Can J Diabetes xxx (2015) 1–2
Contents lists available at ScienceDirect
Canadian Journal of Diabetes journal homepage: www.canadianjournalofdiabetes.com
Letter to the Editor
Glucose Data-Mining Study Inconclusive
To the Editor: Data derived from laboratory information systems (LIS) can provide evidence either to support or refute the adequacy of current laboratory practices such as laboratory glucose meter comparison investigations. Conclusions drawn from such studies depend upon the breadth of the data extracted and the study design. The report by Naugler et al assessed the accuracy of self-monitoring blood glucose (SMBG) devices by mining LIS data for paired capillary blood-glucose results measured by glucose meters and venous serum-glucose results analyzed in a central laboratory (1). This approach enabled access to a wide range of data, but the design did not consider factors known to affect glucose testing, such as prandial status, hematocrit, partial pressure of oxygen (pO2), triglycerides and uric acid. Naugler et al reported that 96.3% of meter-glucose values were within 20% of the serum-glucose values (the analytic standard at the time of data collection) which, given the observational nature of the data, shows remarkable compliance with the standard. They concluded “that 1 in 40 meters is so inaccurate (>2 mmol/L) that the resulting errors would potentially result in an inappropriate change in insulin dosage.” This conclusion is based on an assumption that capillary and venous blood would have identical glucose levels, which may often be a false assumption. Equivalence in glucose concentration between venous and capillary blood can be assumed only when testing fasting specimens (2). Numerous studies have shown that after a meal, glucose load or, in glucose clamping experiments, capillary glucose values can be 20% to 25% higher than venous plasma values. The capillary-venous glucose difference is dependent on insulin’s activity and its action on glucose. In 1976, in advance of patient SMBG, Larsson-Cohn described these large differences in people without diabetes following oral glucose tolerance testing (3). In 2001, Kuwa et al applied the results of oral glucose tolerance testing in 75 healthy volunteers to demonstrate the impact of the capillary-venous glucose bias on the comparability of various SMBG devices (4). In 2009, Kempeet al described the use of glucose clamp experiments to assess the value of capillary and venous testing in intensive care units (5). They concluded that although capillary and venous blood glucose levels are similar in the steady state, they may differ under dynamic conditions, with the differences affecting insulin dosing decisions. This lesson of the importance of capillary venous differences is now being confirmed in diabetes screening studies using the modalities of capillary and venous blood testing (6). For patients who can assess the accuracy of their SMBG devices only by a capillary measurement compared to the central laboratory venous measurement, we recommend
1499-2671/$ – see front matter Ó 2015 Canadian Diabetes Association http://dx.doi.org/10.1016/j.jcjd.2015.03.002
performing these measurements at least 4.5 hours after the patient’s last meal (7). Naugler et al created a multivariate model to evaluate selected variables that could partially explain the relationship between SMBG testing and serum glucose measurement. It’s interesting that the authors included demographic variables to assess potential confounding but did not mention how the final model was determined. The authors commented that female sex was the strongest predictor; however, this variable had the lowest probability of statistical significance based on the t value in the authors’ Table 2 and, with both an average age of 50 years of age and an estimated time of 15 minutes between the blood collection and the testing of the serum sample, had larger effect sizes in the multivariate model (assuming the units of the model were age in years, time in minutes and that female as a categoric variable with value 1). In our opinion, it is possible to interpret that the female gender was the weakest predictor. The relative contributions of additional factors potentially available in a laboratory information system dataset (e.g. prandial status, patient hematocrit, pO2, triglycerides) to explain the differences in SMBG glucose and serum glucose levels were not discussed. The statistically significant associations described in Table 2 remain for other studies to resolve. The data-mining study by Naugler et al concluded that the cause of the discrepancies in glucose results between SMBG and laboratory analyses was SMBG analytic performance. Naugler has captured