Evaluation of A Novel Indirect Reference Interval Model for Canine Calcium and Alanine Aminotransferase.

Abstract

Background: Population-based reference intervals (RIs) are fundamental for appropriate result interpretation. Current recommendations for their determination specify that a direct, de novo approach is preferable, including at least 120 carefully selected subjects. This is not always feasible due to logistical and financial constraints. Recently, several sophisticated algorithms for the indirect estimation of RIs have been proposed in human medicine and described as comparable with direct methods in many cases.

Objectives: Preliminary evaluation of the utility and feasibility of novel indirect methods for determining RIs in the veterinary clinical laboratory.

Methods: Calcium and alanine aminotransferase (ALT) were selected as representative measurands expected to show Gaussian and non-Gaussian distributions, respectively. After limited data filtering, canine results extracted from a laboratory database were used to estimate indirect RIs and 90% confidence intervals (CIs) using the RefineR algorithm. Using 90% CIs as well as permissible differences (pD), the indirect RIs were compared to direct RIs derived by the same laboratory, over the same period, from a population of healthy canine blood donors.

Results: The direct and indirect approaches yielded comparable reference limits for both measurands. Apart from the upper reference limit for Ca, 90% CIs for the reference limits calculated from the conventional direct and indirect methods overlapped. Direct reference limits were within pD calculated from the indirect reference limits.

Conclusion: The indirect method, using the RefineR algorithm, may represent a cost-effective option for RI determination. It may also complement direct approaches and has the potential to be more representative of animal populations.