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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Clin Chim Acta. Author manuscript; available in PMC 2010 July 21.
Published in final edited form as:
Clin Chim Acta. 2008 May 1; 391(1-2): 127–128.
doi:  10.1016/j.cca.2008.02.006
PMCID: PMC2907536
NIHMSID: NIHMS75054

Global Standardization and Improved Technologies for Point-of-Care Glucose Testing

Gerald J Kost, MD, PhD, MS, FACB, Nam K Tran, BS, Victor J Abad, MA, and Richard F Louie, PhD

Mahoney and Ellison [1] point out the realities of point-of-care testing, or for that matter, the challenges facing any diagnostic assay—variations in hematocrit, oxygen tension, pH, and temperature, and myriad other factors, such as drugs, may affect measurements unpredictably. Nonetheless, all diagnostic tests, including those performed at the bedside, should be highly accurate. Point-of-care testing is no excuse for inaccuracy!

Whole-blood samples start with patients, who will present, especially if critically ill, a heterogeneous dynamic mix of variables reflecting in vivo conditions. In our experimental model, we minimized time delays, randomized measurements, and did not tamper with samples, in order to fairly preserve and evenly represent in vivo conditions in vitro, thus giving equal opportunity for each glucose meter system to display its native performance characteristics, which locally-smoothed (LS) median absolute difference (MAD) curves [2] portray so vividly.

The results speak for themselves—the performance of handheld devices for glucose measurement needs to be improved, and also considered in the context of glucose intervals used in tight glycemic control (TGC). Our patient population was representative of critical care settings where TGC is used. Thus, the goal is not to design an artificial experiment lacking clinical relevance, but instead to raise the performance bar so that critical care nurses, physicians, and surgeons, as well as emergency physicians and those responding to crises, will have high quality diagnostic evidence for rapid bedside decisions. TGC has forced the issue. What do we need to do?

First, glucose testing must be based on standardized calibration. Second, proficiency testing should be accuracy-based and traceable to the same global standard. Third, technologies need to be improved to eliminate effects of confounding factors. Fourth, scientists, clinicians, and industry leaders are encouraged to work together in the new point-of-care technology national research network [3] to advance performance collaboratively. Fifth, even with technical advances, human error is likely to persist, so care teams must enhance operator training and educate in proper interpretation and use of point-of-care results.

Without these fundamental elements requisite to progress we will continue to see inaccuracies of the magnitude clearly illustrated (without magnification) by LS MAD curves [2]. The LS MAD curve should be below the error tolerance over clinically relevant decision intervals, and this new approach is recommended not in isolation, but in conjunction with other established evaluation techniques capable of revealing erroneous results and discrepant values [2]. Discrepancies around the TGC interval introduce risk because they can lead to dangerous hypoglycemic episodes, one of the most serious drawbacks of TGC protocols.

The Food and Drug Administration and other responsible agencies should define an accuracy standard for the licensing of POC glucose devices, and as suggested last year [4], all would benefit from independent arbitrator adjudicating performance to certify or disqualify devices for use in pivotal situations, such as critical care, emergency medicine, and disaster response where the price for inaccuracy, erroneous results, or discrepant values may be too costly in terms of patient outcomes, and ultimately, survival.

These principles must extend globally [5]. Handheld meters frequently represent the only instruments available for glucose measurements in low-resources countries, especially remote and rural areas [6]. These countries now face a worldwide diabetes newdemic [7]. During devastating disasters, such as the 2004 tsunami in Southeast Asia and Hurricane Katrina in the United States [8], point-of-care testing often represented the only diagnostic and monitoring option available. Therefore, it must be accurate.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Gerald J Kost, UC Davis-LLNL Center for Point-of-Care Technologies, NIBIB, NIH, Point-of-Care Testing Center for Teaching and Research (POCT•CTR), University of California, Davis, CA.

Nam K Tran, POCT•CTR, University of California, Davis, CA.

Victor J Abad, The Epsilon Group Virginia, LLC, Charlottesville, VA.

Richard F Louie, POCT•CTR, University of California, Davis, CA.

References

1. Mahoney JJ, Ellison JM. Median differences or actual differences. Clin Chim Acta. 2008;389 [PubMed]
2. Kost GJ, Tran NK, Abad VJ, Louie RF. Evaluation of point-of-care glucose testing accuracy using locally-smoothed median absolute difference curves. Clin Chim Acta. 2008;389:31–39. [PMC free article] [PubMed]
3. Kost GJ, Korte B, Beyette FR, Jr, Gaydos C, Weigl B. The NIBIB Point-of-Care Technologies Research Network: Center Themes and Opportunities for Exploratory Projects. Point of Care: The Journal of Near-Patient Testing and Technology. 2008;7:1. [PMC free article] [PubMed]
4. Kost GJ. Glucose meter accuracy and implications for patient care. Best Practices and Practical Applications for Point-of-Care Testing Glucose Testing and Patient Safety; Symposium, American Association for Clinical Chemistry, National Meeting; San Diego. July 19, 2007.
5. Kost GJ. Pattern Recognition of Accuracy Using Locally-Smoothed (LS) Median Absolute Difference (MAD) Curves for Point-of-Care Testing: The New Global Quality Standard. Siriraj Hospital, School of Medicine, Mahidol University; Jun 25, 2007.
6. Kost GJ, Vansith K, Tuntideelert M. Critical care and point-of-care testing in Cambodia and Vietnam. Point of Care: The Journal of Near-Patient Testing and Technology. 2006;5:193–198.
7. Kost GJ. Newdemics, public health, small-world networks, and point-of-care testing. Point-of-Care: The Journal of Near-Patient Testing and Technology. 2006;5:138–144.
8. Kost GJ, Tran NK, Tuntideelert M, Kulrattanamaneeporn S, Peungposop N. Katrina, the tsunami, and point-of-care testing: Optimizing rapid response diagnosis in disasters. Amer J Clin Path. 2006;126:513–520. [PubMed]