insightful data, but the utility of this study could be extended if additional data concerning other factors known to impact laboratory meter comparisons were also extracted and considered. Data mining from LIS databases holds much promise for ensuring the quality of clinical laboratory processes. The information obtained from data-mining studies can be of great interest to patients and healthcare providers who depend upon SMBG devices for accurate representations of blood sugar status and therapeutic decisions. George Cembrowski, MD, PhD Alberta Health Services Walter C. MacKenzie Health Sciences Centre Edmonton, Alberta, Canada Martha E. Lyon, PhD, DABCC, FACB Royal University Hospital Saskatoon, Saskatchewan, Canada David C. Klonoff, MD, FACP, FRCP(Edin), Fellow AIMBE Diabetes Research Institute Mills-Peninsula Health Services San Mateo, California, USA
2
Letter to the Editor / Can J Diabetes xxx (2015) 1–2
References 1. Naugler C, Zhang Z, Redman L. Performance of community blood glucose meters in Calgary, Alberta: An analysis of quality assurance data. Can J Diabetes 2014;38:326–8. 2. Swaminathan A, Lunt H, Chang WSJ, et al. Impact of prandial status on the comparison of capillary glucose meter and venous plasma glucose measurements in healthy volunteers. Ann Clin Biochem 2013;50:6–12. 3. Larsson-Cohn U. Differences between capillary and venous blood glucose during oral glucose tolerance tests. Scand J Clin Lab Invest 1976;36:805–8. 4. Kuwa K, Nakayama T, Hoshino T, Tominaga M. Relationships of glucose concentrations in capillary whole blood, venous whole blood and venous plasma. Clin Chim Acta 2001;307:187–92.
5. Kempe K, Price D, Ellison J, et al. Capillary and venous blood glucose concentrations measured during intravenous insulin and glucose infusion: A comparison of steady and dynamic states. Diabetes Technol Ther 2009;11: 669–74. 6. Priya M, Mohan Anjana R, Pradeepa R, et al. Comparison of capillary whole blood versus venous plasma glucose estimations in screening for diabetes mellitus in epidemiological studies in developing countries. Diabetes Technol Ther 2011; 13:586–91. 7. Voss EM, McNeill L, Cembrowski GS. Extent of errors arising from comparisons of blood glucose monitor results to central laboratory glucoses. Presented at the 52nd Annual Meeting of the American Diabetes Association, June 20-23, 1992, San Antonio, Texas, USA.
Contents lists available at ScienceDirect
Canadian Journal of Diabetes journal homepage: www.canadianjournalofdiabetes.com
Letter to the Editor
Glucose Data-Mining Study Inconclusive
To the Editor: Data derived from laboratory information systems (LIS) can provide evidence either to support or refute the adequacy of current laboratory practices such as laboratory glucose meter comparison investigations. Conclusions drawn from such studies depend upon the breadth of the data extracted and the study design. The report by Naugler et al assessed the accuracy of self-monitoring blood glucose (SMBG) devices by mining LIS data for paired capillary blood-glucose results measured by glucose meters and venous serum-glucose results analyzed in a central laboratory (1). This approach enabled access to a wide range of data, but the design did not consider factors known to affect glucose testing, such as prandial status, hematocrit, partial pressure of oxygen (pO2), triglycerides and uric acid. Naugler et al reported that 96.3% of meter-glucose values were within 20% of the serum-glucose values (the analytic standard at the time of data collection) which, given the observational nature of the data, shows remarkable compliance with the standard. They concluded “that 1 in 40 meters is so inaccurate (>2 mmol/L) that the resulting errors would potentially result in an inappropriate change in insulin dosage.” This conclusion is based on an assumption that capillary and venous blood would have identical glucose levels, which may often be a false assumption. Equivalence in glucose concentration between venous and capillary blood can be assumed only when testing fasting specimens (2). Numerous studies have shown that after a meal, glucose load or, in glucose clamping experiments, capillary glucose values can be 20% to 25% higher than venous plasma values. The capillary-venous glucose difference is dependent on insulin’s activity and its action on glucose. In 1976, in advance of patient SMBG, Larsson-Cohn described these large differences in people without diabetes following oral glucose tolerance testing (3). In 2001, Kuwa et al applied the results of oral glucose tolerance testing in 75 healthy volunteers to demonstrate the impact of the capillary-venous glucose bias on the comparability of various SMBG devices (4). In 2009, Kempeet al described the use of glucose clamp experiments to assess the value of capillary and venous testing in intensive care units (5). They concluded that although capillary and venous blood glucose levels are similar in the steady state, they may differ under dynamic conditions, with the differences affecting insulin dosing decisions. This lesson of the importance of capillary venous differences is now being confirmed in diabetes screening studies using the modalities of capillary and venous blood testing (6). For patients who can assess the accuracy of their SMBG devices only by a capillary measurement compared to the central laboratory venous measurement, we recommend
