A guest post by Nils Person, PhD, FACB

I recently read the essay "The QC We Really Do" with great interest and found the information very useful.  However, there was one topic that I was concerned about. It had to do with re-calibration in response to QC rule failure. The essay indicated that re-calibration is a common response to QC rule failure.... and I am not sure that is a good strategy. .

Posted by Sten Westgard, MS

"We have a department supervisor that instructs the techs to delete (not omit) qc values outside of three s.d..Is there a specific CLIA rule against this? Where I come from, deleting qc values is wrong."

What do you think? The answer, after the jump...

Posted by Sten Westgard

This month, Dr. Westgard was teaching his spring semester class for the University of Wisconsin Medical Technology school. He likes to use recently published papers in the scientific literature as a way to relate his lessons to things happening in the "real world" of the laboratory.

This semester, he has written up a number of lessons covering HbA1c methods, performance, and quality requirements based on the article in Clinical Chemistry, Few Point-of-Care Hemoglobin A1c Assay Methods Meet Clinical Needs, by David E. Bruns1 and James C. Boyd and a study by Lenters-Westra and Slingerland (Six out of eight hemoglobin A1c point-of care instruments do not meet the generally accepted analytical performance criteria. Clin Chem 2010;56:44 –52.)

For your convenience, here are the lessons in order...

Posted by Sten Westgard, MS

We want your control chart data!

We've always had an "open door" policy on the website, welcoming your questions and your data. At times, we may not be so explicit in inviting you to contact us with your observations, comments, inquiries, and frustrations, but we do want to hear from you.

Now, we're asking for a more specific set of data from you - for your problem methods, show us a few months of QC data, ideally with notations such as new control lot, new reagent, recalibration, etc.

Posted by Sten Westgard, MS

This question comes to us from a CLS student in Texas:

"I was hoping that someone might be able to answer a question that is causing me and some of my
classmates some confusion. There is some confusion when you are plotting your QC chart and all your values are a whole number. Would you keep you SD as a whole number or make it to one decimal place? And if you did make it to one decimal place, would you be making your SD more accurate that your original value?"

Answer after the jump...

Here's a question from a website visitor regarding assigning a mean value for a new QC material with the following assumptions:

"1.    The analyte reports out as a whole number.
2.    The results of calculations on 20 replicate samples are;
    A.    Mean = 10.5
    B.    SD = 0.5
    C.    2 SD Range = 9.5 - 11.5
    D.    95% Confidence Interval = 10.3 - 10.7
    E.    CV% = 4.9

The question is "what to set the mean at?" One camp contends that the mean of 10.5 should be used, even though no result will ever "hit" the mean. The other camp states that the mean should be set to 10 or 11 regardless of whether or not a LJ shows bias, or even 10x failure. "

Answer after the fold.

Posted by Sten Westgard, MS

Here's a question that came in about setting the control limits (or range) for a test:

"for some assays we're using this formula: actual SD * 3 and then divided by 2 plus or minus the mean is this acceptable or not because when we use that give us abit wider range than using the mean plus minus 2SD."

When we asked for an example, we got this data:

Manufacturer Data: SD = 22.5, Mean = 224
Actual Data: SD = 8.79, Mean = 223

"We're multiplying ourSD (8.79) by 3 and then we divide it by 2 to give us the new SD which is 13 (8.79*3/2 = 13).
Then we multiply this new SD 13 by 2 to give us the real 2 SD range which is 26.
So our range is now 197 - 249.
Are we following the right way or not?"

The answer, after the jump...

Posted by Sten Westgard, MS

We got an excellent question the other day via email:

I have heard the term "Within QC" and "Across QC"
used, but what do these refer to specifically and
where can I find more information about what is
meant by those terms? I was not able to find this
information, but laboratory leadership staff said
that "Within QC" referred to assessing multi-rules
"within QC level and across QC runs", and that
"Across Qc" referred to assessing multi-rules
"looking at both QC levels, can be within same run
or back-to-back runs".

A lab has the following multi-rules; "Within QC"
1:3s, 1 QC result outside 3sd; 2:2s, 2 consecutive
QC results outside 2sd on the same side of the mean;
4s, 2 consecutive Qc results differ by more than
4sd; and 1:2s, 1 Qc result outside 2sd and within 3.
(1:2s is used as a warning rule, the others as
rejection rules). The rules for "Across Qc" are as
follows; 2:2s, 2 consecutive Qc results (1 each
multiple levels) are outside of 2sd; and 4s, 2 Qc
results (one of each multiple levels) are >4sd
apart. These are both rejection rules. These
multi-rules are used to assess all tests in a
chemistry lab; the majority of tests are assessed
with 2 levels of Qc, a few use 3 levels of Qc.

The situation arose where QC results on one day for
a cancer antigen were the following:
Day 1A:
Level 1 -Within 2sd, acceptable
Level 2 -1:2s, run was accepted as only the warning
rule 1:2s was encountered.

The next day the results were as follows:
Day 2A:
Level 1 -1:2s
Level 2 -Within 2sd
Leadership said run should not be accepted,
violating the "across" 2:2s rule.

However, leadership said the inverse situation would
have been acceptable as *consecutive* data points
did not violate the "across" 2:2s rule, i.e.

Day 1B:
Level 1 1:2s
Level 2 within 2sd

Day 2B:
Level 1 within 2sd
Level 2 1:2s

In the A group, because Level 2 is outside of 2s,
and the very next data point (Level 1 from the next
day) is also out 2s, the run is unacceptable and
should be rejected. In Group B, since consecutive
data points are okay the run is acceptable.

Is this a correct approach? Is it correct to reject
group A (Day 1A and 2A) and not reject group B (Day
1B and 2B)? Do these multi-rules as outlined and
implemented detect some unacceptable variation in
group A that does not exist in group B? Thank you
for any clarification.

So what's the answer? Are scenarios A and B fundamentally different? More after the jump.

Posted by Sten Westgard, MS

Posted by Sten Westgard, MS