Year: 2025
Type: article
Abstract: ABSTRACT Background In clinical practice, reference intervals play a pivotal role in interpreting laboratory test results. Yet, when several tests are taken into consideration simultaneously, the traditional univariate intervals might not suffice due to the elevated risk of Type 1 errors. Methods This study introduces and evaluates two multivariate reference interval techniques: one based on Mahalanobis distance and the other an adaptation of the multivariate confidence interval. Using Monte Carlo simulations, we focused our assessments on the interplay between “Serum Ferritin and Transferrin Saturation” values. Results Upon evaluation, it became evident that the multivariate methods significantly reduced false positives. They presented enhanced accuracy over traditional univariate intervals. Notably, the method involving Mahalanobis distance stood out in terms of efficacy. Contributions Beyond presenting novel techniques, our research underscores the importance and potential of using multivariate approaches in clinical lab settings. The findings can guide better medical decision-making, ensuring optimized allocation of healthcare resources. Highlights We present two alternative multivariate reference interval methods that consider the relationship between two analytes simultaneously. Through comprehensive simulation studies, we compare the proposed methods with the conventional univariate reference interval method. Our results show the superior performance of our proposed methods in terms of patient rate, sensitivity, specificity, and overall accuracy. We discuss the practical relevance of these new methods.
DOI: 10.1002/jcla.70070
Authors: Esra Kutsal Mergen, Sevilay Karahan
Institution: Hacettepe University
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,120
OpenAlex ID: W4411420287