Copyright: , Croatian Society of Medical Biochemistry. Publication date print and electronic : 15 February Gunn Berit Berge Kristensen [ 1 ]. Piet Meijer [ 2 ]. Correspondence to: Corresponding author: gunn. The aim of this paper is to describe knowledge required to interpret EQA results and present a structured approach on how to handle deviating EQA results.
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Copyright: , Croatian Society of Medical Biochemistry. Publication date print and electronic : 15 February Gunn Berit Berge Kristensen [ 1 ]. Piet Meijer [ 2 ]. Correspondence to: Corresponding author: gunn. The aim of this paper is to describe knowledge required to interpret EQA results and present a structured approach on how to handle deviating EQA results. The value of EQA and how the EQA result should be interpreted depends on five key points: the control material, the target value, the number of replicates, the acceptance limits and between lot variations in reagents used in measurement procedures.
This will also affect the process of finding the sources of errors when they appear. The ideal EQA sample has two important properties: it behaves as a native patient sample in all methods is commutable and has a target value established with a reference method. If either of these two criteria is not entirely fulfilled, results not related to the performance of the laboratory may arise.
This flowchart will become available in a public domain, i. EQA should verify on a recurring basis that laboratory results conform to expectations for the quality required for patient care. A typical EQA scheme EQAS consists of the following events: A set of samples is received by the laboratory from an external EQA organization for measurements of one or more components present in the samples.
The laboratories do not know the concentration of the components in the samples and perform measurements in the same manner as for patient samples. Important objectives of EQA are, beside monitoring and documenting the analytical quality, to identify poor performance, detect analytical errors, and make corrective actions.
Participation in EQA gives an evaluation of the performance of the individual laboratory and of the different methods and instruments 3 , 4. Therefore, proper and timely evaluation of EQA survey reports are essential and even a must for accreditation see ISO , paragraph 5. In this opinion paper, we focus on the knowledge required to interpret an EQA result and present a structured approach on how to handle an EQA error. The paper is limited to EQA for evaluation of quantitative measurement procedures.
Key factors for interpreting EQA results are knowledge of the EQA material used, the process used for target value assignment , the number of replicate measurement of the EQA sample, the range chosen for acceptable values around the target acceptance limits , and the impact of between lot variations in reagents used in measurement procedures 4 - 6.
The most important property of the EQA sample is commutability 7 - 9. The significance of this is something that one has become more and more aware of in recent years. A commutable EQA sample behaves as a native patient sample and has the same numeric relationship between measurements procedures as is observed for a panel of patient samples. A non-commutable EQA sample includes matrix related bias that occurs only in the EQA sample but not in authentic clinical patient samples and therefore, does not give meaningful information about method differences.
Matrix related bias is due to an unwanted distortion of the test result attributed to physical and chemical differences in the samples, compared to the patient material the measurement procedures are directed towards. The bias demonstrated by the EQA samples was five times found to be in an opposite direction compared with the native serum samples. Therefore, EQA materials should be tested for commutability and if evaluation of method differences is intended, it is mandatory. Additionally, the sample should be stable during the survey period, homogeneous, available in sufficient volume and have clinical relevant concentrations 10 , Higher concentrations of components can be achieved by adding components spiking to pooled unaltered samples but this may induce non-commutability 12 , In practice, the EQA sample very often is a compromise between ideal behaviour in accordance with native samples and stability of the material and therefore, may not be commutable, which limited the opportunities in EQA result evaluation 4.
If the EQA sample is commutable, target value assignment could be made by using a reference measurement procedure or a high-specificity comparative method that is traceable to a reference measurement procedure 14 , In this case, all participants are compared to the same assigned value and trueness can be assessed.
Target assignment by value transfer based on results from certified reference materials is possible if the commutability of the reference materials has been verified 16 - For many components, a reference method or certified reference material is not available.
In that case, an overall mean or median can be used as the assigned value, after removal of outliers or by the use of robust statistical methods All measurement procedures are expected to give the same results for a commutable sample.
That gives the possibility to compare the result with other methods. However, the measurement procedure with most participants will have greatest influence on the overall mean or median, and you do not know what the true value is. An alternative is to use the mean or median of the peer-group see below means or medians in order to give the same weight to each peer-group The most common procedure used to assign a target value if the commutability of the EQA sample is unknown is to categorize participant methods into peer-groups that represent similar technology and calculate mean or median of the peer-group, after removal of outlier values, and use this as the assigned value.
A peer-group consist of methods expected to have the same matrix-related bias for the EQA sample and it is possible to assess quality, i. A limitation is the number of participants in each group. The uncertainty of the calculated assigned value would be larger in a peer group with few participants compared to a group with many participants. The variability of results in the group will also influence the uncertainty of the assigned value.
A high variability combined with few participants will give the greatest uncertainty of the assigned value. To assess if the EQA result is acceptable, acceptance limits i.
The acceptance limits can be considered regulatory , statistical or clinical. Regulatory limits have the intention to identify laboratories with sufficiently poor performance that they should not be able to continue to practice. The assessment of the individual laboratory is given as z-scores, which is the number of standard deviations SD from the assigned value the EQA result.
Assessment of z-scores is based on the following criteria: - 2. The performance of the individual laboratory is compared against the dispersion of results obtained by the participants in the peer-group in each survey. A disadvantage is that these limits are variable and may change with time as methods and instruments evolve. Another disadvantage with statistical based criteria is that the limits may vary between peer-groups measuring the same component.
