Personalized reference intervals - statistical approaches and considerations.

Area of Science:

• Clinical Chemistry
• Biostatistics
• Laboratory Medicine

Background:

• Physicians rely on population-based reference intervals (popRI) for interpreting laboratory test results.
• Personalized reference intervals (prRI) can enhance individual test result interpretation.
• prRI calculation requires biological variation estimates and prior steady-state test results.

Purpose of the Study:

• To outline statistical approaches for establishing and implementing personalized reference intervals (prRI).
• To discuss data quality assessment and statistical models for prRI calculation.
• To highlight the potential of prRI in improving clinical interpretation of laboratory tests.

Main Methods:

• Data quality assessment, including outlier and trend analysis, is crucial before using previous results.
• Two statistical models based on prediction intervals are proposed for prRI calculation.
• Model 1 uses individual 'within-person biological variation' (min. 5 results); Model 2 uses population 'within-subject biological variation' (fewer results applicable).

Main Results:

• The study outlines methodologies for calculating prRI using individual or population biological variation estimates.
• Two distinct statistical models are presented for generating prRI limits.
• The feasibility of prRI calculation depends on data availability and quality.

Conclusions:

• Personalized reference intervals (prRI) can significantly aid physicians in interpreting individual laboratory test results.
• Further studies are needed to confirm if prRI use improves clinical outcomes.
• Recommends including both population-based (popRI) and personalized (prRI) intervals in lab reports for comprehensive patient follow-up.