. Precision and accuracy comparison of point-of-care and laboratory glucose concentrations in cardiothoracic surgery patients. J Cardiovasc Nurs. 2011;26:512-518. 27. Corl D, Yin T, Mills M, et al. Evaluation of point-of-care blood glucose measurements in patients with diabetic ketoacidosis or hyperglycemic hyperosmolar syndrome. J Diabetes Sci Technol. 2013;7:1265-1274. 28. Lyon ME, Lyon AW. Patient acuity exacerbates discrepancy between whole blood and plasma methods through error in molality to molarity conversion: “Mind the gap!” Clin Biochem. 2011;44:412-417.
29. Kahn SA, Beers RJ, Lentz CW. Resuscitation after severe burn injury using high dose ascorbic acid: a retrospective review. J Burn Care Res. 2011;32:110-117. 30. McConnell MJ, Herst P. Ascorbate combination therapy: New tool in the anticancer toolbox? Sci Transl Med. 2014;6:222fs6. 31. Kim SK, Hahm JR, Kim HS, et al. Spurious elevation of glucose concentration during administration of high dose of ascorbic acid in a patient with type 2 diabetes on hemodialysis. Yonsei Med J. 2013;54:1289-1292. 32. Khan AS, Talbot JA, Tieszen KL, Gardener EA, Gibson JM, New JP. Evaluation of a bedside blood ketone sensor: the effects of acidosis, hyperglycaemia and acetoacetate on sensor performance. Diabet Med. 2004;21:782-785. 33. Zaman Z. Effect of non-glucose sugars and haematocrit on glucose measurements with Roche Accu-Chek Performa glucose strips. Ann Clin Biochem. 2010;47;494-495. 34. Mathew V, Ramakrishnan A, Srinivasan R, Sushma K, Bantwal G, Ayyar V. Erroneous glucose recordings while using mutant variant of quinoprotein glucose dehydrogenase glucometer in a child with galactosemia. Indian J Endocrinol Metab. 2013;17(suppl 1):S289-S291. 35. Newman JD, Ramsden CA, Balazs DH Monitoring neonatal hypoglycaemia with the Accu-check advantage II glucose meter: the cautionary tale of galactosaemia. Clin Chem. 2002;48:2071. 36. Hopf S, Graf B, Gruber M. Comparison of Point-of-Care Testing glucose results from intensive care patients measured with network-ready devices. Diabetes Technol Ther. 2011;13:1047-1056. 37. Tendl KA, Christoph J, Bohn A, Herkner KR, Pollak A, Prusa AR. Two site evaluation of the performance of a new generation point-of-care glucose meter for use in a neonatal intensive care unit. Clin Chem Lab Med. 2013;51:1747-1754. 38. Janssen MJ, Hendrickx BH, Habets-van der Poel CD, van den Bergh J, Haagen A, Bakker JA. Accuracy of the Precision® point-of-care ketone test, examined by LC-MS/MS in the same fingerstick sample. Clin Chem Lab Med. 2010;48:1781-1784.
