Feasibility evaluation of big data algorithms for establishing serum protein electrophoresis reference intervals using Hoffmann and refineR methods.

Background: Serum protein electrophoresis (SPE) is essential for diagnosing monoclonal gammopathies and a variety of other diseases. Despite its importance, there is a scarcity of SPE parameter reference intervals (RIs) derived from large datasets. This study seeks to fill this gap by establishing sex-specific RIs using Hoffmann and refineR algorithms and assessing the feasibility of these methods.

Method: We utilized two health check-up population databases to create a reference and a validation set. The reference set included 52,293 individuals with outlier removal via the Tukey method. Variance component analysis was used to evaluate the impact of sex and age on SPE parameters. The Hoffmann and refineR algorithms were applied to establish RIs, with the bias ratio method comparing RI differences. Validation data from differing timeframes helped assess the RIs' reliability and utility. Moreover, we juxtaposed our RIs with previous studies to identify potential disparities.

Results: Sex-specific RIs were established using the Hoffmann and refineR algorithms, with high consistency between the two algorithms, except for a slight difference in upper limits (ULs) of α1-globulin and β1-globulin. Additionally, there are sex differences in RIs for α2-globulin, β2-globulin, γ-globulin, and Albumin. In the validation analysis, all established RIs passed verification, except for RIs determined by refineR for female α1-globulin and male β2-globulin.

Conclusion: This research highlights the critical impact of sex on SPE RIs and the need for tailored RIs in distinct clinical environments. The utility and feasibility of the Hoffmann and refineR algorithms for creating these customized intervals are effectively demonstrated.