August 13, 2019
Having an occasion to read the ISO 15197 standard (for glucose meters) I notice the statements:
One of the reasons allowed to discard data is: “the blood-glucose monitoring system user recognizes that an error was made and documents the details”
This makes ISO a biased standard because in the real world there will be user error which generates outlier data.
And compounding things is this statement:
“Outlier data may not be eliminated from the data used in determining acceptable system accuracy, but may be excluded from the calculation of parametric statistics to avoid distorting estimates of central tendency and dispersion.”
The problem is outliers that are representative of what happens in the real world should not be thrown out to help statistics such as regression and precision from being distorted. Rather these statistics should not be used. An error grid is a perfectly adequate statistic to handle 100% of the data.
May 11, 2019
Performance standards are used in several ways: to gain FDA approval, to make marketing claims, and to test assays after release for sale that are in routine use.
Using glucose meters as an example…
Endocrinologists, who care for people with diabetes, would be highly suited to writing standards. They are in a position to know the magnitude of error that will cause an incorrect treatment decision.
FDA would also be suited with statisticians, biochemists, and physicians.
Companies through their regulatory affairs people know their systems better than anyone, although one can argue that their main goal is to create a standard that is as least burdensome as possible.
So in the case of glucose meters, at least for the 2003 ISO 15197 standard, regulatory affairs people ran the show.
May 8, 2019
The article, “Getting More Information From Glucose Meter Evaluations” has just been published in the Journal of Diabetes Science and Technology.
Our article makes several points. In the ISO 15197 glucose meter standard (2013 edition), one is supposed to prepare a table showing the percentage of results in system accuracy within 5, 10, and 15 mg/dL. Our recommendation is to graph these results in a mountain plot – it is a perfect example of when a mountain plot should be used.
Now I must confess that until we prepared this paper, I had not read ISO 15197 (2013). But based on some reviewer comments, it was clear that I had to bite the bullet, send money to ISO and get the standard. Reading it was an eye opener. The accuracy requirement is:
95% within ± 15 mg/dL (< 100 mg/dL) and within ± 15% (> 100 mg/dL) and
99% within the A and B zones of an error grid
I knew this. But what I didn’t know until I read the standard is user error from the intended population is excluded from this accuracy protocol. Moreover, even the healthcare professionals performing this study could exclude any result if they thought they made an error. I can imagine how this might work: That result can’t be right…
In any case, as previously mentioned in this blog, in the section when users are tested, the requirement for 99% of the results to be within the A and B zones of an error grid was dropped.
In the section where results may be excluded, failure to obtain a result is listed since if there’s no result, you can’t get a difference from reference. But there’s no requirement for the percentage of times a result can be obtained. This is ironic since section 5 is devoted to reliability. How can you have a section on reliability without a failure rate metric?
March 23, 2019
A recent editorial disagrees with the proposed CLIA limits for HbA1c provided by CMS and CDC (The Need for Accuracy in Hemoglobin A1c Proficiency Testing: Why the Proposed CLIA Rule of 2019 Is a Step Backward) online in J Diabetes Science and Technology. The proposed CLIA limits are ± 10% – the NGSP limits are 5%, and the CAP limits 6%. Reading the Federal Register, I don’t understand the basis of the 10%.
This reminds me of another CMS decree in the early 2000s – Equivalent Quality Control. Under this program, a lab director could run quality control for 10 days as well as the automated internal quality checks and decide whether the two were equivalent. If the answer was yes, the frequency of quality control could be reduced to once a month. This made no sense!
February 13, 2019
I had occasion to read the ISO 15197:2013 standard about blood glucose meters Section 6.3.3 “minimum system accuracy performance criteria.”
Note that this accuracy requirement is what is typically cited as the accuracy requirement for glucose meters.
But the two Notes in this section say that testing meters with actual users is tested elsewhere in the document (section 8). Thus, because of the protocol used, the system accuracy estimate does not account for all errors since user errors are excluded. Hence, the system accuracy requirement is not the total error of the meter but rather a subset of total error.
Moreover, in the user test section, the acceptance goals are different from the system accuracy section!
Ok, I get it. The authors of the standard want to separate two major error sources: error from the instrument and reagents (the system error) and errors caused by users.
But there is no attempt to reconcile the two estimates. And if one considers the user test as a total error test, which is reasonable (e.g., it includes system accuracy and user error), then the percentage of results that must meet goals is 95%. The 99% requirement went poof.
February 13, 2019
I had occasion to read the ISO 15197:2013 standard about blood glucose meters and was struck by the words “minimum system accuracy performance criteria” (6.3.3).
This reminds me of the movie “Office Space”, where Jennifer Anniston, who plays a waitress, is being chastised for wearing just the minimum number of pieces of flair (buttons on her uniform). Sorry if you haven’t seen the movie.
Or when I participated in an earlier version of the CLSI method comparison standard EP9. The discussion at the time was to arrive at a minimum sample size. The A3 version says at least 40 samples should be run. I pointed out that 40 would become the default sample size.
Back to glucose meters. No one will report that they have met the minimum accuracy requirements. They will always report they have exceeded the accuracy requirements.