Imprecise-method peer groups will have a large acceptance interval whereas precise-method peer groups will have a small interval for acceptable results, independent of what is required for clinical needs. Several EQA organizations use z-scores in the feedback reports to the participants. Clinical limits can be based on a difference that might affect clinical decisions in a specific clinical situation These limits are desirable but may be difficult to implement because very few clinical decisions are based solemnly on one particular test.
More common are clinically established limits derived from biological variation in general 29 , A challenge is the fact that the existing database on biological variation is based on few studies or studies with rather poor quality. However, in the strategic conference to arrive at a consensus on how to define analytical performance goals that took place in Milan , a working group for revising the current biological variation database was established 31 - Both regulatory and clinical limits are fixed limits and the uncertainty of the assigned value will be a fraction of the acceptance interval.
To account for the uncertainty of the definitive value, Norwegian Quality Improvement of Laboratory Examinations Noklus have added a fixed interval around the target value in their acceptance limits When the acceptance interval is expressed as a percent, it might also be necessary to include a fixed unit interval below a concentration at which a percent is not reasonably achievable because the concentration-independent variability of a measurement procedure becomes a larger fraction of the acceptance interval.
EQA results are meant to reflect results of patient samples and in most of the schemes, the participant is asked to perform a single measurement of the EQA sample.
Total error is assessed because bias, imprecision, and analytical non-specificity can contribute to variation in a single result. If replicate measurements of the samples are included, it may be appropriate to have different limits to separately assess bias and imprecision.
Between lot variation in the reagents used in measurement procedures may influence participant assessment in EQA 5 , Between lot variation has been described in several publications for glucose strips 38 - EQA organizers should, however, register lot numbers when relevant and in some instances comment on lot variation in feedback reports Additionally, between lot variation found when using control materials may not mirror results when using native blood 5 , To evaluate the clinical importance of between lot variation discovered in routine EQAS, the actual lot should therefore be examined using native blood.
An unacceptable EQA result should be investigated by the participant the person in charge of EQA in the laboratory to find the cause of the deviation and make corrective actions. According to ISO , an accredited laboratory shall participate in EQAS, monitor and document EQA results, and implement corrective actions when predetermined performance criteria are not fulfilled 3. In spite of the extensive use of EQAS in evaluating the quality of the analytical work done in medical laboratories, it is remarkable that there is little aid in the process of finding the sources of errors when they appear.
All the mentioned key factors that must be taken into consideration when interpreting an EQA result also apply for handling an EQA error. The ideal EQA sample has two important properties; it behaves as a native patient sample toward all methods is commutable and has an assigned value established with a reference method with small uncertainty.
If either of these two criteria are not entirely fulfilled, results with errors NOT related to the quality of the laboratory may arise. Therefore, the EQA provider should take steps in the scheme design to avoid or ameliorate adverse consequences. This could be done for example, by using peer-group assigned values for a non-commutable material. It is important to distinguish between different types of error external, generating cost without benefit and those important ones that are caused by the laboratory itself internal.
For the laboratory, errors caused by themselves are most important and of their primary interest. Of the three variables in the above equation, only one, R, is the immediate responsibility of the laboratory.
Errors in AV has an external source while an error in L is fundamentally internal as commented above even if most laboratories tend to adopt the limits proposed by the EQA organizer. To understand the complexity of finding the cause of an EQA error all sources of deviation in an EQA result are included in a flowchart and have to be considered. This means the laboratory should investigate whether there is a reason why the results tend to become an outlier.
The result of this work was further processed by the NKK expert group and resulted in a flow chart with additional comments that could be used by the laboratories, e. In NKK carried out a follow-up and an evaluation of the flowchart. They were also asked if they use the flowchart regularly, and if not, why they do not use it.
Finally, they were asked if they have any suggestions for improvement of the flowchart. Fifty-eight percent of the laboratories that responded used the flowchart regularly. They suggest changing the order of the items in the flowchart and start with transcription errors, the most common cause to a deviating EQA result unpublished data. The recommendations from the evaluation has been taken into account and a new version of the flowchart has been developed in cooperation with the External quality Control for Assays and Tests ECAT Foundation in the Netherlands Figure 1.
The content of the original flowchart is kept and where necessary expanded and re-structured. The flowchart starts with the most frequently errors followed by the logical steps in the flow of an EQA survey from pre-survey issues to report and interpretation — see Figure 1.
Four different aspects elucidate each item in the flowchart: Observation — what is the potential error, Responsibility — who is responsible for the error, Comment — a short comment on action to undertake, Note — a more detailed description of actions see Figure 2. If no explanation is given, the flowchart should be used Figure 1 and Figure 2 to reveal the potential cause s.
The EQA provider may wrongly enter the data or the laboratory may record or report a wrong result. In the evaluation of the first version of the flowchart, transcriptional errors were the most common cause for a deviating result. Obviously, a lot may go wrong before the sample reaches the laboratory like sample selection, inappropriate stability or homogeneity, a mistake in labelling or an error in packaging.
Journals alphabetically. Visit statistics. Biochemia Medica , Vol. Visits: 8. A review of urinary angiotensin converting enzyme 2 in diabetes and diabetic nephropathy str. Prognostic value of procalcitonin and lipopolysaccharide binding protein in cancer patients with chemotherapy-associated febrile neutropenia presenting to an emergency department str.
Interpretation of EQA results and EQA-based trouble shooting