1499-2671/$ – see front matter Ó 2015 Canadian Diabetes Association http://dx.doi.org/10.1016/j.jcjd.2015.03.002
performing these measurements at least 4.5 hours after the patient’s last meal (7). Naugler et al created a multivariate model to evaluate selected variables that could partially explain the relationship between SMBG testing and serum glucose measurement. It’s interesting that the authors included demographic variables to assess potential confounding but did not mention how the final model was determined. The authors commented that female sex was the strongest predictor; however, this variable had the lowest probability of statistical significance based on the t value in the authors’ Table 2 and, with both an average age of 50 years of age and an estimated time of 15 minutes between the blood collection and the testing of the serum sample, had larger effect sizes in the multivariate model (assuming the units of the model were age in years, time in minutes and that female as a categoric variable with value 1). In our opinion, it is possible to interpret that the female gender was the weakest predictor. The relative contributions of additional factors potentially available in a laboratory information system dataset (e.g. prandial status, patient hematocrit, pO2, triglycerides) to explain the differences in SMBG glucose and serum glucose levels were not discussed. The statistically significant associations described in Table 2 remain for other studies to resolve. The data-mining study by Naugler et al concluded that the cause of the discrepancies in glucose results between SMBG and laboratory analyses was SMBG analytic performance. Naugler has captured insightful data, but the utility of this study could be extended if additional data concerning other factors known to impact laboratory meter comparisons were also extracted and considered. Data mining from LIS databases holds much promise for ensuring the quality of clinical laboratory processes. The information obtained from data-mining studies can be of great interest to patients and healthcare providers who depend upon SMBG devices for accurate representations of blood sugar status and therapeutic decisions. George Cembrowski, MD, PhD Alberta Health Services Walter C. MacKenzie Health Sciences Centre Edmonton, Alberta, Canada Martha E. Lyon, PhD, DABCC, FACB Royal University Hospital Saskatoon, Saskatchewan, Canada David C. Klonoff, MD, FACP, FRCP(Edin), Fellow AIMBE Diabetes Research Institute Mills-Peninsula Health Services San Mateo, California, USA
2
Letter to the Editor / Can J Diabetes xxx (2015) 1–2
References 1. Naugler C, Zhang Z, Redman L. Performance of community blood glucose meters in Calgary, Alberta: An analysis of quality assurance data. Can J Diabetes 2014;38:326–8. 2. Swaminathan A, Lunt H, Chang WSJ, et al. Impact of prandial status on the comparison of capillary glucose meter and venous plasma glucose measurements in healthy volunteers. Ann Clin Biochem 2013;50:6–12. 3. Larsson-Cohn U. Differences between capillary and venous blood glucose during oral glucose tolerance tests. Scand J Clin Lab Invest 1976;36:805–8. 4. Kuwa K, Nakayama T, Hoshino T, Tominaga M. Relationships of glucose concentrations in capillary whole blood, venous whole blood and venous plasma. Clin Chim Acta 2001;307:187–92.
5. Kempe K, Price D, Ellison J, et al. Capillary and venous blood glucose concentrations measured during intravenous insulin and glucose infusion: A comparison of steady and dynamic states. Diabetes Technol Ther 2009;11: 669–74. 6. Priya M, Mohan Anjana R, Pradeepa R, et al. Comparison of capillary whole blood versus venous plasma glucose estimations in screening for diabetes mellitus in epidemiological studies in developing countries. Diabetes Technol Ther 2011; 13:586–91. 7. Voss EM, McNeill L, Cembrowski GS. Extent of errors arising from comparisons of blood glucose monitor results to central laboratory glucoses. Presented at the 52nd Annual Meeting of the American Diabetes Association, June 20-23, 1992, San Antonio, Texas, USA.
