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A model for assessing interference.

M H Kroll, M Ruddel, D W Blank

    Clinical Chemistry
    |July 1, 1987
    PubMed
    Summary
    This summary is machine-generated.

    A new model quantifies analytical interference types: independent, dependent, or combined with analyte concentration. This method clarifies how hemoglobin affects creatinine and bilirubin measurements, improving diagnostic accuracy.

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    Area of Science:

    • Clinical Chemistry
    • Analytical Chemistry
    • Biochemistry

    Background:

    • Analytical interference is a significant challenge in clinical diagnostics.
    • Existing methods often quantify interference magnitude but not its type.
    • Understanding interference types is crucial for accurate laboratory test results.

    Purpose of the Study:

    • To develop and validate a novel model for characterizing analytical interference.
    • To differentiate interference into three distinct types: independent of analyte concentration, dependent on analyte concentration, and a combination of both.
    • To apply this model to assess hemoglobin interference in common clinical assays.

    Main Methods:

    • An experimental design using an orthogonally arranged matrix with varying analyte and interfering agent concentrations.

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  • Application of multiple regression analysis using analyte concentration, interfering agent concentration, and their product as independent variables.
  • Assessment of interference magnitude, direction, and statistical significance.

    • Main Results:

    • Hemoglobin interference with creatinine determination was found to be independent of creatinine concentration.
    • Hemoglobin interference with total bilirubin determination was dependent on total bilirubin concentration.
    • Hemoglobin interference with direct bilirubin determination exhibited a combined dependency on analyte and interfering agent concentrations.

    Conclusions:

    • The developed model effectively characterizes different types of analytical interference.
    • The model provides a more nuanced understanding of how substances like hemoglobin impact clinical assays.
    • This approach enhances the reliability of diagnostic testing by identifying specific interference mechanisms.