Indirect Estimation of 25-Hydroxyvitamin D Reference Intervals Using Data Mining

Background: Reference intervals for 25-hydroxyvitamin D are significantly influenced by seasonal variations and population-specific factors. This study aimed to establish local, population-specific Vitamin D reference intervals using the refineR algorithm and to compare these results with the conventional non-parametric method using a hospital database. Methods: A total of 127,220 laboratory results from 2022 to 2025 were retrospectively analyzed. Data were filtered based on adult age, morning fasting specimens, outpatient status, and normal parathyroid hormone levels (14–72 ng/L). Following filtration, Wilcoxon and Kruskal–Wallis tests were employed to evaluate differences between potential subgroups. Consequently, 24,036 eligible results were stratified by age (18–40, 41–65, and >65 years) and sex (female and male). Reference intervals were calculated using the refineR algorithm and the non-parametric percentile method. Results: The distribution of vitamin D levels was found to be right-skewed [Median: 12.92 ng/mL (4.21–149)]. The upper reference limits obtained via refineR were consistently lower than those derived from the non-parametric method. Both methods showed a linear increase in median values and upper reference limits with advancing age. Females in the 18–40 age group exhibited the lowest vitamin D profile. The summer concentrations were significantly higher than those of other seasons (p <0.05). Conclusions: The refineR algorithm managed outliers and pathological results more effectively than the non-parametric method. Our findings highlight the clinical necessity for age- and sex-specific subgroups, rather than relying on the manufacturer’s single reference range. Implementing these population-specific intervals can enhance diagnostic accuracy and prevent misclassification, facilitating earlier identification of vitamin D-related metabolic and skeletal disorders.