The case for common reference intervals

Graham R D Jones1, Antony Barker, Jill Tate

  • 1Department of Chemical Pathology, St Vincent's Hospital, Faculty of Medicine, University of NSW, Sydney, NSW, Australia. gjones@stvincents.com.au

Insights

Laboratories should adopt common pathology reference intervals across Australia and New Zealand. This standardization improves healthcare integration and reduces costs, benefiting the medical community.

Area of Science:

  • Clinical Pathology
  • Laboratory Medicine
  • Health Informatics

Background:

  • Current pathology reference intervals are laboratory-specific, leading to inconsistencies.
  • This fragmented approach hinders the integration of electronic health information.
  • Existing methods are often poorly executed and costly.

Purpose of the Study:

  • To advocate for the adoption of common reference intervals in pathology.
  • To promote standardized reporting formats and assay standardization.
  • To address the challenges of modern healthcare with unified pathology data.

Main Methods:

  • This study presents a conceptual argument and proposes a shift in practice.
  • It emphasizes the need for collaboration across laboratories in Australia and New Zealand.
  • The focus is on the feasibility and benefits of standardization.

Main Results:

  • Implementing common reference intervals offers a more effective approach than current practices.
  • Standardization can lead to improved data integration and potentially reduced costs.
  • A unified system better serves the evolving needs of the medical community.

Conclusions:

  • A move towards common pathology reference intervals is essential for modern healthcare.
  • Standardization is technically achievable and crucial for the pathology profession.
  • Failure to standardize represents a missed opportunity to meet contemporary health challenges.

Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Central Tendency: Analysis01:10

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interval Level of Measurement00:55

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...