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Sigma metrics application for validated and non-validated detecting systems performance assessment.

Yong Xia1, Mingyang Li1, Bowen Li1

  • 1Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China.

Journal of Clinical Laboratory Analysis
|December 14, 2020
PubMed
Summary

Sigma metrics reveal that non-validated laboratory systems may introduce performance uncertainty compared to validated systems. Thorough evaluation is crucial before adopting non-validated systems for routine clinical use.

Keywords:
non-validated systemsperformancesigma metricsvalidated systems

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Area of Science:

  • Clinical Laboratory Science
  • Analytical Chemistry
  • Quality Management in Healthcare

Background:

  • Sigma metrics offer an objective, quantitative method for evaluating analytical quality in clinical laboratories.
  • This study assesses the performance of validated and non-validated systems using sigma metrics.
  • Key parameters influencing system performance were explored.

Purpose of the Study:

  • To compare the analytical performance of validated and non-validated laboratory systems using sigma metrics.
  • To identify factors contributing to performance variations between system types.
  • To inform the adoption of laboratory systems based on objective quality evaluation.

Main Methods:

  • Six biochemistry assays were evaluated on Beckman and Mindray validated and non-validated systems.
  • Reagents and analyzers were crossed to assess system performance.
  • Imprecision and bias were determined using national quality control programs.
  • Total error allowance was defined by the Chinese Clinical Laboratory Centre Industry Standard (WS/T403-2012).

Main Results:

  • Most systems met quality specifications, except for TP assay imprecision on a non-validated Mindray system.
  • Four assays (LDH, TP, TG, GLU) on non-validated systems failed to meet bias criteria.
  • Higher biases were observed across various assays and system types.
  • TP assay showed sigma metrics below 3 for most systems, indicating poor performance, except for the Mindray non-validated system.

Conclusions:

  • Non-validated systems may present performance uncertainties compared to validated systems.
  • Validated systems generally provided lower bias.
  • Rigorous evaluation of non-validated systems is essential before routine clinical laboratory implementation.