The implementation of a system for managing analytical quality in networked laboratories

Abstract

Background: In a network of laboratories analytical variability between instruments, even of the same type, may exist for reasons beyond the control of laboratory staff. Controlling variability is a prerequisite for the application of shared reference ranges and for ensuring the transferability of patient test results. Controlling variability requires a robust, non-conventional quality system to detect poor performance of analysers that are geographically distant. Essential to this quality system is a set of well-defined quality specifications. Methods: The approach used in our study started with (1) selection of a model for quality specifications based on biological variation; the 'three-level model' (TLM) was selected on the basis of its flexibility to accommodate various levels of analytical performance; (2) determination of the performance characteristics of the 71 analytes measured in core biochemistry in terms of imprecision and bias; (3) defining quality requirements in the form of imprecision, bias and total error for 71 analytes measured routinely in core biochemistry; and (4) developing software to assist a consistent wide application of the quality specifications and to monitor analytical indices to the common quality specifications. Results: In this paper we describe how we have implemented this model across our network. Forty-six of the 71 analytes in our core laboratory repertoire were allocated to the TLM. We were able to demonstrate equivalence of results on all analysers, for 42 out of 46 analytes allocated to this model. Conclusions: We propose that other networked laboratories should investigate the suitability of this quality system for use in their network.

Laboratory services: regaining and maintaining control

“…patient and EQA data) to corroborate or refute comparisons involving IQC data. If CV W data are unavailable or inapplicable, precluding the one third rule, then other appropriate comparability criteria to demonstrate results equivalence must be defined, giving primary consideration to clinical decision thresholds and therapeutic limits ( Jassam et al, 2011).…”

Internal quality control: best practice

“…In reality, however, implementation of 3–4 different rule systems is sufficient and practicable 26 27. A four-group system was implemented at three locations within the Netherlands, which categorised assays by the six sigma approach26 as described above.…”

“…A four-group system was implemented at three locations within the Netherlands, which categorised assays by the six sigma approach26 as described above. Jassam et al 27 used a three-level model, which characterised the assays as optimal, desirable or minimal performance based on the classification by Fraser et al 28 given below: Optimal performance: TEa<0.125(CV I 2 +CV G 2 ) 1/2 +1.65(0.25 CV I ) Desirable performance: TEa<0.250(CV I 2 +CV G 2 ) 1/2 +1.65(0.50 CV I ) Minimal performance: TEa<0.375(CV I 2 +CV G 2 ) 1/2 +1.65(0.75 CV I ) where CV G is the within-group biological variation (variation between individuals) and CV I is the within-subject biological variation (variation within an individual results).…”

“…However, variation in assay performance will still exist even when the same analysers and methods are used across different sites. Collation of long-term IQC data from all analysers, including imprecision and bias data, should be used to define the quality of the assays, and then, IQC rules implemented as appropriate to the required quality of each assay, as described previously 26 27…”

“…Applications such as QC-today (Instrumentation Laboratory) are available that allow input of IQC results from multiple analysers, and definition of assay-specific IQC rules. Jassam et al 27. developed their own software to collate data from all sites within their network, define analytical impression and compare this to allowable error.…”