Your results may vary: the imprecision of medical measurements
BMJ 2020; 368 doi: https://doi.org/10.1136/bmj.m149 (Published 20 February 2020) Cite this as: BMJ 2020;368:m149
- James P McCormack, professor1,
- Daniel T Holmes, clinical professor2 3
- 1Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
- 2St Paul’s Hospital, Department of Pathology and Laboratory Medicine, Vancouver, BC, Canada
- 3Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
- Correspondence to: J P McCormack jmccorma{at}interchange.ubc.ca
What you need to know
Inherent in every medical measurement is a degree of uncertainty: you must have a rough idea of the magnitude of that uncertainty to correctly interpret any reported measurement
The greater the uncertainty, the greater the difference that needs to be observed between two measurements before you can be confident that a true change has occurred
The “reference change value” (RCV) allows you to decide whether a change in two serial lab results is likely due to chance alone. The required change may be as small as 2% and as large as 50% depending on the test
Biological variation is typically the largest contributor to the RCV. For analytical variation, your local lab director can tell you the measurement error of any test you are interested in
Clinicians and patients need to interpret a multitude of medical measurements. These are often central to monitoring health and informed decision making. Has the serum cholesterol concentration come down since starting a statin? Have vitamin D levels gone up? Is the dose of thyroid medication correct? An understanding of the imprecision of medical measurements is essential to answer any of these questions. Even when laboratory and industry scientists have optimised their diagnostic testing processes to minimise inaccuracies, there always remains an error in any clinical measurement due to unavoidable, naturally occurring variability.
This practice pointer explains the nature of measurement errors and offers a practical guide to both estimating the confidence interval of a single result and deciding if changes between serial laboratory tests reflect true changes or simply fluctuations based on analytical or biological variation.
How this article was made
This article was based on a review of the available biological variation data for select routine clinical chemistry measurements as collated by the European Federation of Clinical Chemistry and Laboratory Medicine (https://biologicalvariation.eu/) and in select cases identified by …
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