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.
January 31, 2019
The FDA continues to dis the ISO 15197 standard in both their POC and lay user (over the counter) proposed guidelines:
POC – “Although many manufacturers design their BGMS validation studies based on the International Standards Organizations document 15197: In vitro diagnostic test systems—Requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus, FDA believes that the criteria set forth in the ISO 15197 standard do not adequately protect patients using BGMSs in professional settings, and does not recommend using the criteria in ISO 15197 for BGMSs.”
The POC accuracy criteria are:
95% within +/- 12 <75 mg/dL and within +/- 12% >75 mg/dL
98% within +/- 15 <75 mg/dL and within +/- 15% >75 mg/dL
Over the counter – “FDA believes that the criteria set forth in the ISO 15197 standard are not sufficient to adequately protect lay-users using SMBGs; therefore, FDA recommends performing studies to support 510(k) clearance of a SMBG according to the recommendations below.”
The over the counter accuracy criteria are:
95% within +/- 15% over the entire claimed range
99% within +/- 20% over the entire claimed range
To recall, ISO 15197 2013 accuracy criteria are:
95% within ± 15 mg/dl <100 mg/dL
95% within ± 15% >100 mg/dL
99% within A and B zones of a glucose meter error grid
October 16, 2017
A recent article (subscription required) suggests how to estimate measurement uncertainty for an assay to satisfy the requirements of ISO 15189.
As readers may know, I am neither a fan of ISO nor measurement uncertainty. The formal document, GUM – The Guide to the Expression of Uncertainty in Measurement will make most clinical chemists heads spin. Let’s review how to estimate uncertainty according to GUM.
- Identify each item in an assay that can cause uncertainty and estimate its imprecision. For example a probe picks up some patient sample. The amount of sample taken varies due to imprecision of the sampling mechanism.
- Any bias found must be eliminated. There is imprecision in the elimination of the bias. Hence bias has been transformed into imprecision.
- Combine all sources of imprecision into a BHE (big hairy equation – my term, not GUMs).
- The final estimate of uncertainty is governed by a coverage factor. Thus, an uncertainty interval for 99% is wider than one for 95%. Remember that an uncertainty interval for 100% is minus infinity to plus infinity.
The above Clin Chem Lab Med article calculates uncertainty by mathematically summing imprecision of controls and bias from external surveys. This is of course light years away from GUM. The fact that the authors call this measurement uncertainty could confuse some to think that this is the same as GUM.
Remember that in the authors’ approach, there are no patient samples. Thus, the opportunity for errors due to interferences has been eliminated. Moreover, patient samples can have errors that controls do not. Measurement uncertainty must include errors from the entire measurement process, not just the analytical error.
Perhaps the biggest problem is that a clinician may look at such an uncertainty interval as truth, when the likely true interval will be wider and sometimes much wider.
September 29, 2017
I had occasion to read an open access paper “full method validation in clinical chemistry.” So with that title, one expects the big picture and this is what this paper has. But when it discusses analytical method validation, the concept of testing for interfering substances is missing. Precision, bias, and commutability are the topics covered. Now one can say that an interference will cause a bias and this is true but nowhere do these authors mention testing for interfering substances.
The problem is that eventually these papers are turned into guidelines, such as ISO 15197, which is the guideline for glucose meters. And this guideline allows 1% of the results to be unspecified (it used to be 5%). This means that an interfering substance could cause a large error resulting in serious harm in 1% of the results. Given the frequency of glucose meter testing, this translates to one potentially dangerous result per month for an acceptable (according to ISO 15197) glucose meter. If one paid more attention to interfering substances and the fact that they can be large and cause severe patient harm, the guideline may have not have allowed 1% of the results to remain unspecified.
I attended a local AACC talk given by Dr. Inker about GFR. The talk, which was very good had a slide about a paper about creatinine interferences. After the talk, I asked Dr. Inker how she dealt with creatinine interferences on a practical level. She said there was no way to deal with this issue, which was echoed by the lab people there.
Finally, there is a paper by Dr. Plebani, who cites the paper: Vogeser M, Seger C. Irregular analytical errors in diagnostic testing – a novel concept. (Clin Chem Lab Med 2017, ahead of print). Ok, since this is not an open access paper, I didn’t read it but what I can tell from Dr. Plebani comments, the cited authors have discovered the concept of interfering substances and think that people should devote attention to it. Duh! And particularly irksome is the suggestion by Vogeser and Seger of “we suggest the introduction of a new term called the irregular (individual) analytical error.” What’s wrong with interference?
March 27, 2017
I previously blogged about flaws in the Diabetes Technology Society surveillance protocol. I turned this entry into a commentary which has been accepted and should appear shortly in the J Diabetes Sci Technol.
January 27, 2017
I’ve been interested in glucose meter specifications and evaluations. There are three glucose meter specifications sources:
FDA glucose meter guidance
glucose meter error grids
There are various ways to evaluate glucose meter performance. What I wished to look at was the combination of sigma metric analysis and the error grid. I found this article about the sigma metric analysis and glucose meters.
After looking at this, I understand how to construct these so-called method decision charts (MEDX). But here’s my problem. In these charts, the total allowable error TEa is a constant – this is not the case for TEa for error grids. The TEa changes with the glucose concentration. Moreover, it is not even the same at a specific glucose concentration because the “A” zone limits of an error grid (I’m using the Parkes error grid) are not symmetrical.
I have simulated data with a fixed bias and constant CV throughout the glucose meter range. But with a changing TEa, the estimated sigma also changes with glucose concentration.
So I’m not sure how to proceed.