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research-article2014
DSTXXX10.1177/1932296814563351Journal of Diabetes Science and TechnologyCeriotti et al
Original Article
Comparative Performance Assessment of Point-of-Care Testing Devices for Measuring Glucose and Ketones at the Patient Bedside
Journal of Diabetes Science and Technology 2015, Vol. 9(2) 268–277 © 2014 Diabetes Technology Society Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1932296814563351 dst.sagepub.com
Ferruccio Ceriotti, MD1, Ewa Kaczmarek1, Elena Guerra, PhD1, Fabrizio Mastrantonio, PhD2, Fausto Lucarelli, PhD2, Francesco Valgimigli, PhD2, and Andrea Mosca, PhD3
Abstract Point-of-care (POC) testing devices for monitoring glucose and ketones can play a key role in the management of dysglycemia in hospitalized diabetes patients. The accuracy of glucose devices can be influenced by biochemical changes that commonly occur in critically ill hospital patients and by the medication prescribed. Little is known about the influence of these factors on ketone POC measurements. The aim of this study was to assess the analytical performance of POC hospital wholeblood glucose and ketone meters and the extent of glucose interference factors on the design and accuracy of ketone results. StatStrip glucose/ketone, Optium FreeStyle glucose/ketone, and Accu-Chek Performa glucose were also assessed and results compared to a central laboratory reference method. The analytical evaluation was performed according to Clinical and Laboratory Standards Institute (CLSI) protocols for precision, linearity, method comparison, and interference. The interferences assessed included acetoacetate, acetaminophen, ascorbic acid, galactose, maltose, uric acid, and sodium. The accuracies of both Optium ketone and glucose measurements were significantly influenced by varying levels of hematocrit and ascorbic acid. StatStrip ketone and glucose measurements were unaffected by the interferences tested with exception of ascorbic acid, which reduced the higher level ketone value. The accuracy of Accu-Chek glucose measurements was affected by hematocrit, by ascorbic acid, and significantly by galactose. The method correlation assessment indicated differences between the meters in compliance to ISO 15197 and CLSI 12-A3 performance criteria. Combined POC glucose/ketone methods are now available. The use of these devices in a hospital setting requires careful consideration with regard to the selection of instruments not sensitive to hematocrit variation and presence of interfering substances. Keywords point-of-care testing, glucose, ketones, performance evaluation
Point-of-care testing (POCT) devices for monitoring glucose and ketones can play a key role in the management of dysglycemia in hospitalized diabetes patients. It is well recognized that POC monitoring of blood glucose levels in hospitalized acute and critical care patients is important for managing and maintaining normal glycemia to reduce patient morbidity and mortality and improve patient recovery.1-3 The use of handheld glucose meters for intermittent monitoring of patient glucose levels in hospitalized patients requiring intravenous insulin allows for rapid clinical decision making and immediate treatment responses.4 Similarly the measurement of whole-blood ketone (beta hydroxybutyrate [BHB]) plays an important role in the management of adult and pediatric diabetes ketoacidosis (DKA).5 Recent guidelines have advocated the use of whole-blood measurement of BHB for the management of DKA and for monitoring target directed therapy.6,7
Combined testing for glucose and BHB is often used in accident and emergency departments8,9 and can be an aid on ambulances and in a prehospital setting to identify and triage severely hyperglycemic patients.10 A high level of accuracy is required for POC glucose and BHB devices to ensure that patients are managed optimally. However, it has 1
Istituto Scientifico Ospedale San Raffaele, Servizio di Medicina di Laboratorio, Milan, Italy 2 A.Menarini Diagnostics, Florence, Italy 3 Dip. di Fisiopatologia Medico-Chirurgica e dei Trapianti, Centro per la Riferibilità Metrologica in Medicina di Laboratorio (CIRME), Università degli Studi di Milano, Milan, Italy Corresponding Author: Ferruccio Ceriotti, MD, Istituto Scientifico Ospedale San Raffaele, Servizio di Medicina di Laboratorio, Via Olgettina 60, Milan 20132, Italy. Email: [email protected]
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Ceriotti et al been well reported that the accuracy of many hospital glucose meters can be affected by the biochemical and physiological changes that commonly occur in acute, chronic, and critically ill hospital patients and also by the medication prescribed.11,12 A wide range of endogenous and exogenous substances present in the blood of critically ill patients can donate electrons to nonspecifically initiate the electrochemical reaction and cause interference with glucose results, giving elevated values.13 An increasingly large number of interfering factors have been associated with inaccurate glucose readings including abnormal hematocrit, oxygen tension and pH, uric acid, sodium, lipids, and administration of drugs (ascorbic acid, acetaminophen, dopamine). It is also well known that some of the enzymes used in glucose meters are not specific for glucose and can give falsely elevated results when other nonglucose sugars are present in patient samples.14 POC meters for wholeblood ketone testing are based on the same design principle as commonly established glucose meter. Little has been reported on the influence of known glucose meter interferences on the accuracy of ketone meters. The aim of this study was to undertake an analytical assessment of POC hospital whole-blood glucose and ketone meters to determine performance reliability and to determine the extent of glucose interference factors on the design and accuracy of ketone results.
