Quality assurance programs for vitamin A and E in serum: are we doing enough to assess laboratory performance?

OBJECTIVES: Monitoring serum vitamin A (retinol) and vitamin E (α-tocopherol) concentrations is common practice for assessing nutritional status. Measurement of these vitamins can be challenging due to several factors. Whilst the RCPAQAP Vitamins: Serum Program assists participating laboratories in harmonisation, the materials provided do not contain the analogues of retinol and α-tocopherol that may be present in real patient samples. We aimed to assess participants' capacity to accurately report retinol and α-tocopherol in the presence of the vitamin E analogues tocopherol acetate and γ-tocopherol. METHODS: A supplementary series of a control sample and three matched spiked samples were distributed to each laboratory participating in the Program. Retinol and α-tocopherol results for each spiked sample were compared to the results of the control sample submitted by each participant. Acceptability of retinol and α-tocopherol results was determined based on the RCPAQAP allowable performance specifications (APS). RESULTS: Thirteen participants returned results for the supplementary sample series. Interference from α-tocopherol acetate was observed with results below the APS in 30 % (n=4) of laboratories for retinol quantification and in 23 % (n=3) for α-tocopherol quantification. One laboratory returned results above the APS for α-tocopherol when γ-tocopherol was present. CONCLUSIONS: This supplementary sample series has shown that the presence of vitamin E analogues can lead to the over or under estimation of nutritional status by some participants. Affected laboratories are encouraged to review their analytical procedures. To further assess laboratory competence, EQA providers should consider using patient samples or spiked challenge samples.

Detecting reagent lot shifts using proficiency testing data.

Clinically significant systematic analytical shifts can evade detection despite between-lot reagent verification, quality control and proficiency testing systems practiced by most laboratories. Through numerical simulations, we present two methods to determine whether there has been a shift in the proficiency testing peer group of interest, peer group i, using the measurements from peer group i and J other peer groups. In method 1 ('group mean'), the distance of peer group i from the mean of the other J peer groups is used to determine whether a shift occurs. In method 2 ('inter-peer group' method), the distances of peer group i from each of the means of the other J peer groups are used to determine whether a shift has occurred. The power of detection for both methods increases with the magnitude of systematic shift, the number of peer groups, the number of laboratories within the peer groups and the proportion of laboratories within the affected peer group, and a smaller analytical imprecision. When the number of peer groups is low, the power of detection for the group mean method is comparable to the inter-peer group method, using the m = 1 criterion (a single inter-peer group comparison that exceeds the control limit is considered a flag). At larger peer groups, the inter-peer group method using the same (m = 1) criterion outperforms the group mean method. The proposed methods can elevate the professional role of the proficiency testing program to that of monitoring the peer group method on top of the performance of individual laboratories.

Commutability and traceability in EQA programs.

OBJECTIVES: The concept of commutability of samples has focused laboratories on the importance of traceability. However, the critical role of External Quality Assurance (EQA) in achieving the primary role of traceability (i.e. facilitating comparable patient results in different laboratories) has largely been lost. The aim of this paper is to review the role of EQA in achieving traceable/commutable results. DESIGN AND METHODS: The role of commutability and traceability in EQA and Internal Quality Control (IQC) are discussed. Examples of commutable EQA samples are given to highlight the problem of assuming EQA material does not behave like patient samples. RESULTS: We provide the conventional traceability chain (top down) and the role of EQA in a "bottom up" model using conventional EQA samples. CONCLUSIONS: The quest for commutable samples has compromised the value of EQA without an understanding that some EQA materials are commutable for some measurands. EQA plays a key role in performance improvement, but laboratories need to understand the importance of using a range of values appropriate to the assay to identify areas of quality need. Traceability and EQA using conventional samples are not mutually exclusive concepts.