This series explores choice of internal quality control (IQC) material in medical laboratories. Part 2 discusses the matrix effect, variability, and third-party materials’ impact on Measurement Uncertainty (MU). It emphasises the need to detect and mitigate matrix effects, manage vial-to-vial and lot-to-lot variability, and consider the benefits and challenges of using third-party materials.

Introduction

In the first part of our series on choosing the correct material for internal quality control (IQC), we discussed the fundamental characteristics of control materials and the impact on Measurement Uncertainty (MU). In this second part, we will delve into more advanced considerations, including the matrix effect, vial-to-vial and lot-to-lot variability, and the role of third-party materials in MU estimation. For more details on traceability and stability, which are vital to understanding the reliability of control materials, don’t miss Part 3 of our series.

What is the matrix effect, and how does it affect laboratory testing?

The matrix effect occurs when substances within a sample interfere with the assay’s ability to measure the target analyte accurately. It can lead to inaccurate results and increased measurement uncertainty, making it crucial to account for these effects when selecting control materials.

How can laboratories detect and mitigate the matrix effect?

Laboratories can detect matrix effects by performing recovery studies and linearity checks, comparing control material performance with patient samples. Mitigation strategies include using matrix-matched calibrators, adjusting assay conditions, or selecting control materials closely resembling patient samples.

What is vial-to-vial and lot-to-lot variability, and why is it important?

Vial-to-vial and lot-to-lot variability refer to differences in performance between different vials or batches of control materials. Minimising this variability is important to ensure consistent test results and reliable quality control.

Why should laboratories consider using third-party controls?

Third-party controls provide an unbiased assessment of assay performance, independent of the test kit manufacturer. They help detect performance shifts that might not be apparent with manufacturer-provided controls, enhancing the reliability of quality control.

Understanding the Matrix Effect

Control materials should mimic the properties of patient samples as closely as possible. However, to enhance stability and reduce lot-to-lot variability, manufacturers may introduce stabilisers and preservatives that are not typically found in patient samples. This can lead to a matrix effect, where the behaviour of the control material differs from that of patient samples due to changes in the material’s fundamental properties.

Matrix effects can be either chemical or physical. Chemical matrix effects occur when substances within the control material chemically interact with the assay reagents, potentially enhancing or suppressing the signal. Physical matrix effects, on the other hand, arise from differences in the physical properties of the control material, such as viscosity or particle size, which can alter the assay response.

Impact of Matrix Effect on Measurement Uncertainty (MU)

The matrix effect is a critical consideration for MU because it can cause systematic errors in the measurement process. If control materials do not accurately reflect the matrix of patient samples, the uncertainty associated with those measurements may be underestimated or overestimated. Properly accounting for the matrix effect is vital for accurate MU estimation, which is essential for the top-down MU assessment approach outlined in ISO/TS 20914:2019.

Detecting and Mitigating Matrix Effects

To detect matrix effects, laboratories should regularly compare control material performance with that of patient samples. Techniques such as recovery studies and linearity checks can help identify matrix effects. Once detected, steps to mitigate matrix effects include:

  • Selecting More Comparable Control Materials: Choosing control materials that are more similar in composition to patient samples can reduce matrix effects.
  • Adjusting Assay Conditions: Modifying assay conditions, such as dilution protocols or reagent concentrations, can help minimise the impact of matrix effects.
  • Using Matrix-Matched Calibrators: Incorporating calibrators that closely resemble the patient sample matrix can help adjust for any matrix effects in the control materials.

For practical strategies on mitigating matrix effects and ensuring stability, refer to Part 3 of this series, where we discuss these topics in detail.

Detailed Analysis of Vial-to-Vial and Lot-to-Lot Variability

To effectively control measurement imprecision, it is crucial to minimise any external variability in the IQC process. However, variability is inevitable due to the mass manufacturing process. Standardising procedures within the laboratory, particularly with reconstitution methods, can help minimise the influence of vial-to-vial and lot-to-lot variability. According to CLSI C24-A4, the variation due to these differences should be significantly smaller than the variation measured by the control procedure to ensure reliable quality control.

Impact of Variability on MU

Vial-to-vial and lot-to-lot variability can significantly affect MU if not properly managed. Variations between different lots of control materials can introduce additional uncertainty into the measurement process, complicating the estimation of MU. By minimising these sources of variability, laboratories can ensure that MU estimates are more consistent and reflective of actual measurement performance, aligning with ISO/TS 20914:2019 standards.

Managing Variability in the Laboratory

To manage variability effectively, laboratories can implement the following strategies:

  • Standardised Reconstitution Procedures: Ensure that all control materials are prepared and handled in a consistent manner to reduce variability.
  • Regular Quality Checks: Conduct regular quality checks on each new lot of control materials to identify any potential variability issues early. This includes pre acceptance testing
  • Use of Statistical Quality Control Tools: Employ statistical tools to monitor variability and detect trends or shifts that may indicate a problem with the control materials.

This variability is just one aspect of managing MU in clinical laboratories. For a comprehensive look at strategies to handle these challenges, see Part 4 of this series, where we discuss advanced methods for reducing variability and using third-party materials.

Expanding on Third-Party Materials

Third-party quality control materials are designed to provide an independent assessment of an assay’s performance, free from bias. These materials are not optimised for specific measurement systems and offer a platform-independent evaluation.

Using third-party controls has its benefits, as highlighted by various guidelines. The CLSI guideline C24-A3, for instance, states that quality control materials should differ from calibrator materials to ensure a comprehensive assessment of the measurement procedure’s performance. ISO 15189:2012/2022 and other organisations, such as the National Association of Testing Authorities (NATA) in Australia, also recommend considering third-party QC either instead of, or in addition to, control materials supplied by the reagent or instrument manufacturer.

Benefits and Challenges of Third-Party Materials

Benefits:

  • Unbiased Performance Assessment: Third-party materials provide an independent check on assay performance, helping to identify any bias introduced by manufacturer-specific controls.
  • Improved MU Estimation: By offering a more objective measure of assay performance, third-party controls can help improve the accuracy of MU estimation.
  • Enhanced Comparability: Third-party controls facilitate comparisons between different laboratories, as they are not tied to specific assay systems or reagents.

Challenges:

  • Cost: Third-party materials can be more expensive than manufacturer-provided controls, which may be a barrier for some laboratories.
  • Availability: Not all assays have suitable third-party controls available, limiting their use in certain situations.
  • Implementation: Switching to third-party controls requires careful validation and may involve changes to existing quality control protocols.

For more on how third-party materials can enhance your quality control processes and improve MU management, see Part 4 of this series.

Conclusion

Advanced considerations, such as the matrix effect, variability, and third-party materials, are crucial for enhancing the accuracy and reliability of IQC in clinical laboratories. Understanding these factors can help laboratories better manage MU and ensure high-quality patient results. For further insights, read Part 3 on traceability and stability.

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