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Interpreting two TSH results from the same patient.

Arne Åsberg1, Gustav Mikkelsen1,2

  • 1Department of Clinical Chemistry, St. Olav's Hospital, Trondheim, Norway.

Clinical Chemistry and Laboratory Medicine
|June 27, 2023
PubMed
Summary
This summary is machine-generated.

Interpreting analyte concentrations from two separate blood samples requires bivariate reference intervals, not univariate limits and reference change values (RCVs). This method improves diagnostic accuracy for conditions like thyroid function tests.

Keywords:
biological variationbivariate distributionreference change valueswithin-subject variation

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Area of Science:

  • Clinical Chemistry
  • Biostatistics
  • Endocrinology

Background:

  • Assessing patient analyte levels from two separate measurements is common in clinical practice.
  • Traditional methods use univariate reference limits and reference change values (RCVs).
  • The accuracy of these traditional methods when the patient's mean analyte concentration is unknown is questionable.

Purpose of the Study:

  • To compare the efficacy of bivariate reference intervals versus univariate reference limits and RCVs for interpreting analyte concentrations from two separate samples.
  • To evaluate the diagnostic accuracy of each method using simulated thyroid-stimulating hormone (s-TSH) data.

Main Methods:

  • Simulated two s-TSH measurements for 100,000 euthyroid subjects.
  • Plotted bivariate distributions and compared them with univariate reference limits and RCVs.
  • Estimated diagnostic sensitivity and specificity of the univariate approach against the bivariate distribution.

Main Results:

  • The combination of univariate reference limits and RCVs did not accurately represent the central 95% of the bivariate distribution.
  • The sensitivity and specificity of the univariate method were 80.2% and 92.2%, respectively.
  • Graphical and numerical analyses showed discrepancies between the two interpretation models.

Conclusions:

  • Univariate reference limits and RCVs are insufficient for accurately interpreting analyte concentrations from two separate samples.
  • Bivariate reference intervals derived from healthy individuals offer a more reliable approach.
  • This finding has implications for clinical decision-making in endocrinology and other fields relying on serial analyte measurements.