Materials and Methods Instrumentation Two POC devices offering both glucose and ketone testing were assessed: StatStrip glucose/ketone (Nova Biomedical Waltham, MA, U.S.A) and Optium FreeStyle glucose/ketone (Abbott Diabetes Care Roma, Italy). In addition, the AccuChek Performa glucose only meter (Roche Diagnostic Milan, Italy) was also assessed. All 3 meter strip technologies represented the latest version provided for hospital use at the time of the study available. The StatStrip glucose strip technology is a modified glucose oxidase-based amperometric test system, and the ketone strip technology uses a BHB dehydrogenase enzyme based amperometric strip. Both strips incorporate hematocrit and other interference correction; Optium Freestyle uses an electrochemical glucose dehydrogenase/coenzyme nicotinamide adenine dinucleotide–based amperometric strip, and the ketone strip technology uses a BHB dehydrogenase enzyme based amperometric strip; Accu-Chek uses a glucose dehydrogenase/coenzyme pyrroloquinolone quinone–based amperometric strip recently modified to reduce the influence of maltose interference. The calibration of the StatStrip strips is predetermined by the manufacturer and requires no additional calibration code, whereas the Accu-Chek and Optium meters require the use of a calibration code for calibration of each strip lot. The ADVIA 2400 (Siemens Healthcare Diagnostics Milan, Italy)
laboratory analyzer plasma hexokinase method was used as the reference method for glucose measurements. For BHB testing the Ranbut enzymatic reagents (Randox Laboratories Ltd London, UK) run on Uvikon 940 spectrophotometer (Tegimenta Basel, Switzerland) was used as the reference method.
Precision Testing This was performed as according to Clinical and Laboratory Standards Institute (CLSI) guideline EP05-A2.15 Within-day precision was assessed using quality control material specific for each meter supplier and with 3 whole-blood samples containing low, medium, and high levels of glucose or BHB. For glucose testing lithium heparin whole blood was collected from a volunteer and mixed continuously 18 to 24 hours before commencing the study. In this specimen, red blood cell glycolytic activity reduced the glucose concentration to less than 35 mg/dL. For BHB testing lithium heparin whole blood was collected from a volunteer just prior to performing the precision testing. The 3 different levels of samples were prepared by adding varying amounts of a concentrated glucose or BHB stock solution to aliquots of the donated sample. For within-day precision 20 replicates of each of the 3 blood sample levels and quality control samples were tested by each of the meters. Day-to-day precision was assessed according to the CLSI EP5-A2 protocol using quality control (QC) material specific to each meter. Each sample was measured in duplicate 2 times a day for 20 days on each meter and with the reference method.
Method Comparison The comparison was performed according to CLSI guideline EP09-A3.16 Forty lithium heparin samples were collected from hospitalized patients. Each sample was measured in duplicate on each POC meter and for each analyte. The order of testing was randomized, and testing was completed in 5 minutes to reduce the influence of glycolsis on the pattern of results. This approach was taken throughout the analytical studies undertaken. The remainder of each aliquot was immediately centrifuged to prepare a plasma sample for glucose and BHB measurement with the respective reference methods. To ensure glucose and BHB sample concentrations reflected the entire analytical range, higher level samples were prepared by spiking varying amounts of a concentrated glucose (20% wt/vol) solution or BHB (0.5 mol/L) solution to samples. Low-level glucose samples were obtained by leaving samples mixing for 12 to 24 hours.
