Background: Many labs have not yet selected the most appropriate Westgard Quality Control (QC) rule for each test. This is mainly due to the apparent complexity of the matter.
Methods: From the Westgard OPSpecs Charts QC planning tool and the Sigma Metrics formula's it was deduced that every Westgard rule has its own Sigma value. This was converted to an easy three-step road map to optimal Westgard QC rules.
Results: The road map provided is based on Sigma Metrics that hold a definition of “world class quality”, at which no further effort to increase quality needs to be taken. Furthermore, it is shown that clinical chemical tests can be classified as “good”: quality at or above world class, “bad”: quality below world class but controllable with Westgard QC rules and “ugly”: quality not controllable with Westgard QC rules alone. Finally, practical tips of how to deal with this and related aspects are given.
Conclusions: The use of the road map based on Sigma Metrics leads to fast and easy implementation of optimal Westgard QC rules.
The influence of interference by hemolysis, icterus and lipemia on the results of routine chemistries may lead to wrong interpretations. The H-, I- and L-indices that can be measured by the Beckman LX-20 instrument (Beckman Coulter) in serum or plasma samples are a reliable semi-quantitative measure of the size of these interferences. A survey carried out in 16 Dutch clinical laboratories on the use of these indices demonstrated that in several of these laboratories, the influence of interferences is largely underestimated. Therefore, a multicenter study was carried out in which we examined the interference of hemolysis, icterus and lipemia on 32 analytes. On the basis of biological variation, we decided on cutoff indices above which analytically significant interference exists. We found analytically significant interference by hemolysis, icterus or lipemia, in 12, 7 and 15 of the 32 analytes studied, respectively. Flagging of results on the basis of analytically significant interference, however, results in too many clinically insignificant comments. On the basis of clinical significance, we conclude that significant interference by hemolysis, icterus or lipemia is present in only 5, 6 and 12 of the analytes studied, respectively. Use of the cutoff indices presented here facilitates optimal use of the LX-20 indices to prevent reporting of wrong results due to interference.
The influence of interference by hemolysis, icterus and lipemia on the results of routine chemistries may lead to wrong interpretations. On Synchron LX-20 instruments (Beckman Coulter) serum or plasma indices can be used as reliable semi-quantitative measures of the magnitude of such interference. In an article recently published in this journal, we presented the results of a multicenter study carried out in Dutch hospitals in which we determined cutoff indices for analytes above which analytically significant interference exists. Clinically significant interference cutoff indices were also derived for these analytes. In this article, we describe the handling of patient samples with clinically significant interference by hemolysis, icterus or lipemia. We investigated several possible approaches for correction of the result: dilution of the interference; mathematical correction in the case of hemolysis; treatment with ferrocyanide to destroy bilirubin; and removal of lipids in lipemic patient samples. We concluded, that mathematical correction of potassium or lactate dehydrogenase results in hemolytic samples can only be carried out if intravascular hemolysis is ruled out. Hemoglobin quantification in serial patient samples, combined with measurement of haptoglobin, represents a useful tool to rule out in vivo hemolysis. We derived an algorithm for this situation. We do not simply recommend mathematical correction, unless it is clinically acceptable. We present formulas for potassium and lactate dehydrogenase: corrected potassium=measured potassium–(hemolytic index increment×0.14); corrected lactate dehydrogenase=measured lactate dehydrogenase–(hemolytic index increment×75). The dilution studies indicated that dilution is only applicable for bilirubin, C-reactive protein and iron. The results of treatment with ferrocyanide were poor, and we do not recommend this method. Removal of lipids using high-speed centrifugation or LipoClear (StatSpin Inc.), a non-toxic and non-ionic polymer, is a very effective approach, although C-reactive protein, creatine kinase-MB (CK-MB) and cholesterol cannot be removed using LipoClear. For all interferants (hemoglobin, bilirubin, lipids), relatively simple algorithms are derived that can easily be implemented in the clinical laboratory.
Background: In the region Limburg (The Netherlands) almost all of the five participating laboratories use a different immunoassay platform to determine thyroid stimulating hormone (TSH) and free thryoxine (FT4). With the frequent transfer of patients within the region, harmonization of test result interpretation is necessary. In this study, we investigated dysthyroxinemia classification between participating laboratories and developed procedures for improvement.
Methods: Two ring surveys with an interval of 2 years were performed. Four patient groups (n=100) with different dysthyroxinemia classification were based on biochemical results of the Autodelphia analyzer. Samples were tested in five participating laboratories. In each group the percentage of patients classified with dysthyroxinemia was calculated and differences were analyzed by the Fisher’s exact test.
Results: After the first survey, the percentage of patients with hyperthyroxinemia was more than 20% lower in three laboratories compared to the other two. Bhattacharya analysis revealed that the upper reference limit of FT4 was 20%–30% too high in two laboratories. Adjustments of reference ranges appeared to be effective in the second survey. The third laboratory reported significantly lower percentages of patients with hyperthyroxinemia in the second survey. New FT4 reference ranges were determined for this laboratory, resulting in adequate classification of hyperthyroxinemia.
Conclusions: This study illustrates the potential of a multicenter evaluation of dysthyroxinemia in a biochemical-defined patient cohort. In particular, classification of hyperthyroxinemia differed between laboratories. Adjustments of reference ranges resulted in better agreement of dysthyroxinemia classification. Even using internal and external quality assurance programs, application of multicenter ring surveys is advised to prevent inadequate reference ranges.