Flash glucose monitoring

February 16, 2018

Here’s an article about flash glucose monitoring, a way for diabetic patients to avoid finger sticks and glucose monitors. Now I can understand why other glucose meter companies are trying to get out of the business. This product sounds like a game changer.

 

 

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An observation from the ATTD glucose Conference

February 14, 2018

The 11th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD) is underway in Vienna, Austria. The abstracts from the conference are available here. Here’s an interesting observation: I searched for the term MARD and it was found 48 times whereas the term error grid was found only 10 times. I published a paper describing problems with the MARD statistic and offered alternatives.


Comments about clinical chemistry goals based on biological variation – Revised Feb. 7, 2018

February 5, 2018

There is a recent article which says that measurement uncertainty should contain a term for biological variation. The rationale is that diagnostic uncertainty is caused in part by biological variation. My concerns are with how biological variation is turned into goals.

On the Westgard web site, there are some formulas on how to convert biological variation into goals and on another page, there is a list of analytes with biological variation entries and total error goals.

Here are my concerns:

  1. There are three basic uses of diagnostic tests: screening, diagnosis, and monitoring. It is not clear to me what the goals refer to.
  2. Monitoring is an important use of diagnostic tests. It makes no sense to construct a total error goal for monitoring that takes between patient biological variation into account. The PSA total error goal is listed at 33.7%. Example: For a patient tested every 3 months after undergoing radiation therapy, a total error goal of 33.7% is too big. Thus, for values of 1.03, 0.94, 1.02, and 1.33, the last value is within goals but in reality would be cause for alarm.
  3. The web site listing goals has only one goal per assay. Yet, goals often depend on the analyte value, especially for monitoring. For example the glucose goal is listed at 6.96%. But if one examples a Parkes glucose meter error grid, at 200 mg/dL, the error goal to separate harm from no harm is 25%. Hence, the biological goal is too small.
  4. The formulas on the web site are hard to believe. For example, I < 0.5 * within person biological variation. Why 0.5, and why is it the same for all analytes?
  5. Biological variation can be thought to have two sources of variation – explained and unexplained – much like in a previous entry where the measured imprecision could be not just random error, but inflated with biases. Thus, PSA could rise due to asymptomatic prostatitis (a condition that by definition that has no symptoms and could be part of a “healthy” cohort). Have explained sources of variation been excluded from the databases? And there can be causes of explained variation other than diseases. For example, exercise can cause PSA to rise in an otherwise healthy person.
  6. Biological variation makes no sense for a bunch of analytes. For example, blood lead measures exposure to lead. Without lead in the environment, the blood lead would be zero. Similar arguments apply to drugs of abuse and infectious diseases.
  7. The goals are based on 95% limits from a normal distribution. This leaves up to 5% of results as unspecified. Putting things another way, up to 5% of results could cause serious problems for an assay that meets goals.

Two examples of why interferences are important and a comment about a “novel approach” to interferences

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?


HbA1c – use the right model, please

August 31, 2017

I had occasion to read a paper (CCLM paper) about HbA1c goals and evaluation results. This paper refers to an earlier paper (CC paper) which says that Sigma Metrics should be used for HbA1c.

So here are some problems with all of this.

The CC paper says that TAE (which they use) is derived from bias and imprecision. Now I have many blog entries as well as peer reviewed publications going back to 1991 saying that this approach is flawed. That the authors chose to ignore this prior work doesn’t mean the prior work doesn’t exist – it does – or that it is somehow not relevant – it is.

In the CC paper, controls were used to arrive at conclusions. But real data involves patient samples so the conclusions are not necessarily transferable. And in the CCLM paper, patient samples are used without any mention as to whether the CC paper conclusions still apply.

In the CCLM paper, precision studies, a method comparison, linearity, and interferences were carried out. This is hard to understand since the TAE model of (absolute) average bias + 2x imprecision does not account for either linearity or interference studies.

The linearity study says it followed CLSI EP6 but there are no results to show this (e.g., no reported higher order polynomial regressions). The graphs shown, do look linear.

But the interference studies are more troubling. From what I can make of it, the target values are given ± 10% bands and any candidate interfering substance whose data does not fall outside of these bands is said to not clinically interfere (e.g., the bias is less than absolute 10%). But that does not mean there is no bias! To see how silly this is, one could say if the average bias from regression was less than absolute 10%, it should be set to zero since there was no clinical interference.

The real problem is that the authors’ chosen TAE model cannot account for interferences – such biases are not in their model. But interference biases still contribute to TAE! And what do the reported values of six sigma mean? They are valid only for samples containing no interfering substances. That’s neither practical nor meaningful.

Now one could better model things by adding an interference term to TAE and simulating various patient populations as a function of interfering substances (including the occurrence of multiple interfering substances). But Sigma Metrics, to my knowledge cannot do this.

Another comment is that whereas HbA1c is not glucose, the subject matter is diabetes and in the glucose meter world, error grids are well known as a way to evaluate required clinical performance. But the term “error grid” does not appear in either paper.

Error grids account for the entire range of the assay. It seems that Sigma Metrics are chosen to apply at only one point in the assay.


Proposed improvements to the Diabetes Technology Society surveillance protocol

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.


Antwerp talk about total error

March 12, 2017

Looking at my blog stats, I see that a lot of people are reading the total analytical error vs. total error post. So, below are the slides from a talk that I gave at a conference in Antwerp in 2016 called The “total” in total error. The slides have been updated. Because it is a talk, the slides are not as effective as the talk.

 

 

TotalError