Hematocrit Interference A lithium heparin whole-blood specimen was collected, as previously described. Three glucose and BHB levels were
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Table 1. Within-Day Precision for Ketone POC Devices. Whole blood sample 1
Whole blood sample 2
Whole blood sample 3
Quality control materials
Method
N
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
StatStrip meter 1 StatStrip meter 2 Optium meter 1 Optium meter 2 Reference
20 20 20 20 5
1.09 1.12 0.83 0.81 0.86
0.04 0.06 0.04 0.02 0.02
3.38 5.27 5.38 2.78 2.74
4.22 4.17 3.95 3.85 3.53
0.14 0.11 0.14 0.11 0.08
3.29 2.62 3.44 2.98 2.17
6.52 5.99 6.52 6.23 5.39
0.22 0.29 0.16 0.11 0.09
3.42 4.87 2.40 1.80 1.67
2.64 3.11 0.70 0.77 N/A
0.10 0.16 0.05 0.02 N/A
3.95 5.17 6.4 2.78 N/A
prepared by adding the appropriate volume of a concentrated glucose (20% wt/vol) solution or BHB (0.5 mol/L) solution to achieve target glucose values of 20-60, 200-275, and 325-400 mg/dL and target ketone values of approximately 0.3-0.7, 2.2-2.8, and 3.7-4.3 mmol/L. For each glucose and BHB sample, 5 further aliquots were prepared and the hematocrit levels were adjusted after centrifugation and dilution to provide hematocrit levels of 28%, 38%, 48%, 59%, and 68% across each of the 3 concentrations. The actual hematocrit value for each of the aliquot preparations was confirmed using a StatSpin MP microhematocrit centrifuge (Iris Sample Processing, Westwood, MA). Each sample was tested 6 times by each strip meter system. The remainder of the sample was centrifuged immediately, and the plasma glucose and BHB level were tested by the respective reference methods.
Specificity Interference Studies This was performed according to CLSI guideline EP07-A2.17 Three different interfering substances (acetaminophen, ascorbic acid, and acetoacetate) were assessed for influence on the accuracy of BHB measurements, and 6 different interfering substances (acetaminophen, ascorbic acid, acetoacetate, galactose, maltose monohydrate, uric acid, and sodium) were assessed for influence on the accuracy of glucose measurements. Three samples at increasing concentration of BHB or glucose were prepared as described previously. For each concentration level a further 3 samples were prepared with increasing concentrations of each interfering substance. Each whole-blood sample was analyzed 6 times with each of the meters. The remainder of the aliquot was centrifuged, and the plasma glucose and BHB levels were assayed by the laboratory reference methods.
Linearity This was performed according to CLSI guideline EP06-A.18 For glucose linearity testing 11 samples were prepared by scalar dilutions of a high-concentrated sample with a lowlevel one. For BHB linearity testing 7 samples were prepared by adding varying amounts of a concentrated BHB (0.5 mol/L) stock solution to aliquots of a donated sample. Each sample was measured 10 times on each POCT meter.
Data Analysis All calculations were performed on MS Excel. For an assessment of the accuracy of each glucose device, the percentage bias of each glucose meter result compared with the respective reference method result was calculated and assessed by comparison to performance criteria specified in ISO 15197 201319 and CLSI guideline POCT12-A3.20
Results Precision Testing All ketone and glucose testing showed good within-day precision for whole-blood samples as well as for the manufacturers QC materials (Tables 1 and 2). The ketone and glucose meters also showed good day-to-day reproducibility (Tables 3 and 4). For glucose, the 3.3% desirable coefficient of variation (CV) according to biological variability was reached in most of the cases.
Method Comparison For the comparison of BHB methods, 48 samples were tested and the mean plasma BHB value was 2.17 mmol/L (range 0.11-5.65 mmol/L). Both methods showed a good correlation to the laboratory reference method (Table 5 and Figure 1), but both also demonstrated a positive bias. In the case of StatStrip there was a significant constant bias of about 0.2 mmol/L (Figure 1A), while Optium showed a relevant proportional bias (Figure 1B). For Optium the positive bias increased at higher levels of BHB, and this resulted in a higher level of discordance when assessing concordance of results to International Society for Pediatric and Adolescent Diabetes (ISPAD) decision-making criteria (Table 5).7 For the comparison of glucose methods, 40 samples were tested and the mean plasma reference glucose value was 109 mg/dL (range, 28-398 mg/dL). All 3 POC glucose methods showed a good correlation to the reference method (Table 6). Although the method correlation wasn’t set up to assess accuracy in compliance to current standards and guidance, there was some differences seen between the meters in comparison to ISO 1519719 and CLSI 12-A320 performance criteria (Table 6 and Figure 2).
