Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches

Abstract

IntroductionTwo approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory.Materials and methodsSigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa − bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis.ResultsFor the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values.ConclusionsBoth methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.

Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing‐based approach and an internal quality control data inter‐laboratory comparison‐based approach

“…Samples derived from human serum could avoid the matrix effect, which was caused by the interaction of processed material and the measurement procedure and may suggest that erroneous results are being generated when in fact the results are acceptable. Because the target means in PT/EQA programs were derived from statistical results without measurement traceability, 15,26,27 those using this approach should be aware of the possible limitations, including statistical methods used to generate the data and the number of laboratories that participate. 25 (b) Another way to assess relative bias is by comparing laboratory results with the statistical mean of the peer group using the same instrument and method from inter-laboratory QC data or proficiency testing (PT)/external quality assessment (EQA) reports.…”

“…25 (b) Another way to assess relative bias is by comparing laboratory results with the statistical mean of the peer group using the same instrument and method from inter-laboratory QC data or proficiency testing (PT)/external quality assessment (EQA) reports. 15 We further studied the QC rule design based on the sigma metrics of nineteen analytes designed based on sigma metrics by both methods using two TEa standards (data not shown in this study). 23 Imprecision is typically expressed as an SD or CV.…”

“…By contrast, the TPT-based approach may lead to a lower CV and an overestimated σ value because of the short-term assessment. 15 We further studied the QC rule design based on the sigma metrics of nineteen analytes designed based on sigma metrics by both methods using two TEa standards (data not shown in this study). The differences in QC rules, QC numbers, and QC run sizes as calculated using both approaches and two TEa standards were analyzed.…”

“…CV (SD) and bias often vary with the concentration of analytes, so the σ computed at different QC concentration levels could be quite different from one another. Xinqi Cheng et al 15 introduced two approaches to compute bias and CV for σ evaluation, using a proficiency testing (PT) or an internal quality control (IQC)-based approach. Additionally, if the concentration of IQC materials differed significantly from that of EQA samples, the σ calculation may not represent the optimal quality of the analytes.…”

“…The internal quality control data inter-laboratory comparison-based approach has been shown to be a convenient and reliable method among clinical laboratories. Xinqi Cheng et al 15 introduced two approaches to compute bias and CV for σ evaluation, using a proficiency testing (PT) or an internal quality control (IQC)-based approach. Herein, we take similar approaches.…”