External quality assessment schemes for inorganic elements in the clinical laboratory: Lessons from the OELM scheme.

Measurements of inorganic elements in clinical laboratories produce results used for the diagnosis, the treatment and the monitoring of deficiencies or overloads. The main objective of External Quality Assessment Schemes is to verify, on a regular frequency, that clinical laboratory results correspond to the quality requirement for patient care. Therefore, External Quality Assessment Schemes represent an essential component of a laboratory's quality management system. However, External Quality Assessment Schemes within the same analytical field remain heterogeneous for different reasons such as samples, determination of assigned value, acceptable limits, content of the reports. The aim of this review was to describe and illustrate some major critical aspects of External Quality Assessment Schemes based on Occupational and Environmental Laboratory Medicine external quality assessment scheme experience.

Quality specifications for the determination of copper, zinc, and selenium in human serum or plasma: evaluation of an approach based on biological and analytical variation.

BACKGROUND: Trace element external quality assessment schemes monitor laboratory performance and provide a stimulus for improvement in accuracy. However, monitoring of participant performance varies according to the scheme and can lead to conflicting conclusions. METHODS: Quality specifications based on biological intra- and interindividual variability were calculated and compared to those currently used by various trace element external quality assessment schemes for plasma or serum copper, zinc, and selenium concentrations. For this purpose, we evaluated results reported by participating laboratories in different schemes, at key concentrations, using z scores. RESULTS: Minimal quality specifications developed from the biological intra- and interindividual variability were, for Cu, +/-0.84 micromol/L or 12% of the assigned target concentration, whichever is greater; for Zn, +/-1.20 micromol/L or 15% of the assigned target concentration, whichever is greater; and for Se, +/-0.072 micromol/L or 12% of the assigned target concentration, whichever is greater. Reported performance of the participating laboratories depended on analyte, concentration, and the selected quality specification. In addition, the most commonly used methods for the determination of Cu, Zn, and Se may give different results. CONCLUSIONS: The proposed minimal quality specifications based on biological variation are generally slightly less stringent than those currently in use, although they do not drastically change the performance evaluation in the different schemes. These specifications are a first step in the harmonization of practices among the schemes and remain to be evaluated.

Statistical methods for monitoring the relationship between the IFCC reference measurement procedure for hemoglobin A1c and the designated comparison methods in the United States, Japan, and Sweden.

BACKGROUND: The American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD)/International Diabetes Federation (IDF)/IFCC Consensus Statement on the worldwide standardization of HbA(1c) states that "... [HbA(1c)] results are to be reported world-wide in IFCC units ... and derived NGSP units ... , using the IFCC-NGSP master equation." METHODS: We describe statistical methods to evaluate and monitor the relationships as expressed in master equations (MEs) between the IFCC Reference Measurement procedure (IFCC-RM) and designated comparison methods (DCMs) [US National Glycohemoglobin Standardization Program (NGSP), Japanese Diabetes Society/Japanese Society for Clinical Chemistry (JDS/JSCC), and Mono-S in Sweden]. We applied these statistics, including uncertainty calculations, to 12 studies in which networks of reference laboratories participated, operating the IFCC-RM and DCMs. RESULTS: For NGSP and Mono-S, slope, intercept, and derived percentage HbA(1c) at the therapeutic target show compliance with the respective MEs in all 12 studies. For JDS/JSCC, a slight deviation is seen in slope and derived percentage HbA(1c) in 2 of the 12 studies. Using the MEs, the uncertainty in an assigned value increases from 0.42 mmol/mol HbA(1c) (IFCC-RM) to 0.47 (NGSP), 0.49 (JDS/JSCC), and 0.51 (Mono-S). CONCLUSIONS: We describe sound statistical methods for the investigation of relations between networks of reference laboratories. Application of these statistical methods to the relationship between the IFCC-RM and DCMs in the US, Japan, and Sweden shows that they are suitable for the purpose, and the results support the applicability of the ADA/EASD/IDF/IFCC Consensus Statement on HbA1c measurement.

IFCC reference system for measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study.

BACKGROUND: The national programs for the harmonization of hemoglobin (Hb)A(1c) measurements in the US [National Glycohemoglobin Standardization Program (NGSP)], Japan [Japanese Diabetes Society (JDS)/Japanese Society of Clinical Chemistry (JSCC)], and Sweden are based on different designated comparison methods (DCMs). The future basis for international standardization will be the reference system developed by the IFCC Working Group on HbA(1c) Standardization. The aim of the present study was to determine the relationships between the IFCC Reference Method (RM) and the DCMs. METHODS: Four method-comparison studies were performed in 2001-2003. In each study five to eight pooled blood samples were measured by 11 reference laboratories of the IFCC Network of Reference Laboratories, 9 Secondary Reference Laboratories of the NGSP, 3 reference laboratories of the JDS/JSCC program, and a Swedish reference laboratory. Regression equations were determined for the relationship between the IFCC RM and each of the DCMs. RESULTS: Significant differences were observed between the HbA(1c) results of the IFCC RM and those of the DCMs. Significant differences were also demonstrated between the three DCMs. However, in all cases the relationship of the DCMs with the RM were linear. There were no statistically significant differences between the regression equations calculated for each of the four studies; therefore, the results could be combined. The relationship is described by the following regression equations: NGSP-HbA(1c) = 0.915(IFCC-HbA(1c)) + 2.15% (r(2) = 0.998); JDS/JSCC-HbA(1c) = 0.927(IFCC-HbA(1c)) + 1.73% (r(2) = 0.997); Swedish-HbA(1c) = 0.989(IFCC-HbA(1c)) + 0.88% (r(2) = 0.996). CONCLUSION: There is a firm and reproducible link between the IFCC RM and DCM HbA(1c) values.