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A dynamic reference change value model applied to ongoing assessment of the steady state of a biomarker using more than two serial results

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BACKGROUND: Reference change values are used to assess the significance of a difference in two consecutive results from an individual. Reference change value calculations provide the limits for significant differences between two results due to analytical and inherent biological variations. Often more than two serial results are available. Using the reference change value concept on more than two measurements results in an increased number of false-positive results. This problem has been solved for both uni- and bidirectional differences through use of wider limits when additional results are included.

METHODS: Based on normally (Gaussianly) distributed simulated data, a dynamic reference change value model was developed using more than two results and total coefficients of variation. The dynamic reference change value model includes validation of a set-point as the mean of the four first serial results and additional results are assessed for compliance to the steady state with the same set-point. Furthermore, the dynamic reference change value model compensates for increasing false-positive results with subsequent results. The dynamic reference change value model was designed to calculate significant limits for bidirectional differences.

RESULTS: Reference change factors were calculated for multiplication of the mean of previous results to create the limits for significant differences. The reference change factors are provided as a function of number of results and total coefficients of variation both in tables and in figures.

CONCLUSIONS: The dynamic reference change value model is appropriate for ongoing assessment of the steady state of a biomarker using more than two serial results.

OriginalsprogEngelsk
Artikelnummer4563219826168
TidsskriftAnnals of Clinical Biochemistry
Vol/bind56
Udgave nummer2
Sider (fra-til)283-294
Antal sider12
ISSN0004-5632
DOI
StatusUdgivet - 2019

ID: 58154597