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Ceriotti et al Table 2. Within-Day Precision for Glucose POC Devices. Whole blood sample 1
Whole blood sample 2
Whole blood sample 3
Quality control 1
Quality control 2
Method
N
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
Mean
SD
% CV
StatStrip meter 1 StatStrip meter 2 Optium meter 1 Optium meter 2 Accu-Chek 1 Accu-Chek 2 Reference
20 20 20 20 20 20 5
46.0 46.8 31.3 30.1 37.5 37.6 48.4
1.62 2.35 1.59 1.76 0.94 1.05 0.55
3.53 5.03 5.07 5.86 2.52 2.8 1.13
116.3 115.5 116.1 115.5 110.3 110.0 119.6
2.30 2.19 2.83 4.46 1.97 2.28 0.55
1.97 1.90 2.44 3.86 1.79 2.08 0.46
244.6 243.5 226.6 225.6 229.0 225.8 250.2
3.76 3.24 5.35 5.07 3.89 4.12 0.4
1.54 1.33 2.36 2.25 1.70 1.82 0.2
61.3 64.5 61.4 61.9 45.1 42.3 N/A
2.22 1.96 2.33 2.94 1.52 0.66 N/A
3.63 3.04 3.79 4.75 3.37 1.55 N/A
282.3 294.2 270.5 277.5 307.4 292.5 N/A
6.33 9.36 8.91 13.3 4.22 3.56 N/A
2.24 3.18 3.30 4.80 1.37 1.22 N/A
Table 3. Day-to-Day Precision for POC Ketone Devices. StatStrip 1
StatStrip 2
Optium 1
2.73 7.4 8.0 3.4 1.0 10.2
2.76 5.5 6.1 3.5 0.6 8.0
0.72 4.7 3.6 2.4 0.0 5.5
Mean (mmol/L) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%)
Optium 2 0.69 4.3 4.1 1.5 0.1 5.3
Reference method 1.11 3.8 4.1 1.4 0.1 5.1
CV = overall imprecision; CVbd = between-day imprecision, CVbr = between-run imprecision, CVr = repeatability of duplicate; CVw = within-lab imprecision.
Table 4. Day-to-Day Precision for POC Glucose Devices.
Level 1 Mean (mg/dl) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%) Level 2 Mean (mg/dl) CVr (%) CVw (%) CVbd (%) CVbr (%) CV (%)
StatStrip 1
StatStrip 2
Optium 1
Optium 2
Accu-Chek 1
Accu-Chek 2
Reference method
61.2 3.0 2.9 1.3 2.5 3.8
63.5 3.2 2.2 1.3 0.0 3.4
60.3 3.1 3.2 2.5 0.2 4.6
62.6 3.7 5.1 1.3 0.7 5.9
44.6 1.9 1.4 1.5 0.1 2.4
43.2 2.0 1.3 1.0 0.0 2.1
86.0 0.4 0.9 1.7 0.7 1.8
287.6 1.8 2.2 0.7 9.2 2.6
293.0 1.8 2.0 1.5 6.9 2.8
276.8 2.1 3.6 2.3 1.7 4.5
283.2 1.9 2.3 3.3 0.5 4.2
302.2 1.3 0.9 1.4 0.3 1.9
294.6 2.5 2.0 0.8 3.1 2.8
229.2 0.5 0.6 1.5 0.4 1.6
CV = overall imprecision; CVbd = between-day imprecision, CVbr = between-run imprecision, CVr = repeatability of duplicate; CVw = within-lab imprecision.
Hematocrit Interference The final adjusted hematocrit levels were confirmed to be 24%, 38%, 43%, 56%, and 66% for the BHB samples and 24%, 36%, 45%, 56%, and 62% for the glucose samples. For StatStrip over the hematocrit range of 38-56% there was a minimal effect on BHB measurements across the 3 levels tested, while at a hematocrit level of 24% the StatStrip BHB reading was 20% lower and at a hematocrit level of 66% the
BHB reading was 12% higher compared to 43% hematocrit level (Figure 3A). For glucose there was a minimal effect of varying hematocrit levels over the range tested (Figure 3B) The influence of hematocrit was pronounced for Optium device for both glucose and ketone measurements across all concentration levels tested (Figure 3A). For the glucose samples there is up to a 60% difference between the lowest and highest hematocrit levels, for the BHB samples up to 300%.
