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Hierarchy of reference interval models: advancing laboratory data interpretation.

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This summary is machine-generated.

Selecting appropriate reference intervals (RIs) for laboratory data is crucial for clinical decisions. This paper proposes a hierarchical framework, ranking RI models by reliability, with personalized RIs at the top.

Keywords:
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Area of Science:

  • Clinical Laboratory Science
  • Medical Informatics
  • Biostatistics

Background:

  • Accurate interpretation of laboratory data is vital for clinical decision-making.
  • Diverse sources and statistical methods for reference intervals (RIs) lead to multiple RIs for a single measurand.
  • Selecting the most appropriate RI is challenging due to a lack of standardization.

Purpose of the Study:

  • To develop a systematic approach for structuring known RI models.
  • To discuss the advantages and disadvantages of various RI models.
  • To provide a framework for selecting the most appropriate RI for clinical practice.

Main Methods:

  • Constructed a hierarchical pyramid to visually represent RI model reliability.
  • Analyzed data sources and statistical approaches used in RI estimation.
  • Evaluated different RI models based on their reliability and applicability.

Main Results:

  • A hierarchical pyramid model was developed, positioning less reliable RIs at the base and more reliable RIs at the top.
  • Discrete population-based RIs derived from hospital/laboratory data are theoretically the least reliable.
  • Multivariate continuous personalized RIs are theoretically the most reliable.

Conclusions:

  • A systematic framework is needed to navigate the complexity of RI selection.
  • The reliability of RIs varies significantly based on their derivation method and data source.
  • Personalized RIs offer a theoretically superior approach for routine clinical practice.