Posted by Sten Westgard, MS
An important new study was published in the International Journal of Laboratory Hematology, on the QC practices of coagulation laboratories.
But here's a question for you: of all these activities, how many of them occur greater than 90% of the time - and which one doesn't?
- Repeat the QC, and if it passes, report results
- Open [and run] new QC
- Look for trending
- Discontinue testing until controls are within limits
- Repeat all patient samples from the last acceptable QC
To find out which one of the activities occurs at a frequency not like the others, follow the jump...
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Posted by Sten Westgard, MS
An interesting question came from one of our readers, about how to interpret certain outliers.
So let's take one example, just two control values, expressed in z-scores. If the low control is -3.1 and the high control is + 2.3, which rule is violated? the 1:3s rule? The R:4s rule? Both?
An answer, after the jump...
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Posted by Sten Westgard, MS
Visit our booth or better yet Thermo Scientific's booth for a nice take-home from Chicago.
Posted by Sten Westgard, MS
Posted by Sten Westgard, MS
Posted by Sten Westgard, MLO
In the November 2012 MLO magazine there is an intriguing article by Roy Midyett, a hematology supervisor, titled "Empty QC"
Here's how Mr. Midyett defines "Empty QC":
"Empty QC is any nominal QC that does not give techs performing the test any more confidence than they would have without the QC, and has by logic or experience, no influence on the reporting of the test."
Is Mr. Midyett correct? Have our QC procedures become meaningless gestures? More after the jump.
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Posted by Sten Westgard, MS
So here's a Normalized Method Decision chart for a cholesterol method.
Which method would you choose?
Posted by Sten Westgard, MS
Posted by Sten Westgard, MS
Recently, a user submitted a question about the mandatory use of the 10x rule on chemistry analytes as mandated by ISO 15189 through NABL:
"Specific criteria of accreditation of NABL India (for ISO 15189) say that 10x should be considered as a violation for clinical chemistry and Immunoassay parameters. However we understand that Westgard rules consider it as a warning only. Please advise."
So, the central question is, does ISO 15189 mandate the use of the 10x control rule?
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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. .
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Posted by Sten Westgard, MS
An interesting question came in through email (
"1. Can we apply Westgard multirules to hematology control?
2. I read in one paper that the hematology control range is calculated as mean +/- 2.5 SD, is this correct and if not how can I calculate own laboratory hematology control range?"
The answer, after the jump...
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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.
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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...
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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.
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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...
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