Wrong thinking about glucose standards


Glucose has been in the news lately both in the New York Times and the medical literature.

A standard favoring tight glycemic control was dropped, possibly because the glucose meters used were inaccurate (1-3.)

Glucose meters that use glucose dehydrogenase can give very wrong answers in dialysis patients (4).

And finally, the FDA is considering revising glucose standards (5). This blog entry is about glucose standards revision. The article mentions that FDA is considering revising the performance standards in ISO 15197 which are: 95% of values must be with 20% of reference at 75 mg/dL or above and within 15 mg/dL below 75 mg/dL. The Boyd and Bruns modeling paper is referenced and Bruns is quoted in the article. I have previously critiqued the Boyd and Bruns paper (6).

Here is the main point which is not covered in the article. The main problem with the ISO standard is that it specifies performance for only 95% of the data. This of course leaves up to 5% of the data as unspecified and means that if up to 5% of glucose results had large enough error so that hyperglycemic patients were classified as hypoglycemic and vice versa, that assay would be acceptable according to ISO. This is equivalent to saying that up to a 5% wrong site surgery rate is acceptable! 100% of the data must be specified as is the case with glucose error grids, which predated the ISO guideline.

A second problem with the ISO guideline is that the performance limits ignore user error. But user error contributes to the final result and must be part of the performance specification.

The protocol must also be part of the guideline. In a short method comparison, it is possible to observe no large errors. To supplement this, specific analytical properties of the assay must be specified as well as risk management criteria. There are recent glucose recalls where software was faulty and allowed units to be changed from mg/dL to mmol/L or vice versa without customer knowledge.

I mention in passing that the Boyd and Bruns article referred to the article underestimate total user due to an inadequate model which fails to account for interferences. Reference 4 is an example of interferences and responsible for at least 13 deaths.


  1. See: http://www.nytimes.com/2009/08/18/health/policy/18diabetes.html?_r=1&scp=1&sq=glucose&st=cse
  2. Intensive versus Conventional Glucose Control in Critically Ill Patients. NEJM 2009;360:1283-1297
  3. Scott MG, Bruns DE, Boyd JC, and Sacks DB. Tight glucose control in the intensive care unit: Are glucose meters up to the task? Clin Chem 2009;55:18-20.
  4. See: http://www.fda.gov/MedicalDevices/Safety/AlertsandNotices/PatientAlerts/ucm177189.htm
  5. See: http://www.aacc.org/publications/cln/2009/september/Pages/inside0909.aspx
  6. Krouwer, JS. How to Improve Total Error Modeling by Accounting for Error Sources Beyond Imprecision and Bias, Clin Chem 2001;47:1329-30.

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