Although point-of-care (POC) glucose monitoring has transformed hospital-based glycemic control programs (1), the use of POC glucose monitoring in critically ill populations has garnered much attention because research shows that some POC glucose meters are unsuitable for the hospital population.
Several investigations have highlighted the role of interfering substances causing inaccurate POC glucose meter measurements (2,3,8−11). Many of these interferences, such as anemia, presence of oxidizing/reducing substances, and high pO2 levels, are commonly observed in hospitalized populations, especially among critically ill patients. In a study by Tang et al., low hematocrit was attributed to falsely high POC glucose measurements when compared to a plasma-based comparative method (8). The research also showed ascorbic acid leading to falsely high glucose measurements on certain devices (9). Later studies highlighted the impact of inaccurate glucose measurements causing increased risk for hypoglycemic events and glycemic variability (2,3).
POC glucose meter manufacturers evaluate their devices against known interfering substances as required by the Food and Drug Administration (FDA) (7,12−14). Unfortunately, it is difficult to evaluate all known substances. Likewise, in vitro replication of complex in vivo physiologic variables contributing to inaccurate meter performance can be difficult. The emergence of autocorrecting POC glucose biosensors provides means to overcome these interferences (2−4). These novel sensors correct for interferences by automatically measuring hematocrit and correcting for reducing/oxidizing substances to produce more accurate glucose measurements. The clinical impact of using autocorrecting glucose meters versus noncorrecting devices has been shown to reduce hypoglycemic events and glycemic variability—representing the future direction of POC glucose monitoring technology
Validation/Verification of POC Glucose Meters in Hospital Settings
Adoption of any new hospital POC glucose meter requires institutions to verify or validate device performance prior to implementation (15). If the POC glucose meter is used “on label” (i.e., in accordance with the FDA approved intended use of the device), a formal validation is not required and the instrument only needs to be verified to confirm accuracy, precision, and analytical measurement range, among other core elements defined under CLIA (7,14). For “off label” use, the CLIA waived status of many POC glucose meters would be negated and the device would default to high complexity classification. As a high complexity test, CLIA requires the device to undergo more rigorous validation studies, including establishing performance data for the intended use population and in the presence of expected interfering substances. Given the significance of interfering substances and high-risk nature of critically ill populations, institutions should consider the following steps as part of their implementation process:
Comparison Method Selection: Institutions must first define a suitable comparative method. For glucose testing, the “definitive” method relies on isotope dilution mass spectrometry (IDMS); however, few healthcare facilities have access to these higher order analytical methods (16). Alternatives to IDMS include “true reference methods” or “comparative methods” which are more widely accessible. Comparative methods are perhaps more convenient and include clinical laboratory analyzers using plasma/serum, or whole blood with blood gas analyzers. Facilities must balance feasibility, accessibility, and performance when deciding on a comparison method.
Patient Population Selection: After identifying a suitable comparison method, facilities should next define appropriate patient populations to verify glucose meter performance. The Clinical Laboratory Standards Institute suggests a minimum sample size of 40 for method comparisons—with samples spanning the device’s analytical measurement range (17). If testing is to be performed in critically ill populations, laboratories also should include samples from intensive care units and emergency departments, spanning the range of expected specimen types (e,g., arterial, venous, capillary, fingerstick). Samples ideally should include metadata to establish disease severity to classify patients as critically ill. Determining disease severity can be as simple as confirming patient diagnosis (e,g., sepsis, cancer, etc.), however, comprehensive approaches could include the use of scoring systems such as the multiple organ dysfunction, sequential organ failure assessment, and the acute physiology and chronic health evaluation scores (18−20). With the addition of autocorrecting glucose meters, it is also recommended to include populations that exhibit a range of confounding factors (e,g., hematocrit, oxidizing/reducing substances, pO2, etc.) to challenge these devices during the verification or validation process.
Evaluation of Interfering Substances: Healthcare facilities should identify interfering substances that are likely to be encountered by glucose meters. For example, in recent years, there has been an increased interest in high-dose ascorbic acid for a variety of diseases, including sepsis (3).
Facilities that expect patients to receive high-dose ascorbic acid therapy should conduct studies to determine if glucose meter performance is compromised and develop alternative workflows for these scenarios. Non-glucose sugars should also be evaluated if testing is to be performed in the neonatal (e,g., galactose) and peritoneal dialysis populations (e,g., icodextrin/maltose) (21). When necessary and feasible, laboratories may consider contrived samples, especially if devices are used “off label” and require method validation.
