Related Experiment Video
Updated: Jun 4, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
The implementation of a system for managing analytical quality in networked laboratories
Nuthar Jassam1, Chris Lindsay, Kevin Harrison
1Department of Clinical Biochemistry, Leeds General Infirmary, Leeds LS13EX, UK. nuthar.jassam@hdtf.nhs.uk
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.
Related Concept Videos
Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality Assurance
Automated Microbial Diagnostics
Data Validation
Key parameters for method validation include:
Development of Analytical Methods