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Table 5. Method Comparison for POC Ketone Methods and Concordance of Classification According to International Society for Pediatric and Adolescent Diabetes (ISPAD) Decision-Making Criteria. ISPAD performance criteria number of results within each range
Regression parameters Meter system
N
Slope
Intercept (mmol/L)
R2
1.5-36 mmol/L
>36 mmol/L
Reference method StatStrip 1 StatStrip 2 Optium 1 Optium 2
48 48 48 48
0.9950 0.9761 1.4028 1.3677
0.15 0.19 –0.29 –0.28
.9776 .9767 .9764 .9769
4 4 4 4 4
19 17 16 19 18
16 17 19 8 10
9 10 9 17 16
Figure 1. Method comparison between reference method and beta hydroxybutyrate concentrations measured by StatStrip (A) and Optium (B) (device 1 closed symbols, device 2 open symbols).
For Accu-Chek there was a minimal effect of varying hematocrit for the low-level glucose sample, but at higher glucose levels there was an influence on the accuracy of results, with up to a 15% difference between the lowest and highest hematocrit levels (Figure 3B).
Effect of Interfering Substances Acetoacetate at concentration of 5 and 10 mmol/L had no influence on the 3 levels of BHB measurements for both StatStrip and Optium (Figure 4). Acetaminophen at concentrations of 0.33 and 0.66 mmol/L had only a slight negative influence on the 3 levels of BHB and no influence on glucose measurements for both StatStrip and Optium (Figures 4 and 5). The influence of ascorbic acid at a concentration of 0.29 and 0.59 mmol/L was pronounced on BHB measurements for both StatStrip and Optium, with falsely elevated BHB measurements for Optium and falsely reduced measurements for StatStrip (Figure 4). For the glucose samples only the lower level glucose was affected with Optium and Accu-Chek (up to 39% and 31% increase, respectively, as the ascorbic acid concentration increased) (Figure 5).
Ascorbic acid had no influence on StatStrip glucose measurements. For StatStrip and Optium none of the other interferences assessed (galactose 5.6 and 11.1 mmol/L, maltose 2.8 and 5.6 mmol/L, sodium 25 and 50 mmol/L, and uric acid 0.47 and 1.18 mmol/L) affected the accuracy of glucose measurements (Figure 5). For Accu-Chek maltose, sodium and uric acid at the concentrations tested had little effect on the accuracy of glucose measurements. However galactose had a significant effect across all 3 glucose levels with up to an 84% difference at low glucose levels and up to an 18% difference at higher levels (Figure 5).
Discussion The benefit of using POC testing as an alternative to centralized laboratory testing is often debated, but it is recognized that the availability of faster results leads to more efficient diagnosis and treatment, particularly when managing critically ill patients or patients with dysglycemia.21,22 Whole-blood POCT for BHB is widely accepted as part of the management of diabetes type 1 patients with
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Ceriotti et al Table 6. Method Comparison and Accuracy Indication for POC Glucose Methods. Meter system
N
Slope
Intercept (mg/dL)
R2
Meeting ISO 15197 (%)
Meeting CLSI 12A3 (%)
StatStrip 1 StatStrip 2 Optium 1 Optium 2 Accu-Chek 1 Accu-Chek 2
40 40 40 40 40 40
0.932 0.950 1.017 1.000 0.947 0.933
2.0 1.8 –7.6 –6.1 –0.47 –0.61
.9934 .9935 .9821 .9767 .9952 .9949
100.0 (40/40) 100.0 (40/40) 87.5 (35/40) 87.5 (35/40) 100.0 (40/40) 100.0 (40/40)
100.0 (40/40) 97.5 (39/40) 60.0 (24/40) 67.5 (27/40) 97.5 (39/40) 90.0 (36/40)
Figure 2. Bias distribution for glucose method correlation data: Difference of glycemia values between each POCT system (A StatStrip, B Optium, and C Accu-Chek) and the reference method plotted against the reference method, with solid lines representing ISO 15197 2013 and broken lines representing CLSI POCT 12-A3 limits.