Accuracy Is Key
The implementation of POC glucose monitoring is not as simple as one would hope. Glucose monitoring is necessary to properly administer one of the most dangerous drugs in clinical use—insulin. This requires greater scrutiny and diligence when implementing new devices. Accuracy is critical to the safe administration of insulin, especially in hospitalized patients. The role of the clinical laboratory is to not only ensure regulatory compliance, but also establish the safe application of in vitro diagnostic testing. Laboratories should rigorously evaluate POC glucose monitoring systems and be aware of limitations that could impact patient care.
Nam K. Tran, HCLD (ABB), FAACC, is professor and senior director of clinical pathology and is director of the Pathology Biorepository and professor in the division of clinical pathology at University of California, Davis. +Email: [email protected]
- Rajendran R, Rayman G, et al. Point-of-care blood glucose testing for diabetes care in hospitalized patients. J Diabetes Sci Technol 2014;8:1081-1090.
- Tran NK, Godwin ZR, Steele AN, et al. Clinical Impact of Accurate Point-of-Care Glucose Monitoring for Tight Glycemic Control in Severely Burned Children. Pediatr Crit Care Med 2016;17:e406-e412.
- Tran NK, Godwin ZR, Bochold JC, et al. Clinical impact of sample interference on intensive insulin therapy in severely burned patients: A pilot study. J Burn Care Res 2014;35:10.1097.
- Dubois JA, Singerland RJ, Fokkert M, et al. Bedside glucose monitoring—is it safe? A new regulatory-compliant risk assessment evaluation protocol in critically ill patient care settings. Crit Care Med 2017;45:567-574.
- Nichols JH, Brandler ES, Fantz CR, et al. A multicenter evaluation of a point-of-care blood glucose meter system in critically ill patients. J App Lab Med 2021;6:820-833.
- American Association for Clinical Chemistry website: https://www.aacc.org/cln/articles/2014/may/blood-glucose-meters, Accessed on November 14, 2021.
- Food and Drug Administration website: https://www.fda.gov/media/119829/download, Accessed on November 14, 2021.
- Tang Z, Du Ziaogu D, Louie RF, et al. Effects of drugs on glucose measurements with handheld glucose meters and a portable glucose analyzer. Am J Clin Pathol 2000;113:75-86.
- Tang Z, Lee JH, Louie RF, et al. Effects of different hematocrit elvels on glucose measurements with handheld meters for point-of-care testing. Arch Pathol Lab Med 2000;124:1135-1140.
- Dungan K, Chapman J, Braithwaite SS, et al. Glucose measurement: Confounding issues in setting targets for inpatient management. Diabetes Care 2007;30:403-409.
- Hellman R. Glucose meter inaccuracy and the impact on the care of patients. Diabetes Metab Res Rev 2012;28:207-209.
- Clinical Laboratory Standards Institute Guideline – EP07-A2, Interference Testing in Clinical Chemistry (2005).
- Clinical Laboratory Standards Institute Guideline – POCT12A3E, Point-of-care testing in acute and chronic care facilities, 3rd edition (2013).
- Clinical Laboratory Standards Institute Guideline – POCT17, Use of glucose meters in critically ill patients, 1st edition (2016).
- Centers for Medicare and Medicaid Services website: https://www.cms.gov/regulations-and-guidance/legislation/clia/downloads/6064bk.pdf, Accessed on November 14, 2021.
- Medical Laboratory Observations website: https://www.mlo-online.com/home/article/13004447/glucose-meters-where-are-we-now-where-are-we-heading Accessed on November 14, 2021.
- Clinical Laboratory Standards Institute Guideline – EP09-A3, Measurement procedure comparison and bias estimation using patient samples, 3rd edition (2013).
- Marshall JC, Cook DJ, Christou NV, et al. Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome. Crit Care Med 1995;23:1638-1652.
- Jones AE, Trzeciak S, Kline JA. The sequential organ failure assessment for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med 2009;37:1649-1654.
- Breslow MJ, Badawi O. Severity scoring in the critically ill: Part 1—interpretation and accuracy of outcome prediction scoring systems. Chest 2012;141:245-252.
- Schleis TG. Interference of maltose, icodextrin, galactose, or xylose with some blood glucose monitoring systems. Pharmacotherapy. 2007 Sep;27(9):1313-21.