# Calculation of reference intervals using an indirect approach from laboratory database

## TL;DR

This study shows that using routine clinical data can effectively calculate reference intervals for lab tests without needing healthy volunteers.

## Contribution

The study introduces a feasible indirect method for calculating reference intervals using routine clinical samples and strict statistical criteria.

## Key findings

- Newly calculated reference intervals did not significantly differ from existing ones.
- The indirect approach maintained data quality and sufficient sample size.
- The method supports diagnostic accuracy and efficient healthcare resource use.

## Abstract

The establishment of reference intervals (RIs) for biochemical parameters is crucial for the accurate interpretation of laboratory test results and clinical decision-making. This study aims to evaluate the feasibility of determining RIs using an indirect approach, which analyzes routine clinical samples instead of recruiting healthy individuals, thus leveraging large datasets while applying strict statistical criteria to exclude pathological values.

This is a prospective study in which patients fulfilling inclusion criteria were recruited from the routine primary care to achieve a high proportion of healthy individuals. Biochemical magnitudes as creatinine, esterified bilirubin, ferritin, and transferrin were analyzed. A patient exclusion protocol was implemented to minimize results suggestive of pathology but ensuring a sufficient sample size for the calculation of new RIs. The new ones were compared with the RIs used in the laboratory, provided by the manufacturer.

The comparison between the newly calculated RIs and the existing laboratory RIs did not reveal statistically significant differences. The indirect approach provided robust RIs while maintaining data quality and a sufficient sample size.

The findings support the effectiveness of this indirect approach for RI determination, provided that rigorous data quality standards and adequate sample sizes are maintained. The adoption of these RIs in clinical practice could enhance diagnostic accuracy and optimize healthcare resource utilization.