hyperglycemia and DKA. A number of guidelines now recommend the use of whole-blood BHB testing,6 and recent
UK Joint British Diabetes guidelines also advocate goaldirected treatment of adult DKA to lower BHB levels and follow-up BHB testing to monitor BHB levels.7 Many handheld POC devices for markers such as BHB are often based on the design of POC glucose methods, as it is well recognized that the accuracy of whole-blood POC glucose methods can be influenced by a number of interfering factors that may be present in patients with critical or chronic diseases;13 the same limitations may affect the accuracy of measurement results for the other markers. This analytical study was performed on 2 commercially available combined glucose/BHB POC devices (StatStrip and Optium Freestyle), 1 of which (StatStrip) corrected for interferences including hematocrit. The interference study data showed that the hematocrit interference correction of StatStrip did ensure that the accuracy of glucose and BHB measurements was unaffected across a wide hematocrit range. The influence of hematocrit on the accuracy of glucose measurements of Optium Freestyle has previously been reported.23,24 In this study the BHB device also was affected, resulting in the characteristic interference pattern seen with glucose measurements, that is, falsely elevated BHB values when hematocrit is low and vice versa (Figures 3A, 3B). Although the Accu-Chek strip technology has recently been modified, there is still evidence of an influence of hematocrit as previously reported.24 The association of hematocrit interferences on glucose measurements directly leading to adverse risk in hospitalized patients managed as part of a tight glycemic control policy has recently been reported.25,26 In DKA patients it is not clear if the combined use of a POC Glucose/BHB method that exhibits hematocrit interference will lead to adverse decision making. A recent study indicated that venous blood POC error was not considered clinically significant in patients with DKA/HHS.27 The authors concluded that there is a low risk of causing hypoglycemia from excess insulin administration based on POC glucose error but stated that patient harm could occur by delaying correction of metabolic disturbances in patients with DKA/HHS. Therefore further investigation is required to determine if POC BHB methods affected by hematocrit will influence the management of ketonemia particularly in patients following a goal directed targeted treatment for DKA.7 A recent Canadian study28 indicated that the range of hematocrit levels seen in patients
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Figure 3. Influence of varying levels of hematocrit on beta hydroxybutyrate (BHB) and glucose values. (A) BHB values measured with StatStrip and Optium for 3 different levels of BHB samples. (B) Glucose values measured with StatStrip, Optium, and Accu-Chek for 3 different levels of glucose samples.
managed in the primary care varied from 20% to 60% and from 10% to 70% in a hospital setting. Both primary care and hospital populations included patients with diabetes, and this reiterates the importance of considering hematocrit as a risk factor for both glucose and BHB monitoring. As disease progresses in diabetes patients, they may become more exposed to hospitalization and increased uptake of medication. We looked at the influence of acetaminophen and ascorbic acid as medications that have been reported to adversely influence the accuracy of handheld POC glucose measurements. Acetaminophen at the concentrations assessed had no or very small influence, whereas ascorbic acid did. Intravenous ascorbic acid is used in the management of burns patients29 and has been advocated for use in managing oncology patients30 and also in diabetes patients on hemodialysis.31 A recent report highlighted a spurious elevation of glucose measurements with a POC device during administration of high-dose ascorbic acid in a patient with type 2 diabetes on hemodialysis, which masked a hypoglycemic event in the patient.31 In our study the Optium device exhibited falsely elevated readings for both BHB and glucose measurements with ascorbic acid (Figures 4 and 5), while only the BHB measurement was affected on StatStrip causing an underestimation of values (Figures 4 and 5). The enzymes used in some handheld POC glucose devices have been reported not to be specific for glucose and shown to react in the presence of maltose and galactose.14 Similarly it was recently reported that Optium BHB measurements were adversely influenced by acetoacetate, suggesting poor specificity of the BHB dehydrogenase enzyme used.32 This was contradicted in this study as acetoacetate at the concentrations tested did not influence the accuracy of BHB measurements (Figure 4). The Accu-Chek glucose strip technology has been recently modified to eliminate maltose interference. This
