Reference Interval Harmonization: Harnessing the Power of Big Data Analytics to Derive Common Reference Intervals...

Area of Science:

• Laboratory Medicine
• Clinical Chemistry
• Biostatistics

Background:

• Significant variation exists in laboratory reference intervals (RIs), impacting clinical decision-making.
• The Canadian Society of Clinical Chemists (CSCC) Working Group on Reference Interval Harmonization (hRI-WG) aims to establish harmonized RIs (hRIs).

Purpose of the Study:

• To derive common adult RIs for 16 biochemical markers using a big data approach.
• To support the implementation of harmonized RIs (hRIs) for laboratory tests.

Main Methods:

• Collected big data from community laboratories across Canada for 16 biochemical markers.
• Utilized the refineR method to derive harmonized RIs (hRIs), accounting for age, sex, and analytical differences.
• Verified proposed hRIs across 9 laboratories using diverse instrumentation and sample types.

Main Results:

• Harmonized RIs (hRIs) were successfully calculated for most assays using the refineR method.
• Derived hRIs met verification criteria for several analytes, including alkaline phosphatase, albumin (bromocresol green), and potassium (serum).
• Challenges were noted for specific analytes, requiring further investigation due to verification failures or excessively wide hRIs.

Conclusions:

• A novel data-driven approach demonstrates the feasibility of harmonizing laboratory reference intervals (RIs).
• The study highlights the potential of big data analytics for establishing harmonized RIs (hRIs).
• Limitations in harmonization and big data analytics were identified, necessitating careful consideration for future applications.