QC testing by time or number of patient specimens and the implications for patient risk are explored.
By Curtis A. Parvin, PhD, John Yundt-Pacheco and Max Williams
Quality Assurance Series
Editor's note: This is the fourth in a multi-part quality assurance series.
Current U.S. regulations mandate the minimum frequency of quality control (QC) evaluations in a clinical diagnostic laboratory testing human specimens in terms of time. CLIA regulation 493.126(d)(3) states, "At least once each day patient specimens are assayed or examined perform the following for:
(i) Each quantitative procedure, include two control materials of different concentrations;
(ii) Each qualitative procedure, include a negative and positive control material."
While there are some exceptions to the above regulation, in general CLIA standards for acceptable QC practice are based on the maximum allowable time interval between QC evaluations. Requiring laboratories that test human specimens to examine at least two QC specimens per day ensures a baseline for diagnostic quality and establishes an upper limit to patient risk...or does it?
Frequency of QC Testing
There are two different ways that the frequency of QC testing can be considered-in terms of the time between QC evaluations or the number of patient specimens examined between QC evaluations. Two laboratories that perform QC testing at the same frequency in terms of time could be testing at very different frequencies in terms of number of patient examinations between QC evaluations. Likewise, two laboratories that perform QC testing at the same frequency in terms of number of patient examinations between QC evaluations could be testing at very different frequencies in terms of the time interval between QC evaluations.
In an earlier article in this series we argued that the focus of laboratory QC should be managing patient risk.1 From a patient risk perspective, is there any difference if QC testing requirements are formulated in terms of time between QC evaluations versus the number of patient specimens tested between QC evaluations? Here we examine that question using the patient risk paradigm we developed in our previous article.2
Let's consider the patient risk implications for different laboratory scenarios that vary in the number of patient specimens examined between QC evaluations and/or the number of QC evaluations performed per day to determine which approach to QC frequency is most critical. For simplicity we will assume that patient testing is spread out evenly across the day and that each lab is applying the same QC rules on the same diagnostic instrument.
Laboratory "A" (Fig. 1A) tests two levels of QC every day and examines an average of 50 patient specimens per day. In the cartoon depictions the horizontal axis is time, the vertical lines represent patient specimens, and the diamonds represent QC evaluations. Laboratory "B" (Fig. 1B) evaluates QC at the same frequency as Lab "A" in terms of time, but Lab "B" examines an average of 300 patient specimens per day. Laboratory "C" (Fig. 1C) tests two levels of QC every eight hours and examines an average of 150 patient specimens daily. Lab "C" evaluates QC at the same frequency as Lab "A" in terms of number of patient specimens between QC evaluations, but at a different frequency in terms of time.
From CLIA's perspective, both labs "A" and "B" are meeting the regulation's minimum requirement by examining two QC samples every 24 hours, while Lab "C" is exceeding the minimum requirements by doing three times as much QC-six QC examinations every day, split into three QC evaluations.
To evaluate the implications of these QC strategy design decisions, let's look at what happens when the test systems malfunction in a non-obvious manner in the middle of the day. There is little risk in an obvious malfunction producing unreliable results; it is the unrecognized malfunction that increases patient risk. A shift in results can be difficult to detect prior to a QC event because the true patient concentrations are unknown. Fig. 2 depicts a malfunction that shifts results higher to the extent that there is a 40% chance that the examination error in an individual patient result is large enough to make the result unreliable (the error exceeds an allowable total error).
In Fig. 2, each red asterisk represents an unreliable patient result that is produced due to the unrecognized malfunction. After the malfunction is detected by a failing QC event, it is corrected and subsequent results are reliable. This example is somewhat optimistic in assuming that the first QC event will detect the malfunction. Depending on the magnitude of the error and the QC rules used, it may take several QC evaluations to detect the malfunction.
When an undetected, persistent malfunction occurs, the number of unreliable patient results produced will be proportional to the number of specimens examined while the test system is malfunctioning. The number of unreliable results produced by Lab "A" is approximately the same as the number of unreliable results produced by Lab "C" (~10 unreliable results), but Lab "B" produced six times as many unreliable results as labs "A" or "C" (~60 unreliable results).
The number of patient specimens examined between QC evaluations is 50 for labs "A" and "C", but 300 for Lab "B." If Lab "B" examined the same number of patient specimens between QC evaluations as Lab "A" (every 50 patient specimens, or every four hours instead of every 24 hours) there would be a similar outcome in the event of a malfunction (Fig. 3). The number of patient specimens examined between QC evaluations is the critical design factor for managing the risk of producing unreliable patient results in the presence of an undetected malfunction.
Dr. Parvin is manager of Advanced Statistical Research; John Yundt-Pacheco is Scientific Fellow; and Max Williams is Global Scientific and Professional Affairs Manager, Bio-Rad.
1. Parvin CA, Yundt-Pacheco J, Williams M. The focus of laboratory quality control: Why QC strategies should be designed around the patient, not the instrument. ADVANCE for Administrators of the Laboratory 2011;20(3):48-9.
2. Parvin CA, Yundt-Pacheco J, Williams M. Designing a quality control strategy: In the modern laboratory three questions must be answered.ADVANCE for Administrators of the Laboratory2011;20(5):53-4.
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