study confirmed that the presence of maltose at the concentrations tested had no influence on the accuracy of results. As previously reported however,33 the modification has not altered the specificity for other nonglucose sugars as glucose measurements were falsely elevated in the presence of galactose (Figure 5). The consequences of inaccurate readings in terms of managing neonates with galactosemia have previously been reported.34,35 This study focused primarily on assessing the current versions of combined glucose/BHB meter combinations with interferences reported to be associated with glucose meter adverse events. We did not assess other pathophysiological factors such as pH, pO2, and electrolytes in the in-laboratory specificity study as these are more appropriate to assess in patient clinical samples. StatStrip utilizes a modified glucose oxidase enzyme that potentially could be influenced by oxygen tension. This has previously been assessed in adult and neonatal critical care patients exhibiting a wide range of pO2 levels, and the accuracy of StatStrip glucose was unaffected.36,37 In an adult ICU study36 it was reported that StatStrip achieved a much higher level of accuracy than the GDH-based meters across 4 partial pressure oxygen ranges. In the NICU study37 abnormal levels of pH and electrolytes were also assessed and shown not to influence the accuracy of StatStrip. In a recent study it was reported that Accu-Chek POC glucose accuracy was influenced by acidemia, which can be present in DKA/HHS patients,27 and as such further investigation into the role of fluctuating pH ranges on POC meter performance is required. The study was not set up to assess the accuracy of the devices because the glucose measurements do not reflect the distribution of samples as outlined in new standards published since completion of the study. However an assessment of the correlation data in comparison to the new CLSI POCT 12 A320 performance criteria indicates that it will be
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Figure 4. Influence of acetaminophen, acetoacetate, and ascorbic acid on the beta hydroxybutyrate (BHB) values measured with StatStrip and Optium for 3 different levels of BHB samples
Figure 5. Influence of acetaminophen, ascorbic acid, galactose, maltose, uric acid, and sodium on the glucose values measured with StatStrip, Optium, and Accu-Chek for 3 different levels of glucose.
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challenging for some of the current POC glucose methods (Figure 2). At present there are no performance criteria established for BHB testing. Although StatStrip and Optium BHB methods correlated closely to the laboratory method, both methods showed a positive bias, even if of a different nature (constant for StatStrip, proportional for Optium) (Figure 1). If required, this can be compensated for with StatStrip as it allows a calibration adjustment to be implemented. The bias of Optium has previously been reported in a correlation assessment to a liquid chromatography–mass spectrometry BHB method.38 The specificity deficiencies for some of the POC glucose methods evaluated in this analytical study parallel those of similar studies, and there is evidence emerging of the direct consequences of these deficiencies on clinical decision making.23,24 However, although this analytical study also demonstrated that hematocrit and ascorbic acid can influence the accuracy of BHB results, the clinical significance and relevance of this to patient decision making require further investigation. In addition, we assessed only a small number of known glucose interference factors, and further investigation of other known factors may help determine the extent of the inaccuracy problem.
Conclusions In summary, combined POC glucose/BHB methods are now available to aid the management of dysglycemia in hospitalized diabetes patients. Their use in a hospital setting requires careful selection of instruments not sensitive to hematocrit variation and presence of interfering substances. Abbreviations BHB, beta hydroxybutyrate; CLSI, Clinical and Laboratory Standards Institute; DKA, diabetes ketoacidosis; ISO, International Organization for Standardization; ISPAD, International Society for Pediatric and Adolescent Diabetes; POC, point of care; QC, quality control.
Acknowledgments We thank Dr Andrei Malic for his valuable input in data evaluation and discussion.
Declaration of Conflicting Interests The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: FM, FL, and FV are full-time employees of A. Menarini Diagnostics.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by A. Menarini Diagnostics, Florence, Italy.
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