VeRUS: verification of reference intervals based on the uncertainty of sampling

Laboratories are required to routinely verify reported reference intervals (RIs), but common verification methods like the CLSI-EP28-A3c binomial test are often impractical due to sample collection requirements. Indirect verification methods like equivalence limits (ELs) use routine data from patient care but lack systematic evaluation. This study aimed to develop and evaluate a novel indirect verification method: verification of reference intervals based on the uncertainty of sampling (VeRUS).

VeRUS compares the to-be-verified candidate RI to an RI estimated from local routine data. Acceptable differences are based on the sampling uncertainty intrinsic to the nonparametric method for establishing RIs with n=120 samples. The three verification methods were systematically compared with simulated test sets resembling 10 differently distributed biomarkers and a wide range of plausible candidate RIs.

The binomial test is inherently unable to reject too wide RIs; e.g. the 99.8 %-interval, for which ELs and VeRUS showed high rejection rates (mean 89.2 %, SD 31.5 % and mean 95.8 %, SD 2.3 %, respectively). Moreover, the binomial test incorrectly accepts 29.3 % of "too narrow" 80%-intervals, whereas the false acceptance rates of ELs and VeRUS were lower (mean 21.7 %, SD 40.9 % and mean 7.2 %, SD 4.7 %, respectively). Overall, both indirect verification methods demonstrated increased statistical power, while ELs were least consistent among different biomarker distributions.

Its robust performance without the need for sample collection makes VeRUS an attractive tool for RI verification. By enabling routine verification of previously practically unverifiable RIs (e.g., in pediatrics), VeRUS may enhance clinical decision-making and improve patient care.