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The multivariate reference range: an alternative interpretation of multi-test profiles

J C Boyd, D A Lacher

    Clinical Chemistry
    |February 1, 1982
    PubMed
    Summary
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    Multivariate interpretation of laboratory data using Mahalanobis distance (D2) offers a sensitive method for detecting subtle abnormalities across multiple analytes. This approach significantly reduces false positives compared to traditional univariate testing.

    Area of Science:

    • Clinical Chemistry
    • Medical Statistics
    • Biostatistics

    Background:

    • Traditional laboratory data interpretation relies on univariate reference ranges.
    • Univariate analysis can lead to a high rate of false-positive results, necessitating further investigation.
    • Multivariate approaches offer a more comprehensive interpretation of clinical chemistry data.

    Purpose of the Study:

    • To develop and evaluate a multivariate reference range using Mahalanobis distance (D2) for clinical chemistry analytes.
    • To assess the utility of multivariate interpretation in identifying abnormalities in laboratory test results.
    • To compare the sensitivity and specificity of multivariate versus univariate analysis.

    Main Methods:

    • Collected clinical chemistry data for 261 medical students across 20 common analytes.

    Related Experiment Videos

  • Assessed normality of data distribution using the Kolmogorov-Smirnov test.
  • Applied a two-stage log-exponential transform for non-normal data, then calculated Mahalanobis distance (D2).
  • Derived chi-square percentiles for D2 scores and established a 95% cutoff for abnormality.

    • Main Results:

    • Over two-thirds of students exhibited univariate test abnormalities.
    • Fewer than 7% of students had abnormal multivariate D2 scores.
    • The multivariate reference range effectively identified minor variations across multiple analytes.
    • The multivariate approach showed reduced sensitivity for single, highly abnormal analytes.

    Conclusions:

    • Multivariate reference ranges, based on Mahalanobis distance, show promise in reducing unnecessary follow-up of false-positive laboratory results.
    • This method offers enhanced sensitivity for detecting subtle, multi-analyte variations.
    • Further clinical validation is required to establish the widespread utility of this multivariate approach.