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Related Experiment Videos

The data warehouse as a foundation for population-based reference intervals.

Brian J Bock1, C Terrence Dolan, Gerald C Miller

  • 1Pathology Laboratory Associates, Applied Healthcare Infometrics, Tulsa, OK, USA.

American Journal of Clinical Pathology
|November 12, 2003
PubMed
Summary
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Large patient data warehouses can establish specific reference intervals for common analytes like liver enzymes and bilirubin. This provides more representative and diverse laboratory reference intervals, especially for aging populations.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Data Science in Healthcare

Background:

  • Traditional laboratory reference intervals often lack demographic diversity.
  • Large patient datasets offer potential for more robust reference interval determination.
  • Data warehouses can aggregate extensive clinical testing information.

Purpose of the Study:

  • To determine reference intervals for common analytes using a large patient data warehouse.
  • To assess the representativeness and demographic diversity of warehouse-derived reference intervals.
  • To explore the utility of data warehouses for generating advanced laboratory interpretative data.

Main Methods:

  • Extracted routine chemistry panel data from a data warehouse (438,180 patients).

Related Experiment Videos

  • Applied data filters to establish a large, demographically diverse reference population.
  • Calculated sex- and age-specific reference intervals for aspartate aminotransferase, alanine aminotransferase, total bilirubin, and alkaline phosphatase.

    • Main Results:

    • Generated reference intervals from populations exceeding 60,000 individuals.
    • Subgroups comprised 495 to 4,949 persons, ensuring statistical power.
    • Derived intervals demonstrated significant age and sex diversity, particularly for older demographics.

    Conclusions:

    • Data warehouses provide a valuable resource for creating detailed and representative laboratory reference intervals.
    • Warehouse-derived intervals enhance clinical utility, especially for diverse and aging patient populations.
    • Future data integration holds potential for advanced diagnostic and treatment support.