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Objective The aim of this study was to validate a gas chromatography-mass spectrometry (GC-MS) method for the measurement of 5α-cholestanol in the clinical laboratory in agreement with ISO 15189:2012. Materials and Methods The GC-MS performance was evaluated and proficiency testing data were used to estimate the measurement uncertainty of the method considering the recommendations of international guidelines. Results The calibration curves were linear from 6 to 50 μmol/L, with r 2 >.99. The limit of detection and limit of quantitation were determined to be 0.36 and 2.58 μmol/L, respectively. The bias ranged from –18.9% to 15.2% for 6.5, 18.3, and 66 μmol/L. The intra- and interassay reproducibility was <20% at the various concentrations studied. The expanded uncertainty was determined to be 50.9%. Conclusion The GC-MS method for the measurement of 5α-cholestanol has proved to have acceptable analytical performance for use in the clinical laboratory. [ABSTRACT FROM AUTHOR]

Background: Sigma metric calculations provide laboratories an objective means to assess analytical method performance. Methods with higher sigma values are desirable because they are more reliable and may use less frequent quality control in order to maintain optimal performance. Sigma metrics can also serve as a tool when comparing method performance across assay and manufacturer platforms. Methods: Sigma values were calculated for 28 common chemistry and 24 immunoassay assays across 3 academic medical centers. Method imprecision and percent bias relative to peer group means was tabulated from Bio-Rad quality control (QC) data. Sigma values were calculated for each method using allowable total error (TEa) from either the CLIA evaluation limits or desirable biological variation. Average sigma values were generated for each site and graded as optimal: >6 sigma; good: 5–6 sigma; marginal: 3–5 sigma; or poor: <3 sigma. Analysis of NIST SRM1950 standards for a subset of analytes allowed an estimation of absolute bias. Results: Clinical chemistry assays displayed similar method performance across all 3 study sites. Immunoassays showed significant differences between manufacturers, and a majority of assays failed to meet an optimal level of performance. Different TEa values produced different sigma metrics with more stringent TEa limits based on biological variation, resulting in poorer performance estimates than the wider CLIA limits. Analysis of NIST standards revealed similar performance. Conclusions: Sigma metrics are comparable for chemistry but not immunoassay platforms. The selection of total allowable error goals led to differences in sigma metrics. [ABSTRACT FROM AUTHOR]

Evaluation of method performance involves the consideration of numerous factors that can contribute to error. A variety of measures of performance can be borrowed from the signal detection literature and others are drawn from statistical science. This article demonstrates the principles of performance evaluation by applying multiple measures to osteometric sorting models for paired elements run against data from known individuals. Results indicate that false positive rates are close, on average, to expected values. As assemblage size grows, the false positive rate becomes unimportant and the false negative rate becomes significant. Size disparity among the commingled individuals plays a significant role in method performance, showing that case-specific circumstances (e.g. assemblage size and size disparity) will determine method power. [ABSTRACT FROM AUTHOR]