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Big data and reference intervals.

Dan Yang1, Zihan Su1, Min Zhao1

  • 1National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Units of Medical Laboratory, Chinese Academy of Medical Sciences, PR China.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|January 9, 2022
PubMed
Summary
This summary is machine-generated.

Establishing laboratory-specific reference intervals (RIs) is crucial for accurate clinical diagnosis. This review explores using big data analytics as a standardized, validated approach to derive RIs, overcoming limitations of traditional methods.

Keywords:
Big dataData pre-processingIndirect methodReference intervalVerification

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Biostatistics

Background:

  • Reference intervals (RIs) are vital for clinical diagnosis but vary significantly across populations.
  • Current Clinical Laboratory Standards Institute (CLSI) EP28-A3c guidelines recommend population-specific RIs.
  • Traditional RI establishment is resource-intensive, requiring large healthy cohorts and extensive testing.

Purpose of the Study:

  • To review the historical development and current state of using big data for establishing laboratory-specific RIs.
  • To assess data processing, statistical methodologies, and validation techniques for big data-driven RI determination.
  • To address the lack of standardization and consensus in the indirect big data approach.

Main Methods:

  • Historical review of RI establishment methodologies.
  • Comprehensive assessment of big data processing and statistical analysis techniques.
  • Evaluation of post-verification analysis for validating big data-derived RIs.

Main Results:

  • The big data era offers a novel opportunity to "mine" existing laboratory information for RI development.
  • The indirect big data approach currently lacks standardization, consensus, and CLSI guidance.
  • This review provides a framework for validating the big data approach in establishing laboratory-specific RIs.

Conclusions:

  • Big data analytics presents a promising, albeit currently unstandardized, alternative to traditional methods for establishing population-specific reference intervals.
  • Further research and standardization are needed to fully leverage big data for reliable and clinically applicable RIs.
  • Validation of big data methodologies is essential for their adoption in clinical laboratories.