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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Lot-to-Lot Variation.

Simon Thompson1, Douglas Chesher1,2

  • 1Chemical Pathology, NSW Health Pathology, University of Sydney, Royal North Shore Hospital, St Leonards, NSW 2065, Australia.

The Clinical Biochemist. Reviews
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Summary

Lot-to-lot variation in laboratory reagents can cause inconsistent patient results and adverse clinical outcomes. Laboratories need robust procedures to detect and manage this variation, ideally through manufacturer improvements and clinically relevant acceptance criteria.

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Control

Background:

  • Lot-to-lot variation in calibrators and reagents poses a significant challenge to laboratory result consistency.
  • This variation can lead to adverse clinical outcomes, underscoring the need for effective detection and management strategies.

Purpose of the Study:

  • To highlight the clinical consequences of lot-to-lot variation in laboratory testing.
  • To discuss current methods for assessing reagent lot variation and their limitations.
  • To propose improvements for detecting and mitigating lot-to-lot variation.

Main Methods:

  • Review of existing evaluation protocols, including the Clinical and Laboratory Standards Institute (CLSI) guidelines.
  • Emphasis on the preference for native patient samples over non-commutable quality control materials.
  • Discussion of supplementary quality procedures like moving patient averages.

Main Results:

  • Current protocols, including CLSI, may lack the statistical rigor or power to detect clinically significant lot-to-lot variation.
  • Existing methods may fail to detect cumulative shifts between reagent lots.
  • Collaboration and data sharing are crucial for identifying variation.

Conclusions:

  • Laboratories must implement procedures to quantify inaccuracy and determine acceptable variation for patient result release.
  • Addressing cumulative shifts and improving detection requires enhanced quality procedures and manufacturer collaboration.
  • Reducing variation at the manufacturing point and using medically relevant acceptance criteria are key to improving consistency.