Multidisciplinary approach to redefining thyroid hormone reference intervals with big data analysis

Area of Science:

• Clinical Chemistry
• Endocrinology
• Big Data Analytics

Background:

• Reference intervals (RIs) for thyroid function tests are crucial for accurate diagnosis.
• Existing RIs may not reflect current populations or analytical methods, necessitating updates.
• Optimizing TSH reflex algorithms can improve clinical decision-making and resource allocation.

Purpose of the Study:

• To harmonize and refine reference intervals for thyroid function tests using big data.
• To specifically update the upper reference limit for TSH.
• To optimize the TSH reflex algorithm for improved clinical management and test utilization.

Main Methods:

• Extracted large-scale laboratory data (TSH, free T4, free T3) from Alberta, Canada.
• Applied statistical models (Bhattacharya, refineR, simple non-parametric) to derive new RIs.
• Excluded data from specialists, inpatients, repeat tests, and individuals with specific biomarkers.

Main Results:

• Established new TSH RIs: 1.23-25.0 mIU/L (0-7 days), 1.00-6.80 mIU/L (8 days-1 year), and 0.20-6.50 mIU/L (≥1 year).
• Defined new free T4 RIs: 13.5-50.0 pmol/L (0-14 days), 8.7-32.5 pmol/L (15-29 days), and 10.0-25.0 pmol/L (≥30 days).
• Developed an updated TSH reflex algorithm, triggering free T4 testing only when TSH is <0.1 mIU/L.

Conclusions:

• Big data analysis and multidisciplinary collaboration successfully enhanced thyroid function RIs.
• The upper TSH reference limit was widened to 6.50 mIU/L.
• Optimized RIs and the TSH reflex algorithm provide a guide for improved thyroid function test interpretation.