A New Concept for Reference Change Values—Regression to the Population Mean

Graham R. D. Jones (Lead / Corresponding author), Aasne K. Aarsand, Anna Carobene, Abdurrahman Coşkun, Pilar Fernández-Calle, Bill Bartlett, Jorge Díaz-Garzón, Sverre Sandberg

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval.

Methods: Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed.

Results: For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen.

Conclusions: We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.
Original languageEnglish
Number of pages9
JournalClinical Chemistry
Early online date22 May 2024
DOIs
Publication statusE-pub ahead of print - 22 May 2024

Keywords

  • reference change value
  • regression to the mean
  • within-subject biological variation

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