Patient-Based Real Time QC

Bob Barrett (0:00)

This is a podcast from Clinical Chemistry, sponsored by the Department of Laboratory Medicine at Boston Children's Hospital. I'm Bob Barrett. Analyzing quality control specimens is a tool routinely used by medical laboratories to help assure that correct results are reported for patient samples.

That involves periodic analysis of materials with a known analyte concentration and estimating if measurement error is within acceptable limits. However, the adequacy of those samples to represent authentic patient samples is not often addressed. But what if we were to use the patient samples themselves as a source of quality control data?

A review paper appearing in the August 2019 issue of Clinical Chemistry reexamined this concept and was the topic of an earlier podcast on patient-based real-time quality control.

Now, in the September 2020 issue of Clinical Chemistry, a Q&A feature follows up with differing perspectives on this topic. We asked five experts from the International Federation of Clinical Chemistry and Laboratory Medicine Committee on Analytical Quality to discuss the advantages of patient-based real-time quality control. The moderator of that Q&A session was Dr. Tony Badrick, who is CEO of the Royal College of Pathologists of Australasia Quality Assurance Programs based in Sydney, Australia. So let's start off, Dr. Badrick, by reminding us what exactly patient-based real-time quality control is and how does this relate to or integrate with traditional internal quality control?

Tony Badrick (1:29)

So the concept between patient-based real-time quality control is that you use some characteristic of the patient population to detect error in your assay. So for example, let's assume that you're measuring sodium on some instrument. What patient-based real-time QC would do is take the sodium values for a group of patients and then add a block of those sodium values together, you know, the sodium value from this patient, together with the sodium value from the patient that was analyzed before, together with the sodium value from the patient that was analyzed before that, and form a mean. You might take 20 or 30 of those patient values and use those 20 or 30 values, consecutive values, to calculate a mean. So you use that mean, that mean of those 20 patients, as your quality control parameter. So when you analyze the next patient sample, you add that new sample into the mean, but you drop the 20th sample off. So the mean keeps moving along. As you get another patient in, you drop the 21st patient off, but you use the new patient value to detect a new mean, to calculate a new mean. So the mean keeps moving along. So that's the basis of it. You're using some characteristic of the patient values, in this case, the mean, to generate something which tells you something about the assay.

So for example, let's assume that something goes wrong with the assay, and let's look at sodium again. So we were calculating our mean for our sodiums, and in a normal population, you might expect that the mean of those consecutive sodium values might be 140 So if you had a shift in bias in your assay, rather than the mean being 140, it might move to 142 So that's how you can detect changes in the assay, things like bias changes, by monitoring something like the average of all your normal sodiums.

You don't need to use mean, you can use other things. You might use a median, particularly when the population might not be normally distributed, or you might transform some characteristic of the population, take the square root of the values or something like that. But you use something that's related to the assay values for the population of your samples.

You can use other things as well. So you might use the, might calculate the standard deviation of the population, or you might look at the percentage of the patient values that are outside the reference interval. And all those things will be affected if there's a problem with the assay. But what you're using is the actual values of your patient's samples to detect these changes.

And you compare perhaps the mean of the sodiums, you compare that with normal control limits, plus or minus 2 SD of your population. So you can detect errors the same way that you detect errors using conventional internal quality control. But in fact, what you're using, I say, is these patient values. This certainly isn't a new concept. It's been around for a long time. The hematologists use it routinely. It's called Boole's algorithm. So they use it to detect errors in hemoglobin and some of their parameters. So it's certainly not a new concept. Why I think it's become sort of topical right now is that laboratories are now analyzing a lot of samples. Most laboratories, you know, are analyzing lots and lots of samples. So there's a lot of values. We've got very sophisticated IT systems now that can calculate these moving means or medians, you know, and this software can be connected to your analyzer. So we've now got the means, no pun intended, to be able to use these systems more consistently. The advantages of using this sort of patient-based real-time QC over conventional internal QC, that it's continuous, it's in real time. So every time you analyze a new sample, you use that sample value to put into your means. So every sample almost will contribute to this QC process.